Building a Real-time Chatbot with GPT-3 and Unreal Engine

Building a Real-time Chatbot with GPT-3 and Unreal Engine

Table of Contents

1. Introduction
2. Moving and Working
3. Modifying Code for Chat GPT3 Integration
4. Setting Up the Server
5. Connecting with Chat Clients
6. Interacting with Jabe
7. Exploring AWS Poly and Speech Components
8. Understanding the Python Code
9. Using Socket.io and Chat Utils
10. Power of Socket.io for Command and Action

Introduction

In this article, we will discuss the integration of Amazon Poly and the Metahumans project with Chat GPT3. We will explore the modifications made to the code and how to set up the server to communicate with different chat clients. Additionally, we'll Delve into the functionalities of the speech components and the AWS Poly SDK. Finally, we'll examine the Python code and how to utilize Socket.io and chat utilities to enable command and action interactivity.

Moving and Working

I've been busy moving and focusing on my day job, but I managed to work on some code that integrates Amazon Poly and the Metahumans project with Chat GPT3. While there are still some details to sort out, I wanted to share my progress and get a video out to showcase the functionality.

Modifying Code for Chat GPT3 Integration

To make the code compatible with Chat GPT3, I made various modifications. These changes enable anyone to easily download and run the source code. While it may take some time to set up due to the intricacies involved, let's jump right into it!

Setting Up the Server

To begin, we need to run Socket.IO as Python. Alongside, we will have a server running, which will act as the central communication point for all clients. To get started, ensure that You have the necessary folders and clients set up in the source code. Getting the server up and running is paramount before progressing further.

Connecting with Chat Clients

With the server successfully running, all communication will take place through it. Clients will connect to the server and send and receive messages accordingly. This central interaction point serves as the backbone of the entire system.

Interacting with Jabe

Jabe, our virtual assistant, will be the primary entity we Interact with. Once the server is up and running, Jabe will respond to your messages and queries. So, let's turn on the system and start conversing with Jabe. Feel free to ask Jabe anything you'd like or initiate a conversation on any topic.

Exploring AWS Poly and Speech Components

AWS Poly is a crucial component of this integration. By leveraging AWS's text-to-speech engine, we can convert text into spoken words. In this section, we'll delve into the intricacies of AWS Poly and how it interacts with the other speech components.

Understanding the Python Code

To integrate GPT3 functionality, we need to work with Python code. I've sourced examples of integrating GPT3 and modified the existing code to suit our requirements. In this section, we'll walk through the Python code and understand how it functions within our system.

Using Socket.io and Chat Utils

Socket.io and chat utilities play a vital role in enabling communication between the server and clients. By using Socket.io's features and chat utilities, we can parse and process incoming chat messages, extract commands and parameters, and perform appropriate actions.

Power of Socket.io for Command and Action

By utilizing Socket.io, we can build a command and action engine that allows for synchronized communication. We can send commands to the server, which then instructs Ada (our virtual assistant) to perform specific actions such as blinking, looking around, or playing animations. Socket.io's power lies in its ability to synchronize multiple actions seamlessly.

Conclusion

In conclusion, we have explored the integration of Amazon Poly, the Metahumans project, and Chat GPT3. We have discussed the modifications made to the code, examined the server setup, and explored the AWS Poly and speech components. Additionally, we have understood the Python code used and how Socket.io and chat utilities enable command and action functionality. With the information provided, you can now begin working on implementing this integration in your own environment.

Highlights

• Integration of Amazon Poly and the Metahumans project with Chat GPT3
• Modifying code for seamless compatibility
• Setting up the server for communication
• Connecting with chat clients and interacting with Jabe
• Exploring AWS Poly and speech components
• Understanding the Python code and utilizing Socket.io and chat utilities

FAQs

Q: Can I run this integration on a web page? A: While it is possible to host this integration on a web page, it requires additional setup and configuration. However, the provided code and instructions serve as a starting point for implementation.

Q: Do I need an AWS account for this integration? A: Yes, an AWS account is necessary to leverage the AWS Poly functionality. Ensure that you have the required credentials and access to AWS services.

Q: Can I use an alternative speech-to-text service instead of Google or AWS? A: Yes, the implementation allows for flexibility in choosing the speech-to-text service. However, the provided code is tailored for integration with Google and AWS.

Q: How can I contribute or provide feedback for this project? A: Contributions and feedback are always welcome! You can follow the provided GitHub link to contribute, report issues, or suggest improvements.

Q: Are there any costs associated with using GPT3 or AWS Poly? A: Both GPT3 and AWS Poly come with their respective pricing. Ensure that you review the pricing details on the OpenAI and AWS websites before implementing the integration to understand any potential costs involved.