深入解析LangChain及其應用場景

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const { LangChain } = require("langchain");
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// 初始化LangChain
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const chain = new LangChain({
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  model: "gpt-4",
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  apiKey: "your-api-key",
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});
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// 定義上下文管理器
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const contextManager = chain.createContextManager();
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// 添加初始上下文
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contextManager.addContext("user", "Hello, how can I help you today?");
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// 處理用戶輸入並生成響應
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async function handleUserInput(input) {
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  contextManager.addContext("user", input);
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  const response = await chain.generateResponse(contextManager.getContext());
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  contextManager.addContext("bot", response);
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  return response;
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}
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// 示例用戶輸入
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handleUserInput("I need help with my order.").then((response) => {
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  console.log("Bot:", response);
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});

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const { LangChain } = require("langchain");
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const axios = require("axios");
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// 初始化LangChain
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const chain = new LangChain({
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  model: "gpt-4",
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  apiKey: "your-api-key",
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});
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// 定義數據源獲取函數
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async function getDataFromSource1() {
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  const response = await axios.get("https://api.source1.com/data");
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  return response.data;
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}
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async function getDataFromSource2() {
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  const response = await axios.get("https://api.source2.com/data");
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  return response.data;
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}
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// 集成多個數據源並生成響應
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async function generateResponse() {
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  const data1 = await getDataFromSource1();
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  const data2 = await getDataFromSource2();
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  // 組合數據
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  const combinedData = {
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    source1: data1,
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    source2: data2,
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  };
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  const response = await chain.generateResponse(combinedData);
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  return response;
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}
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// 示例調用
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generateResponse().then((response) => {
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  console.log("Generated Response:", response);
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});

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const { LangChain } = require("langchain");
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// 初始化LangChain
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const chain = new LangChain({
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  model: "gpt-4",
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  apiKey: "your-api-key",
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});
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// 定義自定義輸入輸出處理函數
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function customInputProcessor(input) {
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  return `Processed input: ${input}`;
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}
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function customOutputProcessor(output) {
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  return `Processed output: ${output}`;
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}
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// 處理自定義任務
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async function handleCustomTask(input) {
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  const processedInput = customInputProcessor(input);
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  const response = await chain.generateResponse(processedInput);
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  const processedOutput = customOutputProcessor(response);
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  return processedOutput;
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}
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// 示例調用
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handleCustomTask("Custom task input").then((response) => {
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  console.log("Custom Task Response:", response);
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});

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const { LangChain } = require("langchain");
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// 初始化LangChain
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const chain = new LangChain({
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  model: "gpt-4",
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  apiKey: "your-api-key",
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});
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// 定義模板
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const template = `
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  Dear {{name}},
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  Thank you for reaching out to us regarding {{issue}}. We are currently looking into it and will get back to you shortly.
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  Best regards,
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    Support Team
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`;
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// 使用模板生成響應
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async function generateResponseWithTemplate(data) {
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  const response = await chain.generateResponse(template, data);
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  return response;
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}
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// 示例調用
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generateResponseWithTemplate({ name: "John Doe", issue: "your order" }).then(
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  (response) => {
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    console.log("Generated Response:", response);
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  },
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);

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  Dear John Doe,
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  Thank you for reaching out to us regarding your order. We are currently looking into it and will get back to you shortly.
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  Best regards,
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  Support Team

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const { LangChain } = require("langchain");
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// 初始化LangChain
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const chain = new LangChain({
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  model: "gpt-4",
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  apiKey: "your-api-key",
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});
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// 定義鏈式調用
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async function chainOperations(input) {
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  // 第一步：翻譯為法語
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  const step1 = await chain.generateResponse(`Translate to French: ${input}`);
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  // 第二步：總結法語文本
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  const step2 = await chain.generateResponse(`Summarize: ${step1}`);
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  // 第三步：將總結的文本翻譯回英語
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  const step3 = await chain.generateResponse(
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    `Translate back to English: ${step2}`,
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  );
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  return step3;
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}
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// 示例調用
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chainOperations("This is a test sentence.").then((response) => {
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  console.log("Chained Response:", response);
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});

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const { LangChain } = require("langchain");
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// 初始化LangChain
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const chain = new LangChain({
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  model: "gpt-4",
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  apiKey: "your-api-key",
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});
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// 定義模板
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const template = `
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  Hi {{name}},
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  We have received your request regarding {{issue}}. Here is a summary of the information you provided:
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  {{summary}}
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  We will get back to you with more details shortly.
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  Best,
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  Support Team
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`;
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// 定義數據源獲取和處理函數
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async function getDataAndProcess(input) {
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  // 數據源1：用戶輸入
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  const userInput = input;
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  // 數據源2：模擬的外部數據
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  const externalData = await new Promise((resolve) => {
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    setTimeout(() => {
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      resolve("This is some external data related to the issue.");
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    }, 1000);
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  });
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  // 生成總結
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  const summary = await chain.generateResponse(
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    `Summarize: ${userInput} and ${externalData}`,
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  );
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  return { summary };
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}
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// 使用模板和鏈式調用生成響應
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async function generateComplexResponse(data) {
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  const processedData = await getDataAndProcess(data.issue);
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  const completeData = { ...data, ...processedData };
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  const response = await chain.generateResponse(template, completeData);
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  return response;
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}
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// 示例調用
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generateComplexResponse({ name: "Jane Doe", issue: "a billing problem" }).then(
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  (response) => {
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    console.log("Generated Complex Response:", response);
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  },
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);

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  Hi Jane Doe,
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  We have received your request regarding a billing problem. Here is a summary of the information you provided:
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  This is a summary of the user input and some external data related to the issue.
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  We will get back to you with more details shortly.
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  Best,
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  Support Team