Constructing a Twitter bot with Node.js and OpenAI | by Samuel | Jan, 2023

Whats up fellow builders,

On this tutorial, I’ll present you the way to construct a Twitter bot that feedback on tweets that point out a sure key phrase each half-hour.

This bot makes use of the OpenAI API to generate distinctive and related feedback for every tweet.

Not solely is that this a enjoyable mission to work on, however it can be a great tool for anybody trying to enhance their presence on social media and interact with their viewers in a significant means.

You possibly can take a look at the GitHub repo from right here.

Conditions:

Earlier than we get began, ensure you have the next:

• A Twitter account and developer account (you will have to create a Twitter developer account to get your API keys)
• A free OpenAI API key (you will have this to generate feedback for the tweets)
• Node.js and npm put in in your machine
• A code editor of your selection (I can be utilizing Visible Studio Code)

Step 1: Arrange the mission

First, create a brand new listing on your mission and navigate to it in your terminal. Then, run the next command to initialize a brand new npm mission:

npm init -y

This can create a bundle.json file in your mission listing.

Subsequent, set up the required dependencies by working the next instructions:

npm set up axios twit

Axios is a library that we’ll use to make API requests, and Twit is a library that permits us to work together with the Twitter API.

Step 2: Arrange the Twitter API

Now that we now have the required libraries put in, it’s time to arrange the Twitter API. In your mission listing, create a brand new file known as index.js.

Then, add the next code to the file, changing the placeholder textual content with your individual Twitter API keys:

import Twit from "twit";
const twitterConsumerKey = "Shopper Key";
const twitterConsumerSecret = "Shopper Secret Key";
const twitterAccessToken = "Acess Token";
const twitterAccessTokenSecret = "Entry Token Secret";
const api = new Twit(
consumer_key: twitterConsumerKey,
consumer_secret: twitterConsumerSecret,
access_token: twitterAccessToken,
access_token_secret: twitterAccessTokenSecret,
);

Step 3: Arrange the OpenAI API

Now that we now have the Twitter API arrange, let’s arrange the OpenAI API. Add the next code to your index.js file, changing the placeholder textual content with your individual OpenAI API key:

import axios from "axios";
const openaiApiKey = "Open AI API key";

Step 4: Seek for tweets utilizing the Twitter API

Now that we now have authenticated our Twitter API consumer, we will use it to seek for tweets that point out the key phrase we specified. On this case, we’re trying to find tweets that point out the key phrase “javascript”.

To do that, we use the api.get() technique and move within the endpoint search/tweets and an object with the next properties:

• q: the question string to seek for
• rely: the utmost variety of tweets to return

We save the search outcomes to a variable known as searchResults and log the variety of tweets discovered within the console.

  const question = 'javascript';
const maxTweets = 100;
const  information: searchResults  = await api.get('search/tweets', 
q: question,
rely: maxTweets
);
console.log(`Discovered $searchResults.statuses.size tweets. Producing feedback...`);

Step 5: Generate feedback utilizing OpenAI API

On this step, we’ll use the OpenAI API to generate distinctive and related feedback for every tweet that we looked for within the earlier step.

To do that, we make a POST request to the OpenAI API with the next choices:

• mannequin: The title of the mannequin that we need to use. On this case, we’ll use the “text-davinci-003” mannequin.
• immediate: The textual content that we would like the mannequin to generate a response for. On this case, we would like the mannequin to generate a touch upon the tweet.
• max_tokens: The utmost variety of tokens (phrases) that we would like the mannequin to generate within the response.
• temperature: The “creativity” of the mannequin. A worth of 0.5 implies that the mannequin will generate responses which might be extra balanced between artistic and predictable.
• top_p: The likelihood of the mannequin selecting the most definitely response. A worth of 1 implies that the mannequin will at all times select the most definitely response.

We additionally must set the Content material-Kind header to software/json and embody our OpenAI API key within the Authorization header.

Right here is the code that accomplishes this:

const  information: response  = await axios.put up(
"https://api.openai.com/v1/completions",

mannequin: "text-davinci-003",
immediate: `Touch upon this tweet: "$tweet.textual content", the reply to this tweet should be like i'm writing it and in addition embody some emoji that matches the generated textual content`,
max_tokens: 70,
temperature: 0.5,
top_p: 1,
,

headers: 
"Content material-Kind": "software/json",
Authorization: `Bearer $openaiApiKey`,
,

);

Step 6: Submit the generated remark as a reply to the tweet

We use the api.put up() technique to put up a brand new tweet in reply to the unique tweet that we’re commenting on. We move within the standing parameter which incorporates the textual content of the tweet that we need to put up.

On this case, we need to put up a reply to the unique tweet, so we embody the @ image adopted by the screen_name of the consumer who posted the unique tweet. We additionally embody the in_reply_to_status_id parameter, which specifies the ID of the unique tweet that we’re replying to.

Right here is the code that does this:

const  information: postResponse  = await api.put up("statuses/replace", 
standing: `@$tweet.consumer.screen_name $remark`,
in_reply_to_status_id: tweet.id_str,
);
console.log(`Remark posted: $postResponse.textual content`);

Step 7: Delay every iteration for half-hour

To make sure that we aren’t posting feedback too ceaselessly, we use the setTimeout() perform to delay every iteration of the loop by half-hour. That is achieved utilizing the next code:

await new Promise((resolve) => setTimeout(resolve, 30 * 60 * 1000));

Wrapping up:

Lastly, we name the searchAndComment perform once more on the finish of the loop to begin the method over. This can proceed indefinitely till the script is stopped.

And that’s it! With just some strains of code and the facility of the OpenAI API, we have been in a position to construct a Twitter bot that may seek for tweets containing a particular key phrase and put up distinctive, related feedback on them.

You possibly can customise the code to suit your particular wants, reminiscent of altering the key phrase or the frequency at which the bot posts feedback.

I hope this tutorial was useful and gave you a superb understanding of the way to construct a Twitter bot utilizing the Twitter API and the OpenAI API.

If you happen to’re all for trying out this mission, you will discover it on my GitHub web page. Be happy to go away a star for those who prefer it!

In case you have any questions or strategies, be at liberty to go away a remark beneath.

Pleased coding! 😄