ChatGPT | 使用自己Prompt替换LangChain默认Prompt

某些场景会要求ChatGPT重复处理同一个操作,要么在问题里面加入Prompt,要么用自己Prompt替换LangChain默认Prompt。

直接看看前后对比结果

LangChain默认的Prompt

template="Use the following pieces of context to answer the users question. \nIf you don't know the answer, just say that you don't know, don't try to make up an answer.\n----------------\n{context}"

我自己Prompt替换了之后

template="Use the following pieces of context to answer the users question in Chinese.\n If you don't know the answer, just say that you don't know, don't try to make up an answer.\n There are multiple answers, provide each answer in Chinese,specify the source file for each answer.\n \n\n{context}

源代码如下:

python 复制代码
def getMyPrompt():
    '''自定义Prompt模板'''
    prompt_template = """Use the following pieces of context to answer the users question in Chinese.
    If you don't know the answer, just say that you don't know, don't try to make up an answer.
    There are multiple answers, provide each answer in Chinese,specify the source file for each answer.
    \n\n{context}\n\nQuestion: {question}\n\nAnswer in Chinese:"""
    
    MyPrompt = PromptTemplate(
        template=prompt_template, input_variables=["context","question"]#必须有上下文context和问题question
    )
    return MyPrompt

db_RTCS = Chroma(persist_directory="./RCTS/", embedding_function=embeddings)
print('----------------')
chain_type_kwargs = {"prompt": getMyPrompt()}#用自己的Prompt替换掉langchain默认的Prompt
qa_RTCS = RetrievalQA.from_chain_type(llm=openAiLLm,chain_type="stuff",
                                      retriever=db_RTCS.as_retriever(),
                                      chain_type_kwargs=chain_type_kwargs)

print(qa_RTCS)#查看自定义Prompt的结构体内容
相关推荐
狐狐生风1 小时前
LangChain 向量存储:Chroma、FAISS
人工智能·python·学习·langchain·faiss·agentai
狐狐生风2 小时前
LangChain RAG 基础
人工智能·python·学习·langchain·rag·agentai
DanCheOo9 小时前
AI 应用的安全架构:Prompt 注入、数据泄露、权限边界
前端·人工智能·prompt·安全架构
yanghuashuiyue9 小时前
Deep Agents 框架-前端
langchain·langgraph·deepagents
微软技术分享12 小时前
本地部署千问 2.5-1.5B-GGUF + LangChain 封装学习
数据库·学习·langchain
FrontAI12 小时前
深入浅出 LangGraph —— 第11章:子图:构建模块化Agent
人工智能·langchain·ai agent·langgraph
绘梨衣54713 小时前
Agentic RAG、传统RAG、ReAct、Function Calling 核心关系
人工智能·chatgpt·tensorflow
暗不需求13 小时前
深入理解 LangChain:AI 应用开发框架的工程化实践
前端·langchain
BU摆烂会噶13 小时前
【LangGraph】持久化实现的三大能力——人机交互
数据库·人工智能·python·langchain·人机交互