59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
import os
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import dotenv
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import logging
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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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from langchain_core.chat_history import BaseChatMessageHistory
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logging.basicConfig(
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level=logging.DEBUG,
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format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
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)
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dotenv.load_dotenv()
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# 1. 设置环境变量
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os.environ['OPENAI_API_KEY'] = os.getenv("SILICONFLOW_API_KEY")
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os.environ['OPENAI_BASE_URL'] = os.getenv("SILICONFLOW_BASE_URL")
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# 2. 初始化模型
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llm = ChatOpenAI(model="deepseek-ai/DeepSeek-V3.1")
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# 3. 定义 Prompt (现代版无需手动处理 question 变量)
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prompt = ChatPromptTemplate.from_messages([
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("system", "你是一个万能的人工智能AI"),
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MessagesPlaceholder(variable_name="history"),
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("human", "{question}")
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])
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# 4. 【核心改動】使用 LCEL 組合鏈
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# 這裡不需要 LLMChain，直接用管道符
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chain = prompt | llm
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# 5. 管理記憶體 (現代版做法：使用字典存儲不同 Session 的歷史)
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store = {}
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def get_session_history(session_id: str) -> BaseChatMessageHistory:
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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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# 包裝成帶有記憶功能的鏈
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with_message_history = RunnableWithMessageHistory(
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chain,
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get_session_history,
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input_messages_key="question",
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history_messages_key="history",
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)
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# 6. 執行調用
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config = {"configurable": {"session_id": "xiaoming_test"}}
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res1 = with_message_history.invoke({"question": "我是小明"}, config=config)
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print(f"回答1: {res1.content}")
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res2 = with_message_history.invoke({"question": "我是谁？"}, config=config)
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print(f"回答2: {res2.content}") |