Create Custom Chatbot Using ChatGPT and Supabase

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Tripo AI

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Table of Contents

1. Introduction
2. Building a Q&A Chatbot API
3. Storing Custom Data
4. Scaling with Superbase Functions
5. Connecting to OpenAI
6. Understanding Embeddings
7. Creating Postgresql Table
8. Getting Relevant Embeddings
9. Turning Data into Embeddings
10. Storing Custom Data in the Database
11. Creating Edge Functions
12. Initialize Superbase Client
13. Generating Embeddings
14. Client-side Custom Data
15. Formatting Documents for Prompt
16. Creating the Prompt
17. Getting Response from ChatGPT
18. Returning the Response
19. Deploying the Endpoint
20. Testing the API

Building a Q&A Chatbot API with Custom Data

In this tutorial, we will explore how to build a Q&A chatbot API that allows us to use our own custom data. Whether You're creating a customer service agent or building a chatbot for your own data, this tutorial will cover everything you need to know. We will start by building a documents database to store our embeddings. Using Superbase Functions, we will be able to Scale our API infinitely. Additionally, we will connect to the OpenAI API in the backend.

Introduction

Welcome to this tutorial where we will be exploring the process of building a Q&A chatbot API. This project will allow us to utilize our own custom data when creating chatbots. Whether you are looking to build a customer service agent or have a specific use case in mind, this tutorial will provide you with all the necessary knowledge to get started.

Building a Q&A Chatbot API

The first step in building our Q&A chatbot API is to set up the necessary infrastructure. We will be using Superbase Functions, which provide us with a highly scalable API endpoint. This endpoint will allow users to Interact with our chatbot and retrieve answers to their questions.

Storing Custom Data

To provide more accurate responses, we will be utilizing our own custom data. This data will be stored in a documents database, where it can be easily accessed and transformed into embeddings. We will use Postgresql for our database and Create a table to store our documents and their corresponding embeddings.

Scaling with Superbase Functions

Superbase Functions allow us to scale our API infinitely. By leveraging Superbase's serverless infrastructure, we can handle any amount of traffic without worrying about performance issues. This scalability is essential for ensuring a smooth user experience, even during peak periods of activity.

Connecting to OpenAI

To enhance the intelligence of our chatbot, we will connect it to the OpenAI API. This integration will enable us to leverage the power of OpenAI's language model to generate more accurate and contextually relevant responses. We will utilize the DaVinci model, which is well-suited for natural language understanding and generation.

Understanding Embeddings

Embeddings play a crucial role in our chatbot's ability to understand and compare different pieces of text. They represent the semantic meaning of words and sentences and allow us to measure their similarity. By understanding how embeddings work, we can ensure that our chatbot provides accurate and relevant responses to user queries.

Creating Postgresql Table

To store our embeddings, we will create a Postgresql table within our Superbase database. This table will contain the necessary fields to store both the content and the embedding for each document. We will use Superbase's SQL editor to create the table and set up the appropriate data structure.

Getting Relevant Embeddings

In order to retrieve the most relevant embeddings for a given query, we will create a function called "match_documents". This function will compare the query with the documents in our database and return a list of the most relevant documents. By leveraging Superbase's RPC functionality, we can perform efficient searches and retrieve the desired results.

Turning Data into Embeddings

To utilize our custom data in the chatbot, we need to convert it into embeddings. We will use the OpenAI embedding model to transform our text into a unique vector representation. By passing our data through this model, we will obtain embeddings that capture the key features and Context of the text.

Storing Custom Data in the Database

Once we have converted our custom data into embeddings, we can store them in our Superbase database. We will utilize the Superbase client to insert the content and embedding of each document into the corresponding table. This ensures that our chatbot has access to the necessary data when generating responses.

Creating Edge Functions

To distribute our chatbot API globally, we will utilize Superbase Edge functions. These functions allow us to deploy our API endpoints closer to the users, resulting in faster response times and improved overall performance. We will create an Edge function for our "ask_custom_data" endpoint, which will contain all the logic for generating responses Based on the user's queries.

Initialize Superbase Client

Before we can interact with our Superbase database and perform various operations, we need to initialize the Superbase client. This client will establish a connection to our Superbase project and allow us to execute queries and access the necessary data.

Generating Embeddings

To generate embeddings for user queries, we will create a JavaScript function called "generate_embeddings". This function will take in the query text and convert it into an embedding using the OpenAI embedding model. We will then store the embedding and corresponding text in our Superbase database for future reference.

Client-side Custom Data

To provide relevant responses, we need to format our custom data in a way that is compatible with the chatbot's prompt. This involves looping through the relevant documents, retrieving their content, and encoding it using the GPT-3 tokenizer. By concatenating these encoded documents, we can create a single context text that captures the necessary information for generating responses.

Formatting Documents for Prompt

To create the prompt for our chatbot, we will concatenate the system statement, the relevant documents, and the user's query. This will ensure that the chatbot has access to all the necessary information to provide accurate responses. By formatting the prompt correctly, we can improve the overall effectiveness of our chatbot.

Creating the Prompt

The prompt represents the input given to the GPT-3 model and consists of the system statement, the relevant documents, and the user's query. By concatenating and formatting these components correctly, we can ensure that the chatbot provides accurate responses based on the context provided. The prompt serves as a guide for the model's generation process.

Getting Response from ChatGPT

Once we have constructed the prompt, we can pass it to the GPT-3 model using the OpenAI API. The model will generate a response based on the information provided in the prompt and its understanding of the context. We will utilize the text DaVinci 003 model, which is capable of generating high-quality, context-aware responses.

Returning the Response

After receiving the response from the GPT-3 model, we need to format it and return it to the user. This involves extracting the relevant data from the completion response and packaging it in a response object that adheres to the API specifications. By providing a clear and structured response, we ensure a seamless user experience.

Deploying the Endpoint

To make our API accessible to users, we need to deploy the endpoint. Using the Superbase CLI, we can easily deploy our functions and make them available for consumption. This step finalizes the setup process and prepares our chatbot API for usage.

Testing the API

With our endpoint deployed, we can now test the functionality of our chatbot API. By calling the "ask_question" function and providing a query, we can simulate a user interaction and observe the response generated by the chatbot. This allows us to evaluate the effectiveness of our implementation and make any necessary adjustments.

Highlights

• Build a Q&A chatbot API using custom data
• Utilize Superbase Functions to scale the API infinitely
• Connect to the OpenAI GPT-3 model for language generation
• Store custom data in a Postgresql database
• Generate embeddings to compare and analyze text
• Format and structure data for chatbot Prompts
• Deploy the API endpoint using Superbase Edge functions
• Test and evaluate the chatbot's functionality

FAQs

Q: Can I use any language model with this chatbot API? A: Yes, you can use different language models based on your requirements. In this tutorial, we utilized the OpenAI GPT-3 model for language generation.

Q: How can I add more custom data to the chatbot API? A: To add more custom data, you can follow the steps outlined in the tutorial. Simply create new documents with their corresponding embeddings and store them in the database.

Q: Is it possible to modify the chatbot's response style? A: Yes, you can modify the response style by adjusting the temperature parameter when generating responses with the GPT-3 model. Higher values result in more random and creative responses, while lower values produce more focused and deterministic responses.

Q: Can I integrate this chatbot API with other applications? A: Yes, you can integrate the chatbot API with various applications, such as customer service platforms, recommendation systems, or chat interfaces on websites. The API allows you to provide dynamic and context-aware responses based on the user's queries.