AI Chatbot

This plugin adds an AI chatbot to your Next.js Supabase SaaS application.

This plugin adds an AI chatbot trained on your website content to your Next.js application built using the OpenAI API. It's a simple component that will be displayed on the bottom right of your website.

This documentation is for the Next.js Supabase version. Please make sure you're looking at the right documentation.

Using the Plugin


To install the plugin, you can use git subtrees from your original repository:

git subtree add --prefix plugins/chatbot chatbot --squash

After running this command, you will have the plugin in your repository at plugins/chatbot. Once pulled, you can apply any customization you need.

Using the CLI

If you're using the CLI, you can run the following command to install the plugin:

npx @makerkit/cli@latest plugins install

Follow the instructions to install the plugin.

Add the plugin as a workspace in your package.json

You can do so by adding the following to your package.json file:

{ "workspaces": [ "plugins/chatbot" ] }

Add it next to the other workspaces in your package.json file.

If the installation fails

Some users are not able to install using the GitHub SSH URL. If you're having issues with that:

  1. properly set up SSH access to GitHub with your SSH key
  2. use the HTTPS URL instead of the SSH URL

To use the HTTPS URL, you can run the following command:

git subtree add --prefix plugins/chatbot chatbot --squash

Add the paths alias to the TypeScript configuration

To make sure that the TypeScript compiler can find the plugin, you will need to add the following paths alias to your tsconfig.json file, in addition to the other paths aliases that you may have:

{ "compilerOptions": { "paths": { "~/plugins/*": [ "./plugins/*" ] } } }

Installing dependencies

To install the dependencies, you can run the following command:

npm i

NPM will install the dependencies in the plugins/chatbot folder as an NPM workspace.

Configuring the database migration

This plugin needs to store the documents that the AI will be trained on as vectors in the Supabase database. As such, we need to add a migration to the database.

The migration SQL file is available at plugins/chatbot/migration.sql. You can generate a new mutation using the following command:

supabase migration new chatbot

Make sure you're happy with the default table names before proceeding so that they don't conflict with your existing tables.

Now paste the content of the plugins/chatbot/migration.sql file into the new migration file that was created.

When going to production, you will need to run the migration using the Supabase CLI:

supabase db push

Importing the Plugin

You can import the Chatbot component from the plugin in your app/layout.tsx file if you want it available on all pages:

import dynamic from 'next/dynamic'; const ChatBot = dynamic(() => import('~/plugins/chatbot/components/ChatBot')); export default function RootLayout() { return ( <> <ChatBot /> {/* ... */} </> ); }

Alternatively, you can import the Chatbot component in the layout of the page where you want it to appear. If you only want it available in the website pages, then you will import it in the app/(site)/layout.tsx layout.

Initial Prompts

You can pass an array of strings to the defaultPrompts prop of the component:

const DEFAULT_CHAT_PROMPTS = [ `Can you tell me more about ${}?`, `What is the price of ${}?`, `How can I contact you?`, `I want to share some feedback`, ]; // in the component <Chatbot defaultPrompts={DEFAULT_CHAT_PROMPTS} />

Configuring the plugin

To configure the plugin, add the following environment variables to your .env.local files:

  • OPENAI_API_KEY - Your OpenAI API key. You can get one from your OpenAI dashboard.
  • NEXT_PUBLIC_CHATBOT_FALLBACK_URL - The URL of the fallback chatbot. This is the link the chatbot will display if it can't answer the question. You can use an email such as or a link to the customer service page of your website.

When going to production, you will need to add these environment variables from your provider dashboard.

Setting the API Route Handler

CSRF exemption

Open the file src/app/middleware.ts and add the route /api/chat to the CSRF exemption list:

export const config = { matcher: [ '/((?!_next/static|_next/image|favicon.ico|locales|assets|api/chat|api/stripe/webhook).*)', ], };

Next.js Route

Create a route at app/api/chat/route.ts with the following content:

import handleChatBotRequest from '~/plugins/chatbot/lib/server/route-handler'; export const runtime = 'edge'; export const POST = handleChatBotRequest;

Indexing the content's embeddings

By default, the AI will be trained on the content of your website available within your documentation pages at /docs.

In addition, you can provide a list of questions and answers.

To do so, add MDX files at plugins/chatbot/questions/<filename>.mdx file and add content with the following format:

--- question: "<question>" --- <answer>

For example, you can create a file at plugins/chatbot/questions/refund-policy.mdx:

--- question: "What is your refund policy?" --- We offer a 30-day money-back guarantee. If you're not happy with our product, we will refund you.

To generate the embeddings, you can run the following command:

npx tsx plugins/chatbot/cli.ts generate

Follow the instructions to generate the embeddings.

If you are generating the embeddings for your production environment, you will need to provide the Supabase credentials in the .env.production file.

Captcha Protection (optional) (WIP)

To protect your chatbot from bots (ha ha!), you can add a captcha challenge using Cloudflare's Turnstile service.

To do so, you will need to add the following environment variables to your environment files:


[You can get the site key from your Turnstile dashboard](https://developers.

On the server side, you will need to add the following environment variables: CHATBOT_TURNSTILE_SECRET_KEY.

You can also get the secret key from your Turnstile dashboard. Since this is a secret key - avoid adding it to your repository. Please use the environment variables of your provider to add it to your production environment.

De-duplicating indexing

To avoid indexing the same content multiple times, the CLI generates a file named plugins/chatbot/indexed-files.json that contains a list of all the pages that have been indexed and the SHA256 hash of their content.

If the content of the file changes, the CLI will re-index the pages.

Keeping the plugin up to date

To keep the plugin up to date, you can use git subtrees again:

git subtree pull --prefix plugins/chatbot chatbot --squash

Best Practices

As you may know - the AI is only as good as the data it's trained on. As such, make sure to provide as much content as possible to the AI. I don't mean dozens of pages - but hundreds. Seriously, the more content you provide

  • the more useful the AI will be. Without context, hallucinations are bound to happen - and you don't want that.

Provide a fallback email

If the AI can't answer the question, it will display a fallback URL. Make sure to provide a fallback URL that will allow your users to contact you in case the AI can't answer their question.

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