61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
import os
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from dotenv import load_dotenv
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from langchain_openai import ChatOpenAI
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain_core.runnables.history import RunnableWithMessageHistory
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from langchain_community.chat_message_histories import ChatMessageHistory
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# 加载 .env 中的 API 密钥等配置
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load_dotenv()
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# 初始化大模型
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llm = ChatOpenAI(
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api_key=os.getenv("OPENAI_API_KEY"),
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base_url=os.getenv("DASHSCOPE_BASE_URL"),
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model="qwen-plus",
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temperature=0.7
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)
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# 定义带历史记录的提示模板
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prompt = ChatPromptTemplate.from_messages([
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("system", "你是一个乐于助人的助手。"),
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MessagesPlaceholder("history"), # 历史消息占位符
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("human", "{input}") # 当前用户输入
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])
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# 创建基础链
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chain = prompt | llm
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# 内存存储：用字典模拟会话历史（仅用于演示）
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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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# 包装成带记忆的链
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chatbot = RunnableWithMessageHistory(
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chain,
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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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def chat_with_agent(input_message, session_id):
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print(f"用户: {input_message}")
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print("助手: ", end="", flush=True)
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for chunk in chatbot.stream(
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{"input": input_message},
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config={"configurable": {"session_id": session_id}} # 多轮对话（使用同一个 session_id）
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):
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print(chunk.content, end="", flush=True)
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print("\n\n---\n")
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chat_with_agent(input_message='一句话解释下人工智能。', session_id="user_001")
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chat_with_agent(input_message='我们都聊了什么？', session_id="user_001")
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chat_with_agent(input_message='我们都聊了什么？', session_id="user_002")
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chat_with_agent(input_message='我们都聊了什么？', session_id="user_001") |