少样本提示模板

少样本提示是一种提示技术，它为大型语言模型 (LLM) 提供了一系列示例，然后要求 LLM 生成一些遵循所提供示例的文本。

以下是这种情况的一个示例

假设您希望 LLM 以特定的格式进行响应。您可以使用少样本提示为 LLM 提供一系列问答对，以便它了解要以什么格式进行响应。

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:

这里我们将最后一个 答案： 留空，以便 LLM 可以填写它。LLM 然后将生成以下内容

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

用例

在下面的示例中，我们对 LLM 进行少样本提示，将其问题重新表述为更通用的查询。

我们提供了两组带有特定问题和重新表述的通用问题的示例。FewShotChatMessagePromptTemplate 将使用我们的示例，并在调用 .format 时，我们将看到这些示例被格式化为可以传递给 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，如果我们使用它和另一个问题，LLM 将按照我们想要的方式重新表述该问题。

提示

请参阅 此部分以获取有关安装集成包的常规说明。

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 }
}

使用函数进行少样本提示

您还可以使用函数进行部分提示。这种情况的用例是在您有一个变量时，您知道始终希望以一种常见的方式获取它。一个典型的例子是使用日期或时间。假设您有一个提示，您始终希望它包含当前日期。您不能在提示中对其进行硬编码，并且将它与其他输入变量一起传递可能很繁琐。在这种情况下，能够使用始终返回当前日期的函数来部分提示提示非常方便。

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

少样本提示与聊天少样本提示

聊天和非聊天少样本提示模板的行为相似。以下示例将演示使用聊天和非聊天，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?
 */

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 通过获取 ChatPromptTemplate 的列表作为示例来工作，其输出是 BaseMessage 实例的列表。

另一方面，FewShotPromptTemplate 通过获取 PromptTemplate 作为示例来工作，其输出是一个字符串。

与非聊天模型一起使用

LangChain 还为非聊天模型提供了用于少样本提示格式化的类：FewShotPromptTemplate。该 API 基本上相同，但输出格式不同（聊天消息与字符串）。

使用函数进行部分提示

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

使用函数和示例选择器

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