AI Chatbot

This plugin adds an AI chatbot to your Next.js Firebase 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 Firebase 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.

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 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. Please don't miss this step - otherwise, the plugin will install duplicate node modules and they will conflict with each other.

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/*" ] } } }

Update TailwindCSS configuration

To make sure that the plugin's styles are applied, you will need to update your TailwindCSS configuration.

Add the plugins path to the content array of your tailwind.config.js file:

module.exports = { // ... content: [ // ... './plugins/**/*.tsx', ], // ... }

This is only needed the first time you install any plugin.

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.

Importing the Plugin

You can import the Chatbot component from where you want to display it.

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

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

We will use the App Directory to create a route handler for the chatbot - so we can leverage streaming.

Next.js Route

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

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

NB: make sure you're using the new App Directory (located at src/app) and not the Pages Directory (located at src/pages). This is because the Pages Directory does not support streaming.

Feel free to change the route to whatever you want - but make sure to update the route in the ChatBot component.

Creating a Vector Store with LangChain

Given the variety of possible Databases - we don't provide a database vector store by default. Instead, we use LangChain - which provides an exhaustive list of Vector Store integrations out of the box.

Therefore - you will need to choose a Vector Store based on the technology you want to use (Pinecone, Supabase, Chroma, TypeSense, etc.) and follow the Vector Store integration instructions.

Once you've decided which Vector Store you want to use, export a default vector store from ~/plugins/chatbot/lib/server/vector-store.ts:

Example: Using Typesense as a Vector Store

For example, if you want to use TypeSense, you can create the following vector store using the typesense client and the Langchain integration:

import { Typesense, TypesenseConfig } from 'langchain/vectorstores/typesense'; import { OpenAIEmbeddings } from 'langchain/embeddings/openai'; import { Client } from 'typesense'; const TYPESENSE_URL = process.env.TYPESENSE_URL; const TYPESENSE_HOST = process.env.TYPESENSE_HOST || 'localhost'; const TYPESENSE_PORT = process.env.TYPESENSE_PORT || 8108; const TYPESENSE_API_KEY = process.env.TYPESENSE_API_KEY || 'xyz'; const TYPESENSE_PROTOCOL = TYPESENSE_HOST.includes('localhost') ? 'http' : 'https'; const nodes = TYPESENSE_URL ? [ { url: TYPESENSE_URL, }, ] : [ { host: TYPESENSE_HOST, port: Number(TYPESENSE_PORT), protocol: TYPESENSE_PROTOCOL, }, ]; const typeSense = new Client({ nodes, apiKey: TYPESENSE_API_KEY, numRetries: 2, connectionTimeoutSeconds: 5, }); const TYPESENSE_SCHEMA_NAME = process.env.TYPESENSE_SCHEMA_NAME || 'chatbot_documents'; const typesenseVectorStoreConfig: TypesenseConfig = { typesenseClient, schemaName: TYPESENSE_SCHEMA_NAME, onFailedAttempt: (error) => { console.error(error); }, import: async (data, collectionName) => { await typesenseClient .collections(collectionName) .documents() .import(data, { action: 'emplace', dirty_values: 'drop' }); }, } satisfies TypesenseConfig; export default function getVectorStore() { return new Typesense( new OpenAIEmbeddings({ openAIApiKey: process.env.OPENAI_API_KEY, }), typesenseVectorStoreConfig, ); }

Don't forget to install the required dependencies for your Vector Store.

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 Vector DB 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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