Как создать базовый чат-бот с использованием TensorFlow и JavaScript

mkdir chatbot
cd chatbot
npm init -y

npm install @tensorflow/tfjs @tensorflow-models/universal sentence-encoder

const intents = {
  greeting: ["hello", "hi", "hey", "good morning", "good evening", "howdy"],
  goodbye: ["bye", "goodbye", "see you later", "farewell", "catch you later"],
  thanks: ["thank you", "thanks", "much appreciated", "thank you very much"],
  product_inquiry: ["tell me about your products", "what do you sell?", "product information", "what can I buy?", "show me your products"],
  order_status: ["where is my order?", "order status", "track my order", "order tracking", "order update"],
  shipping_info: ["shipping information", "how do you ship?", "shipping methods", "delivery options", "how long does shipping take?"],
  return_policy: ["return policy", "how to return?", "return process", "can I return?", "returns"],
  payment_methods: ["payment options", "how can I pay?", "payment methods", "available payments"],
  support_contact: ["contact support", "how to contact support?", "customer support contact", "support info", "customer service contact"],
  business_hours: ["business hours", "working hours", "when are you open?", "opening hours", "store hours"]
};

module.exports = { intents }

const responses = {
  greeting: "Hello! How can I help you today?",
  goodbye: "Goodbye! Have a great day!",
  thanks: "You're welcome! If you have any other questions, feel free to ask.",
  product_inquiry: "We offer a variety of products including electronics, books, clothing, and more. How can I assist you further?",
  order_status: "Please provide your order ID, and I will check the status for you.",
  shipping_info: "We offer various shipping methods including standard, express, and next-day delivery. Shipping times depend on the method chosen and your location.",
  return_policy: "Our return policy allows you to return products within 30 days of purchase. Please visit our returns page for detailed instructions.",
  payment_methods: "We accept multiple payment methods including credit/debit cards, PayPal, and bank transfers. Please choose the method that suits you best at checkout.",
  support_contact: "You can contact our support team via email at support@example.com or call us at 1-800-123-4567.",
  business_hours: "Our business hours are Monday to Friday, 9 AM to 5 PM. We are closed on weekends and public holidays."
};

module.exports = { responses };

const tf = require('@tensorflow/tfjs');
const use = require('@tensorflow-models/universal-sentence-encoder');
const { intents } = require('./intents');
const { responses } = require('./responses');
const readline = require('readline');

// Load the Universal Sentence Encoder model
let model;
use.load().then((loadedModel) => {
  model = loadedModel;
  console.log("Model loaded");
  startChatbot();
});

async function recognizeIntent(userInput) {
  const userInputEmb = await model.embed([userInput]);
  let maxScore = -1;
  let recognizedIntent = null;

  for (const [intent, examples] of Object.entries(intents)) {
    // Embedding the example phrases for each intent & Calculating similarity scores between the user input embedding and the example embeddings
    const examplesEmb = await model.embed(examples);
    const scores = await tf.matMul(userInputEmb, examplesEmb, false, true).data();
    const maxExampleScore = Math.max(...scores);
    if (maxExampleScore > maxScore) {
      maxScore = maxExampleScore;
      recognizedIntent = intent;
    }
  }
  return recognizedIntent;
}

async function generateResponse(userInput) {
  const intent = await recognizeIntent(userInput);
  if (intent && responses[intent]) {
    return responses[intent];
  } else {
    return "I'm sorry, I don't understand that. Can you please rephrase?";
  }
}

function startChatbot() {
  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout
  });

  console.log("Welcome to the customer service chatbot! Type 'quit' to exit.");
  rl.prompt();

  rl.on('line', async (line) => {
    const userInput = line.trim();
    if (userInput.toLowerCase() === 'quit') {
      console.log("Chatbot: Goodbye!");
      rl.close();
      return;
    }

    const response = await generateResponse(userInput);
    console.log(`Chatbot: ${response}`);
    rl.prompt();
  });
}

const tf = require('@tensorflow/tfjs');
const use = require('@tensorflow-models/universal-sentence-encoder');
const { intents } = require('./intents');
const { responses } = require('./responses');
const readline = require('readline');

// Load the Universal Sentence Encoder model
let model;
use.load().then((loadedModel) => {
  model = loadedModel;
  console.log("Model loaded");
  startChatbot();
});

async function recognizeIntent(userInput) {
  const userInputEmb = await model.embed([userInput]);
  let maxScore = -1;
  let recognizedIntent = null;

  for (const [intent, examples] of Object.entries(intents)) {
    const examplesEmb = await model.embed(examples);
    const scores = await tf.matMul(userInputEmb, examplesEmb, false, true).data();
    const maxExampleScore = Math.max(...scores);
    if (maxExampleScore > maxScore) {
      maxScore = maxExampleScore;
      recognizedIntent = intent;
    }
  }

  return recognizedIntent;
}

async function generateResponse(userInput) {
  const intent = await recognizeIntent(userInput);
  if (intent && responses[intent]) {
    return responses[intent];
  } else {
    return "I'm sorry, I don't understand that. Can you please rephrase?";
  }
}

function startChatbot() {
  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout
  });

  console.log("Welcome to the customer service chatbot! Type 'quit' to exit.");
  rl.prompt();

  rl.on('line', async (line) => {
    const userInput = line.trim();
    if (userInput.toLowerCase() === 'quit') {
      console.log("Chatbot: Goodbye!");
      rl.close();
      return;
    }

    const response = await generateResponse(userInput);
    console.log(`Chatbot: ${response}`);
    rl.prompt();
  });
}

node chatbot.js