How to Build and Deploy a Node.js App That Uses OpenAI’s APIs

Near the end of 2023, ChatGPT announced that it had 100M weekly users. That’s a massive base of users who want to take advantage of the convenience and power of intelligent question answering with natural language.

With this level of popularity for ChatGPT, it’s no wonder that software developers are joining the ChatGPT app gold rush, building tools on top of OpenAI’s APIs. Building and deploying a GenAI-based app is quite easy to do—and we’re going to show you how!

This blog post is adapted from a talk given by Julián Duque at NodeConf EU 2019 titled "Let it crash!."

Before coming to Heroku, I did some consulting work as a Node.js solutions architect. My job was to visit various companies and make sure that they were successful in designing production-ready Node applications. Unfortunately, I witnessed many different problems when it came to error handling, especially on process shutdown. When an error occurred, there was often not enough visibility on why it happened, a lack of logging details, and bouts of downtime as applications attempted to recover from crashes.

Building a SaaS product, a system to handle sensor data from an internet-connected thermostat or car, or an e-commerce store often requires handling a large stream of product usage data, or events. Managing event streams lets you view, in near real-time, how users are interacting with your SaaS app or the products on your e-commerce store; this is interesting because it lets you spot anomalies and get immediate data-driven feedback on new features. While this type of stream visualization is useful to a point, pushing events into a data warehouse lets you ask deeper questions using SQL.

In this post, we’ll show you how to build a system using Apache Kafka on Heroku to manage and visualize...

The CLI Team at Heroku strives to create a CLI user experience that is intuitive and productive. We had “build CLI autocomplete” in the icebox of our roadmap for many years. But if we were going to ship it, it had to complement the existing CLI experience. This is challenging because the Heroku CLI is very dynamic: it comprises user installable plugins, and the data needed for completions is behind an API.

Recently, we spent some time brainstorming the experience we wanted from Heroku CLI Autocomplete and decided it was time. We took “build autocomplete” out of the icebox and shipped it.

This post will discuss the main challenges we faced building Heroku CLI Autocomplete and how we solved...

Heroku recently released [a managed Apache Kafka][1] offering. As a Node.js developer, I wanted to demystify Kafka by sharing a simple yet practical use case with the many Node.js developers who are curious how this technology might be useful. At Heroku we use Kafka internally for a number of uses including data pipelines. I thought that would be a good place to start.

When it comes to actual examples, Java and Scala get all the love in the Kafka world. Of course, these are powerful languages, but I wanted to explore Kafka from the perspective of Node.js. While there are no technical limitations to using Node.js with Kafka, I was unable to find many examples of their use together in...