OpenAI APIs with Node.js: Building Next-Generation Web Apps

Create an OpenAI account and API key, then build a NodeJS app that talks to OpenAI, convert it to an express server, and explore GPT chat and Dall-E image generation.

Create a node.js application, install OpenAI, dotenv, and express, and configure an OpenAI object using the OpenAI API key, organization ID, and project ID via a config file.

Discover OpenAI models for building next-generation web apps, including GPT-4 turbo, GPT-3.5 turbo, DALL-E, TTS, Whisper, embeddings, and moderation, with emphasis on tokens, context windows, and vision features.

Learn to call the OpenAI chat completion API with GPT-3.5 (and GPT-4 by model name), using system and user messages, a messages array, temperature control, and token handling.

Transform your Node.js app by converting it to Express and exposing JSON-based REST APIs, tested with Postman, and powered by OpenAI chat completion calls.

Build a front-end with ChatGPT to call a REST API using a public static page, bootstrap, and vanilla JS; submit prompts and display responses.

Learn to implement streaming in chat completion with Node.js by delivering tokens as soon as they are ready. Explore chunked rest API and front-end that render tokens token by token.

Explore OpenAI API costs, including input and output token pricing, embeddings and image and audio models, plus per-minute rate limits and the $5 free credit.

Discover what tokens are and why they matter for cost and model limits. Learn to estimate token counts using tokenizer tools, examples for prompts and responses, including GPT 3.5 turbo.

Explore chat completion parameters in OpenAI APIs with Node.js, including temperature, max tokens, and context window. Learn how n, stop sequences, and frequency penalty shape deterministic versus probabilistic outputs.

Learn to use the system role to control how the chat model replies, whether brief or elaborate, and to provoke in-depth examples on OpenAI playground.

Learn how to generate unique images from text with Dolly, create variations of existing images, and edit images using a mask and a new prompt.

Develop image variations with Dall-E two using the OpenAI API, handling base64 images, converting to a read stream, and returning a variation URL via a REST API.

Learn to edit images with DALL-E in a Node.js workflow using an image, a mask, and a full image prompt to produce an edited image URL.

Discover whisper, an open-source neural network for robust English speech recognition, with OpenAI's transcription (many languages to same language) and translation (many languages to English) endpoints.

Compare tts-1 and tts-1-hd text-to-speech models, featuring six voices, and demonstrate generating mp3 from text in a Node.js controller for web delivery.

Explore embeddings as high-dimensional text vectors that capture semantic meaning, measure similarity with cosine distance, and implement OpenAI text embeddings in node applications.

design and set up the emotion analyzer project in node.js, input emotional keywords, and analyze text to identify expressed emotions, enabling social media monitoring, product reviews, and email filtering.

Create a GPT analyze emotions function using the OpenAI chat completion model to classify text into emotions with a system and user prompt.

Expose a rest API with Express in Node.js to call the GPT analyze emotions function, reading emotions and prompt from the request body and returning a JSON content result.

Build a bootstrap-styled HTML page with vanilla JavaScript that calls a local API to classify text by emotions, showing live results and a history log.

Build a ChatGPT clone with Node.js and the GPT 4.5 turbo model to deliver context retention and human-like responses for customer support, chatbots, virtual assistants, and language translation.

Build a dialogue with the model using OpenAI chat completion in Node.js. Learn to structure system and user messages, maintain history for context, and set a deterministic temperature.

Generalize dialogue with recursion by maintaining a history of the system prompt, user questions, and assistant responses in a command line chat app.

Convert a chat controller into a REST API with Express, expose a post route at OpenAI/ChatGPT_clone, parse JSON messages, and test with Postman.

Develop the frontend for a chat-based GPT clone using vanilla JavaScript and bootstrap, wiring a REST API with system and user prompts and a live history display.

introduces fridge chef, an ai kitchen assistant that generates three personalized meals with ingredients, instructions, calories, and images using OpenAI GPT-4 Turbo and Dolly; demonstrates a node.js setup with express.

Use chat completion in node.js to generate a healthy three-meal plan from given ingredients under a 2000 kcal limit, with titled breakfast, lunch, and dinner.

Parse titles and meals to extract meal titles from chat completion output and build breakfast, lunch, and dinner entries using splitting and regex cleanup.

Generate one image for each of the three meals (breakfast, lunch, dinner) using the OpenAI image API, attach image URLs to each meal, and return the enriched meals list.

Create a REST API with Express by turning the controller into a web service, reading ingredients and Kilocalorie from the request body, and returning a meals list at /OpenAI/meals.

Build a bootstrap front end with vanilla JavaScript that calls a REST API to render breakfast, lunch, and dinner with images and descriptions, while showing loading states.

YouTube insights transcribes and translates YouTube videos with whisper, then uses OpenAI chat completion to extract keywords and topics from the transcription.

Download YouTube audio from a video using a node.js library and save as mp3. Transcribe or translate the audio and generate insights with the OpenAI chat completion model.

Master transcribing or translating audio with the whisper model using OpenAI APIs in Node.js, handling file existence, saving transcripts, and distinguishing English vs non-English workflows.

Obtain insights from a transcript using the chat completion model, extracting keywords and topics with a JSON output that includes keyword arrays and topic titles and descriptions.

Create a frontend with ChatGPT to call a json web API that generates insights, keywords, and topics for a YouTube video, using Bootstrap, vanilla JavaScript, and a single html file.

Learn to build a semantic search project with OpenAI API embeddings and machine learning in Node.js, delivering top-k semantically similar results for e-commerce, customer support, academic search, and research literature.

Generate embeddings with the OpenAI embeddings model text-embedding-ada-002 and measure similarities using cosine similarity calculated from dot product and vector norms.

Cache embeddings in a tab-separated values cache file to avoid repeated OpenAI calls and reduce costs. Read from the cache first, and append new embeddings as needed.

implement an asynchronous semantic search function in Node.js that embeds the search term and statements, computes cosine similarity, and returns the top k matches.

Convert the semantic search function into a rest API using express, with a post route at the OpenAI/semantic search endpoint and a json body containing search_term, target_list, and k.

Build a web page that uses a semantic search REST API, with a ChatGPT-generated prompt, displaying results (index, text, similarity) in a Bootstrap UI.