小样本提示模板

小样本提示是一种提示技术，它为大型语言模型 (LLM) 提供一个示例列表，然后要求 LLM 生成一些文本，以遵循所提供示例的引导。

这方面的一个例子如下

假设你希望你的 LLM 以特定格式响应。你可以使用问题答案对的小样本提示 LLM，以便它知道以何种格式响应。

Respond to the users question in the with the following format:

Question: What is your name?
Answer: My name is John.

Question: What is your age?
Answer: I am 25 years old.

Question: What is your favorite color?
Answer:

这里我们将最后的 `Answer:` 保留为未定义，以便 LLM 可以填写它。然后 LLM 将生成以下内容

Answer: I don't have a favorite color; I don't have preferences.

用例

在以下示例中，我们使用小样本提示 LLM 将问题改写为更通用的查询。

我们提供了两组示例，其中包含具体问题和改写后的通用问题。`FewShotChatMessagePromptTemplate` 将使用我们的示例，当调用 `.format` 时，我们将看到这些示例被格式化为我们可以传递给 LLM 的字符串。

import {
  ChatPromptTemplate,
  FewShotChatMessagePromptTemplate,
} from "langchain/prompts";

const examples = [
  {
    input: "Could the members of The Police perform lawful arrests?",
    output: "what can the members of The Police do?",
  },
  {
    input: "Jan Sindel's was born in what country?",
    output: "what is Jan Sindel's personal history?",
  },
];
const examplePrompt = ChatPromptTemplate.fromTemplate(`Human: {input}
AI: {output}`);
const fewShotPrompt = new FewShotChatMessagePromptTemplate({
  examplePrompt,
  examples,
  inputVariables: [], // no input variables
});

const formattedPrompt = await fewShotPrompt.format({});
console.log(formattedPrompt);

[
  HumanMessage {
    lc_namespace: [ 'langchain', 'schema' ],
    content: 'Human: Could the members of The Police perform lawful arrests?\n' +
      'AI: what can the members of The Police do?',
    additional_kwargs: {}
  },
  HumanMessage {
    lc_namespace: [ 'langchain', 'schema' ],
    content: "Human: Jan Sindel's was born in what country?\n" +
      "AI: what is Jan Sindel's personal history?",
    additional_kwargs: {}
  }
]

然后，如果我们将其用于另一个问题，LLM 将按照我们想要的方式改写问题。

提示

请参阅此章节，获取关于安装集成包的通用说明。

npm

npm install @langchain/openai @langchain/core

Yarn

yarn add @langchain/openai @langchain/core

pnpm

pnpm add @langchain/openai @langchain/core

import { ChatOpenAI } from "@langchain/openai";

const model = new ChatOpenAI({});
const examples = [
  {
    input: "Could the members of The Police perform lawful arrests?",
    output: "what can the members of The Police do?",
  },
  {
    input: "Jan Sindel's was born in what country?",
    output: "what is Jan Sindel's personal history?",
  },
];
const examplePrompt = ChatPromptTemplate.fromTemplate(`Human: {input}
AI: {output}`);
const fewShotPrompt = new FewShotChatMessagePromptTemplate({
  prefix:
    "Rephrase the users query to be more general, using the following examples",
  suffix: "Human: {input}",
  examplePrompt,
  examples,
  inputVariables: ["input"],
});
const formattedPrompt = await fewShotPrompt.format({
  input: "What's France's main city?",
});

const response = await model.invoke(formattedPrompt);
console.log(response);

AIMessage {
  lc_namespace: [ 'langchain', 'schema' ],
  content: 'What is the capital of France?',
  additional_kwargs: { function_call: undefined }
}

使用函数进行小样本提示

你也可以使用函数进行部分应用。这种情况的用例是当你有一个变量，你知道你总是想以通用的方式获取它。日期或时间就是一个典型的例子。想象一下，你有一个提示，你总是想拥有当前日期。你不能将其硬编码在提示中，并且将其与其他输入变量一起传递可能会很繁琐。在这种情况下，能够使用一个总是返回当前日期的函数来部分应用提示是非常方便的。

const getCurrentDate = () => {
  return new Date().toISOString();
};

const prompt = new FewShotChatMessagePromptTemplate({
  template: "Tell me a {adjective} joke about the day {date}",
  inputVariables: ["adjective", "date"],
});

const partialPrompt = await prompt.partial({
  date: getCurrentDate,
});

const formattedPrompt = await partialPrompt.format({
  adjective: "funny",
});

console.log(formattedPrompt);

