openai_api_key

twitter_api_key

twitter_api_secret_key

twitter_access_token

twitter_access_token_secret

Lead the GenAI revolution by learning to build intelligent, scalable applications with OpenAI APIs, Redis, and Node.js. Future-proof your skills with hands-on projects and practical experience.

Harsh Jain - Build Open AI Applications Using JavaScript (1).png

docker.tar.gz

Node With Express

Node Without Express

Node Without Express with Redis

Node With Express and Redis

In this course, you’ll get an introduction to Node.js and Express.js. You’ll learn about large language models—including how a general LLM works, OpenAI’s GPT model, and its APIs with Node.js. You’ll also learn about Redis to implement API rate limiting. You’ll then gain hands-on, practical experience via three applied projects. The first involves building a Q&A bot using the PDF document to understand embeddings. The second includes building a YouTube video summarizer app to understand speech-to-text processing using OpenAI’s Whipser model. The final project is tol build a Twitter tweet generator to learn image generation with Redis’s role in managing API rate limits.

After taking this course, you can develop intelligent and scalable applications using OpenAI APIs, Redis, and Node.js.

Build OpenAI Applications with JavaScript

Large Language Models (LLMs) represent a breakthrough in the field of natural language processing, revolutionizing how computers understand and generate human-like text. These models, such as OpenAI's GPT (Generative Pre-trained Transformer), are trained on vast amounts of diverse textual data, enabling them to learn the intricacies of language structure, semantics, and context. LLMs leverage transformer architectures, which facilitate capturing long-range dependencies in text, making them particularly adept at tasks like language translation, summarization, and question answering.  One of the key strengths of LLMs lies in their pre-training phase, where the model learns from a wide range of internet text before fine-tuning on specific tasks. This pre-training enables LLMs to acquire a broad understanding of language, allowing them to adapt and excel in various language-related applications. GPT, for instance, can generate coherent and contextually relevant paragraphs, articles, or responses, showcasing its ability to contextualize information and produce human-like outputs.  Despite their impressive capabilities, LLMs also face challenges such as potential biases present in the training data and ethical concerns regarding their use. Researchers and developers continue to refine these models, aiming to strike a balance between their remarkable language generation capabilities and the responsible deployment of such technology in diverse applications. As LLMs pave the way for more advanced natural language understanding, their ongoing development promises to reshape how we interact with and benefit from intelligent language-based systems.

// Import the required modules
const { OpenAI } = require("openai");
const fs = require("fs");

// Implement the below function to read the data from text file
const readData = () => {
    const data = fs.readFileSync('./data.txt', 'utf-8');
    return data;
}


// Implement the below function to fetch the posts
const fetchSummary = async (text) => {

    // Instantiate the OpenAI client
    const openai = new OpenAI({ apiKey: openai_api_key });

    const response = await openai.chat.completions.create({
        model: "gpt-3.5-turbo",
        messages: [
            {
                "role": "system",
                "content": `Generate a summarized text for the given input text`
            },
            {
                "role": "user",
                "content": text
            }
        ]
    });

    return response.choices[0].message.content;
}

// Export the functions
module.exports = {
    readData,
    fetchSummary
}

// Importing the required module and functions
const express = require("express");
const { readData, fetchSummary } = require('./utils.js');

// Instantiating an object express
const app = express();

app.get('/get-summary', async (req, res) => {

    // Implement the API route handling logic
    try {
        const data = readData();
        const summary = await fetchSummary(data);
        res.send({ summary });
    } catch (e) {
        res.send({'error': e.message}).status(400);
    }
})

// Using the express object to listen to port 8000 for incoming requests
app.listen(8000, async () => {
    console.log("The App is listening on port 8000!");
});

import React from 'react';
import { shallow, configure } from 'enzyme';
import Adapter from 'enzyme-adapter-react-16';

configure({ adapter: new Adapter() });

import HelloWorld from './app';

var TestResult = function() {
    this.succeeded = false;
    this.reason = "";
    this.input = "";
    this.expected_output = "";
    this.actual_output = "";
}

export const executeTests = function() {

  var results = [];

  result = new TestResult();
  result.input = 'HelloWorld Component';
  result.expected_output = "span containing text 'Hello World'"

  let wrapper = shallow(<HelloWorld />);

  // Call your Challenge function here.

  let type = wrapper.type();
  let testSuccessful = true;
  let failureReason;

  if (type !== 'span') {
    testSuccessful = false;
    failureReason = "You need to render exactly one span HTML element";
  } else if (wrapper.props().children != "Hello World") {
     testSuccessful = false;
     failureReason = "You have rendered wrong message in your span element";
  }

  result.actual_output = wrapper.html();

  if (testSuccessful) {
    result.succeeded = true;
    result.reason = "Succeeded"
  } else {
    result.succeeded = false;
    result.reason = failureReason;
  }

  results.push(result);

  return results;
}


React

Dart

GoJS React

Typescript React

Vue.js

Take a look at the solution for the coding challenge to build a text summarize using OpenAI with middleware validations.


Solution: Text Summarizer Using OpenAI

Take a look at the solution for the coding challenge to build a text summarize using OpenAI with middleware validations.

Overview of the Course

Quick Bites to Node.js and Express.js

Build a Rest API for an Image-Sharing App With Node.js

Introduction to OpenAI and LLMs

Document Q&A Bot Using OpenAI

YouTube Video Captions Project

Quick Bites to Redis with Node.js

Twitter Tweet Generator

Wrap Up

Course Assessment

Exploring OpenAI API

Solution: Text Summarizer Using OpenAI

Solution