In this guide, we will walk through the steps required to create an engaging ChatGPT chatbot for your website using LangChain, Supabase, TypeScript, OpenAI, and Next.js. This combination not only boosts user engagement but also leverages powerful tools under the hood.

Getting Started

Before we dive into coding, let’s break down what we are working with:

• LangChain: A framework that simplifies building scalable AI and Language Model applications.
• Supabase: An open-source Postgres database ideal for storing embeddings with a pg vector extension.
• Next.js: A React framework that enables server-side rendering for faster page loads.

Step-by-Step Guide

1. Clone the Repository

Start by cloning the repository to your local environment:

git clone [github https url]

2. Install Necessary Packages

To ensure all dependencies are in place, run the following command:

pnpm install

3. Set Up Your .env File

Next, you’ll need to set up your environment variables:

• Copy .env.local.example to .env
• Your .env file should include:
• OPENAI_API_KEY= (Get your API key from OpenAI)
• NEXT_PUBLIC_SUPABASE_URL=
• NEXT_PUBLIC_SUPABASE_ANON_KEY=
• SUPABASE_SERVICE_ROLE_KEY=

4. Configure the URLs

Open the config folder and replace the URLs in the array with your website URLs. Ensure that you include more than one URL.

5. Modify the Web Loader

In the utils/custom_web_loader.ts, update the load function to extract specific elements from a webpage:

async load(): Promise {
    const $ = await this.scrape();
    const text = $(body).text();
    const metadata = { source: this.webPath };
    return [new Document({ pageContent: text, metadata })];
}

This structure will ensure that the scraped contents and their respective metadata are stored in your Supabase database table.

6. Run the SQL Schema

Copy the contents of schema.sql into your Supabase SQL editor to set up the necessary tables and functions. Verify that the documents table exists and contains the match_documents function.

7. Scraping and Embedding Data

To execute the scraping and embedding script, run:

npm run scrape-embed

This script will visit all specified URLs and extract data as configured, converting it into vectors using OpenAI’s Embeddings(text-embedding-ada-002).

8. Running Your App

Once you’ve confirmed that the data is correctly stored in your Supabase table, start your application by running:

npm run dev

Now, you can interact with your chatbot by typing questions directly.

Troubleshooting

If you encounter issues during setup or execution, consider the following troubleshooting steps:

• Ensure all API keys are correctly set in your .env file.
• Double-check the Supabase database connection and that the required tables exist.
• Verify that your URLs in the config folder are correctly formatted.
• Check the validity of your embedding script by logging any errors using console.log.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

And there you have it! Your own ChatGPT chatbot is up and running on your website. Happy chatting!