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GPT-3 REST API node.js example like ChatGPT

gpt-3 codehooks

This is an example of a serverless Node.js application that uses to create a REST API for the OpenAI GPT-3 language model (which is similar in many ways to ChatGPT). The API allows REST API clients to send text prompts to the GPT-3 model and receive a response in return.

The code uses the codehooks-js library to handle routing and middleware for the API, and the node-fetch library to make HTTP requests to the OpenAI API. The code also uses the built-in Key-Value store to cache the response from the OpenAI API for 60 seconds, in order to reduce the number of requests made to the OpenAI API (If you want to create your own Node.js Express backend, you just need to replace this cache with Redis for example).

Additionally, the code also implements a middleware to rate limit the number of requests coming from the same IP address to 10/min.


Create the OpenAI API keyโ€‹

Navigate to your Open AI account settings and create a new secret key.


Create a new project to host the GPT-3 REST APIโ€‹

coho create gptrestapi

This will create a new directory for your REST API backend application.

cd gptrestapi

Open the index.js file and replace the code with the source code at the end of this example.

Create a new secret environment variableโ€‹

Once you have created your Open AI API secret key, you need to add this to the serverless runtime. You can use the CLI command for this.

coho set-env OPENAI_API_KEY 'XXXXXXXX' --encrypted

Deploy the Node.js ChatGPT-like REST API to the backendโ€‹

From the project directory run the CLI command coho deploy. The output from my test project is shown below:

coho deploy 
Project: gptrestapi-pwnj Space: dev

Deployed Codehook successfully! ๐Ÿ™Œ

Test your ChatGPT REST APIโ€‹

The following curl example demonstrates that we have a live REST API that takes a POST body with a text string ask and returns the response from the Open AI API.

curl -X POST \
'' \
--header 'x-apikey: cb17c77f-2821-489f-a6b4-fb0dae34251b' \
--header 'Content-Type: application/json' \
--data-raw '{
"ask": "Make a great title for a code example with GPT-3 and serverless node.js with"

Output from the GPT-3 REST API is shown below.

"Building Conversational AI with GPT-3 and Serverless Node.js Using"

Another example shows that we a actually talking to the Open AI API.

curl -X POST \
'' \
--header 'x-apikey: cb17c77f-2821-489f-a6b4-fb0dae34251b' \
--header 'Content-Type: application/json' \
--data-raw '{
"ask": "Tell a joke"

Output from the second example ๐Ÿ˜‚:

Q: What did the fish say when it hit the wall?
A: Dam!

Full source code exampleโ€‹

* GPT-3 REST API example using serverless node.js and

import { app, Datastore } from 'codehooks-js';
import fetch from 'node-fetch';

// REST API routes'/chat', async (req, res) => {
if (!process.env.OPENAI_API_KEY) return res.status(500).end('Please add your OPENAI_API_KEY'); // CLI command: coho set-env OPENAI_API_KEY 'XXX'
const { ask } = req.body;
const db = await;
const cacheKey = 'chatgpt_cache_' + ask;

// check cache first
const cachedAnswer = await db.get(cacheKey);

// get from cache or OpenAi
if (cachedAnswer) {
} else { // get from Open AI API

// pick text element from the OpenAI response by JS nested destructuring
const { choices: { 0: { text } } } = await callOpenAiApi(ask);
console.log(ask, text);

// add to cache for 1 minute
await db.set(cacheKey, text, { ttl: 60 * 1000 });
// send text back to client

// Call OpenAI API
async function callOpenAiApi(ask) {
const raw = JSON.stringify({
"model": "text-davinci-003",
"prompt": ask,
"temperature": 0.6,
"max_tokens": 1024,
"stream": false

var requestOptions = {
method: 'POST',
headers: {
"Content-Type": "application/json",
"Authorization": `Bearer ${process.env.OPENAI_API_KEY}`
body: raw,
redirect: 'follow'

const response = await fetch("", requestOptions);
return response.json();


// global middleware to IP rate limit traffic
app.use(async (req, res, next) => {
const db = await;
// get client IP address
const ipAddress = req.headers['x-real-ip'];
// increase count for IP
const count = await db.incr('IP_count_' + ipAddress, 1, { ttl: 60 * 1000 })
console.log(ipAddress, count);
if (count > 10) {
// too many calls
res.status(429).end("Sorry too many requests for this IP");
} else {
// ok to proceed

// Export app to the serverless runtime
export default app.init();

Full source is available on our GitHub example repo. We will update this example when ChatGPT becomes available as an API.