Build an AI Chatbot with ChatGPT & OpenAI API

Table of Contents:

1. Introduction
2. Setting Up the Project
3. Implementing the API Service
4. Configuring the UI
5. Handling User Input
6. Fetching and Displaying Chat Responses
7. Implementing a Custom Table View Cell
8. Conclusion

Introduction

In this Tutorial, we will explore how to use the Open AI API to create a chatbot using GPT-3. We will build upon a previous project and reuse some of the code. The purpose of this chatbot is to provide responses based on user input using the power of artificial intelligence.

Setting Up the Project

To get started, we need to set up our project. We will need to download the necessary files from GitHub and import them into our project. We will also need to obtain our API key from the Open AI Website. Once everything is set up, we can proceed to the next steps.

Implementing the API Service

In this section, we will create an API service class that will handle the communication with the Open AI API. We will create functions for sending prompts and fetching chat responses. We will also handle different types of requests depending on whether we are using Dolly 2 or GPT-3. By making our API service flexible, we can easily modify the code in the future if needed.

Configuring the UI

Next, we will configure the user interface for our chatbot. We will create a label to display the Prompt, a text field for user input, and a table view to display the chat conversation. We will use auto layout to position and size our UI elements correctly. We will also register a custom table view cell for displaying the chat messages.

Handling User Input

In this section, we will implement the functionality to handle user input. When the user taps the submit button, we will retrieve the text from the text field and pass it to the API service to fetch a chat response. We will also resign the text field as the first responder to dismiss the keyboard. We will add error handling to handle any issues that may arise during the API request.

Fetching and Displaying Chat Responses

Once we fetch the chat response from the API service, we will update the UI to display the response in the table view. We will store the chat messages in an array and reload the table view data whenever a new message is received. We will also handle the case where the response exceeds the maximum token limit by truncating the response and displaying a notification to the user.

Implementing a Custom Table View Cell

To enhance the appearance of the chat conversation, we will implement a custom table view cell. This cell will allow us to distinguish between the user's messages and the chatbot's responses. We will use the modulo operator to alternate the styling of the cells and make the conversation more visually appealing.

Conclusion

In this tutorial, we have learned how to build a chatbot using the Open AI API and GPT-3. We have covered the steps to set up the project, implement the API service, configure the UI, handle user input, fetch and display chat responses, and create a custom table view cell. By following this tutorial, you should now have a fully functional chatbot that can provide responses based on user input.

Highlights

• Learn how to use the Open AI API to create a chatbot
• Reuse code from a previous project to speed up development
• Implement an API service class to handle communication with the API
• Configure the user interface for the chatbot using a label, text field, and table view
• Handle user input and fetch chat responses from the API
• Display chat responses in a table view, with custom styling for user and chatbot messages
• Create a visually appealing chat conversation using a custom table view cell

Frequently Asked Questions:

Q: How do I obtain an API key for the Open AI API? A: To obtain an API key, you can visit the Open AI website and sign up for an account. Once you have an account, you can generate an API key from the developer dashboard.

Q: Can I use this chatbot for commercial purposes? A: The usage of the Open AI API, including the chatbot created in this tutorial, may be subject to certain restrictions and licensing agreements. It is important to review and comply with the terms and conditions set forth by Open AI when using their API for commercial purposes.

Q: Can I customize the appearance of the chatbot? A: Yes, you can customize the appearance of the chatbot by modifying the UI elements and adding your own styling. This tutorial provides a basic implementation, but you can extend and modify it according to your own design preferences.

Q: How can I handle errors during the API request? A: In the event of an error during the API request, you can use error handling techniques such as try-catch blocks to gracefully handle the error and provide appropriate feedback to the user. The API service class in this tutorial already includes error handling for fetching chat responses.

Q: Is there a limit to the number of tokens in a chat response? A: Yes, there is a limit to the number of tokens in a chat response, which can vary depending on the specific model being used. It is important to be aware of this limit and adjust the maximum tokens parameter accordingly to ensure that the entire response is captured.