Create an AI Chatbot with Next.js and OpenAI

Create an AI Chatbot with Next.js and OpenAI

Table of Contents:

1. Introduction
2. Building Your Own Chat GPT
3. Pre-processing the Knowledge Base
4. Generating Embeddings with OpenAI
5. Performing Similarity Search with PG Vector
6. Runtime: Creating Embeddings for User Questions
7. Selecting the Most Relevant Content
8. Injecting Context into OpenAI GPT-3
9. Streaming the Response Back to the Client
10. Using the OpenAI Doc Search Starter
11. Deploying the Chat GPT Demo to Vercel
12. Pushing Local Migrations to the Superbase Remote Database
13. Conclusion

Article:

Introduction

In this article, we will explore how to build your very own chat GPT style talk search using Superbase. We have been testing this feature within the Superbase documentation for some time now, and we're excited to Show you how it works.

Building Your Own Chat GPT

To build your own chat GPT, you can follow the tutorial provided in the Superbase documentation. This tutorial assumes that you have a Next.js project with a collection of MDX files nested inside your Pages directory. These MDX files will be indexed, and embeddings will be generated for them.

Pre-processing the Knowledge Base

To begin building your chat GPT, you need to pre-process your knowledge base, which consists of the MDX files in your Pages folder. This step is necessary to generate embeddings for the content.

Generating Embeddings with OpenAI

To generate embeddings for your MDX files, you can use the OpenAI Doc Search Starter. This starter allows you to deploy your project straight away to Vercel, using the Versal integration. Additionally, you will need to set up the database schema, which requires the use of PG Vector in PostgreSQL for vector similarity search.

Performing Similarity Search with PG Vector

Once your embeddings are generated, you can perform similarity search using PG Vector. This allows you to find the most similar content Based on a user's search query. PG Vector is an open-source extension for PostgreSQL that specializes in vector similarity search.

Runtime: Creating Embeddings for User Questions

At runtime, when a user asks a question, You need to Create an embedding for that specific question. This embedding will be used to find the most appropriate content in your database for the given query.

Selecting the Most Relevant Content

After performing the vector similarity search between the question and the content embeddings, you will need to select the most relevant content. This selection ensures that the context provided to the OpenAI GPT-3 model is accurate and aligned with the user's query.

Injecting Context into OpenAI GPT-3

To create a chat-like experience, you need to inject the selected context into the OpenAI GPT-3 text completion prompt. This prompt will be used to generate a response to the user's question.

Streaming the Response Back to the Client

Once the response is generated, it is streamed back to the client. This allows for real-time interaction and a seamless user experience. The response can be displayed to the user, providing them with the information they requested.

Using the OpenAI Doc Search Starter

The OpenAI Doc Search Starter is a convenient tool for building your chat GPT. It provides templates and pre-built functionality that can be easily customized to suit your needs.

Deploying the Chat GPT Demo to Vercel

To deploy your chat GPT demo to Vercel, you can follow the steps outlined in the documentation. This deployment process ensures that your application is up and running in a live environment.

Pushing Local Migrations to the Superbase Remote Database

If you prefer not to use the Vercel integration, you can push your local migrations to the Superbase remote database. This allows you to set up the necessary tables and schema for your project.

Conclusion

In conclusion, building your own chat GPT-style talk search can be an exciting and rewarding project. With the help of Superbase and OpenAI, you can create a personalized and interactive experience for your users. By following the steps outlined in this article, you will be well on your way to building your own chat GPT. So why wait? Start building your chat GPT today and unlock the power of conversational AI.

Highlights:

• Build your own chat GPT-style talk search using Superbase and OpenAI
• Pre-process your knowledge base and generate embeddings for your content
• Perform similarity search using PG Vector in PostgreSQL
• Select the most relevant content and inject context into OpenAI GPT-3
• Stream the response back to the client for a seamless user experience

FAQ:

Q: Can I use Superbase and OpenAI to build a chat-like experience? A: Yes, by following the steps outlined in this article, you can create a chat GPT-style talk search using Superbase and OpenAI.

Q: How do I deploy my chat GPT demo to Vercel? A: The article provides instructions on how to deploy your chat GPT demo to Vercel. Simply follow the steps outlined in the documentation.

Q: Is Superbase a good choice for building AI apps? A: Yes, Superbase provides an open-source ecosystem that is rapidly developing. With the templates and functionality offered, building AI apps with Superbase is a great choice.