// Tell me a funny joke about the day 2023-07-13T00:54:59.287Z

小样本提示 vs 聊天小样本提示

聊天和小样本提示模板的作用方式类似。下面的示例将演示如何使用聊天和非聊天方式，以及它们输出的差异。

import {
  FewShotPromptTemplate,
  FewShotChatMessagePromptTemplate,
} from "langchain/prompts";

const examples = [
  {
    input: "Could the members of The Police perform lawful arrests?",
    output: "what can the members of The Police do?",
  },
  {
    input: "Jan Sindel's was born in what country?",
    output: "what is Jan Sindel's personal history?",
  },
];
const prompt = `Human: {input}
AI: {output}`;
const examplePromptTemplate = PromptTemplate.fromTemplate(prompt);
const exampleChatPromptTemplate = ChatPromptTemplate.fromTemplate(prompt);
const chatFewShotPrompt = new FewShotChatMessagePromptTemplate({
  examplePrompt: exampleChatPromptTemplate,
  examples,
  inputVariables: [], // no input variables
});
const fewShotPrompt = new FewShotPromptTemplate({
  examplePrompt: examplePromptTemplate,
  examples,
  inputVariables: [], // no input variables
});

console.log("Chat Few Shot: ", await chatFewShotPrompt.formatMessages({}));
/**
Chat Few Shot:  [
  HumanMessage {
    lc_namespace: [ 'langchain', 'schema' ],
    content: 'Human: Could the members of The Police perform lawful arrests?\n' +
      'AI: what can the members of The Police do?',
    additional_kwargs: {}
  },
  HumanMessage {
    lc_namespace: [ 'langchain', 'schema' ],
    content: "Human: Jan Sindel's was born in what country?\n" +
      "AI: what is Jan Sindel's personal history?",
    additional_kwargs: {}
  }
]
 */

console.log("Few Shot: ", await fewShotPrompt.formatPromptValue({}));
/**
Few Shot:

Human: Could the members of The Police perform lawful arrests?
AI: what can the members of The Police do?

Human: Jan Sindel's was born in what country?
AI: what is Jan Sindel's personal history?
 */

在这里，我们可以看到 `FewShotChatMessagePromptTemplate` 和 `FewShotPromptTemplate` 之间的主要区别：输入和输出值。

FewShotChatMessagePromptTemplate 的工作方式是接收一个 `ChatPromptTemplate` 示例列表，其输出是 `BaseMessage` 实例的列表。

另一方面，`FewShotPromptTemplate` 的工作方式是接收一个 `PromptTemplate` 示例，其输出是一个字符串。

与非聊天模型一起使用

LangChain 还为非聊天模型的小样本提示格式化提供了一个类：`FewShotPromptTemplate`。API 大致相同，但输出格式不同（聊天消息与字符串）。

使用函数进行部分应用

import {
  ChatPromptTemplate,
  FewShotChatMessagePromptTemplate,
} from "langchain/prompts";

const examplePrompt = PromptTemplate.fromTemplate("{foo}{bar}");
const prompt = new FewShotPromptTemplate({
  prefix: "{foo}{bar}",
  examplePrompt,
  inputVariables: ["foo", "bar"],
});
const partialPrompt = await prompt.partial({
  foo: () => Promise.resolve("boo"),
});
const formatted = await partialPrompt.format({ bar: "baz" });
console.log(formatted);

boobaz\n

与函数和示例选择器一起使用

import {
  ChatPromptTemplate,
  FewShotChatMessagePromptTemplate,
} from "langchain/prompts";

const examplePrompt = PromptTemplate.fromTemplate("An example about {x}");
const exampleSelector = await LengthBasedExampleSelector.fromExamples(
  [{ x: "foo" }, { x: "bar" }],
  { examplePrompt, maxLength: 200 }
);
const prompt = new FewShotPromptTemplate({
  prefix: "{foo}{bar}",
  exampleSelector,
  examplePrompt,
  inputVariables: ["foo", "bar"],
});
const partialPrompt = await prompt.partial({
  foo: () => Promise.resolve("boo"),
});
const formatted = await partialPrompt.format({ bar: "baz" });
console.log(formatted);

boobaz
An example about foo
An example about bar