提示範本

使用 LangChain 開發 LLM 應用

Jonathan Bennion

AI Engineer & LangChain Contributor

提示範本

定義 LLM 提示的配方
可包含：說明、範例與額外脈絡

含輸入變數佔位符的提示範本。

提示範本

from langchain_core.prompts import PromptTemplate


template = "Expain this concept simply and concisely: {concept}"

prompt_template = PromptTemplate.from_template(
    template=template
)


prompt = prompt_template.invoke({"concept": "Prompting LLMs"})
print(prompt)

text='Expain this concept simply and concisely: Prompting LLMs'

llm = HuggingFacePipeline.from_model_id(
    model_id="meta-llama/Llama-3.3-70B-Instruct",
    task="text-generation"
)

llm_chain = prompt_template | llm


concept = "Prompting LLMs"
print(llm_chain.invoke({"concept": concept}))

Prompting LLMs (Large Language Models) refers to the process of giving a model a
specific input or question to generate a response.

LangChain Expression Language（LCEL）：|（pipe）運算子
Chain：串接不同元件的呼叫

聊天模型

聊天角色：system、human、ai

from langchain_core.prompts import ChatPromptTemplate


template = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a calculator that responds with math."),
        ("human", "Answer this math question: What is two plus two?"),
        ("ai", "2+2=4"),
        ("human", "Answer this math question: {math}")
    ]
)

整合 ChatPromptTemplate

llm = ChatOpenAI(model="gpt-4o-mini", api_key='<OPENAI_API_TOKEN>')


llm_chain = template | llm

math='What is five times five?'


response = llm_chain.invoke({"math": math})
print(response.content)

5x5=25

一起來練習吧！

使用 LangChain 開發 LLM 應用