Langchain中的提示词

• 语言模型提示模板是预定义的生成语言模型提示的方法。
• 模板可能包括指令、少样本示例、特定任务的上下文和问题。
• LangChain 提供了创建和处理提示模板的工具。
• LangChain 致力于创建模型不可知的模板，以便轻松地跨不同的语言模型重用现有的模板。
• 通常，语言模型expects 提示要么是字符串，要么是聊天消息列表。

官网地址

声明: 由于操作系统, 版本更新等原因, 文章所列内容不一定100%复现, 还要以官方信息为准

https://python.langchain.com/

Langchain概述

LangChain是一个用于开发由大型语言模型（LLM）驱动的应用程序的框架。

Langchain的提示词用法

• PromptTemplate: 默认情况下，PromptTemplate 使用 Python 的 str.format 语法进行模板化。

  from langchain_core.prompts import PromptTemplate
  
  prompt_template = PromptTemplate.from_template(
      "Tell me a {adjective} joke about {content}."
  )
  prompt_template.format(adjective="funny", content="chickens")
  

• ChatPromptTemplate: 聊天模型的提示是一个聊天消息列表。每个聊天消息都与内容关联，并有一个额外的参数称为角色。例如，在OpenAI聊天完成API中，一个聊天消息可以与AI助手、人类或系统角色关联。

  from langchain_core.prompts import ChatPromptTemplate
  
  chat_template = ChatPromptTemplate.from_messages(
      [
          ("system", "You are a helpful AI bot. Your name is {name}."),
          ("human", "Hello, how are you doing?"),
          ("ai", "I'm doing well, thanks!"),
          ("human", "{user_input}"),
      ]
  )
  
  messages = chat_template.format_messages(name="Bob", user_input="What is your name?")
  messages
  

• Message Prompts: 提供了不同类型的MessagePromptTemplate
  • AIMessagePromptTemplate
  • SystemMessagePromptTemplate
  • HumanMessagePromptTemplate
  • 任意指定角色的PromptTemplate

  from langchain_core.prompts import ChatMessagePromptTemplate
  
  prompt = "May the {subject} be with you"
  
  chat_message_prompt = ChatMessagePromptTemplate.from_template(
      role="Jedi", template=prompt
  )
  chat_message_prompt.format(subject="force")
  

• MessagesPlaceholder: 完全控制格式化期间要呈现的消息

  from langchain_core.prompts import (
      ChatPromptTemplate,
      HumanMessagePromptTemplate,
      MessagesPlaceholder,
  )
  
  human_prompt = "Summarize our conversation so far in {word_count} words."
  human_message_template = HumanMessagePromptTemplate.from_template(human_prompt)
  
  chat_prompt = ChatPromptTemplate.from_messages(
      [MessagesPlaceholder(variable_name="conversation"), human_message_template]
  )
  from langchain_core.messages import AIMessage, HumanMessage
  
  human_message = HumanMessage(content="What is the best way to learn programming?")
  ai_message = AIMessage(
      content="""\
  1. Choose a programming language: Decide on a programming language that you want to learn.
  
  2. Start with the basics: Familiarize yourself with the basic programming concepts such as variables, data types and control structures.
  
  3. Practice, practice, practice: The best way to learn programming is through hands-on experience\
  """
  )
  
  chat_prompt.format_prompt(
      conversation=[human_message, ai_message], word_count="10"
  ).to_messages()
  

• LCEL
  • PromptTemplate 入参是字典, 返回值是StringPromptValue
  • ChatPromptTemplate 入参是字典, 返回值是ChatPromptValue

  from langchain_core.prompts import PromptTemplate
  prompt_template = PromptTemplate.from_template(
      "Tell me a {adjective} joke about {content}."
  )
  
  prompt_val = prompt_template.invoke({"adjective": "funny", "content": "chickens"})
  prompt_val
  

from langchain_core.messages import SystemMessage
from langchain_core.prompts import ChatPromptTemplate
chat_template = ChatPromptTemplate.from_messages(
    [
        SystemMessage(
            content=(
                "You are a helpful assistant that re-writes the user's text to "
                "sound more upbeat."
            )
        ),
        HumanMessagePromptTemplate.from_template("{text}"),
    ]
)

chat_val = chat_template.invoke({"text": "i dont like eating tasty things."})

更新计划

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