Hello XR Developers! In today’s tutorial, we’re diving into how to integrate Meta’s Voice SDK with Large Language Models (LLMs) like Meta’s Llama, using Amazon Bedrock. This setup will allow you to send voice commands, process them through LLMs, and receive spoken responses, creating natural conversations with NPCs or assistants in your Unity game.

Let’s break down the process step-by-step!

Setting Up Amazon Bedrock

1. Accessing Amazon Bedrock:
   • Visit Amazon Bedrock and sign in to your console. If you don’t have an account, register a new one.
   • Navigate to your account name and select Security Credentials. Here, create an Access Key and a Secret Access Key under “Access Key”.
2. Request Model Access:
   • In your Bedrock console, go to Bedrock Configurations > Model Access.
   • Select Meta’s Llama 3 (or any other model) and click on “Request model access”.
   • Depending on your region, you might need to select the closest server that hosts your desired model, e.g., London for Llama 3 or Oregon for Llama 3.1.
3. Check Pricing:
   • Be aware of the pricing for different models by visiting Amazon Bedrock Pricing.

Setting Up Unity for LLM Integration

1. Install NuGet for Unity:
   • Download the latest NuGet for Unity package from GitHub.
   • Drag the downloaded package into your Unity project. This adds a “NuGet” menu to your Unity Editor.
2. Install Required Packages:
   • If using Unity 2022.3.20 or later, ensure that Newtonsoft.Json is installed via the Unity Package Manager.
   • Install the Amazon Bedrock Runtime package via the NuGet package manager.
3. Create Amazon Bedrock Connection Script:
   • Create a new script called AmazonBedrockConnection.
   • Utilize the Amazon and Amazon Bedrock namespaces to manage AWS credentials and interact with Bedrock.

Developing the Interaction Logic

1. Set Up AWS Credentials:
   • In your AmazonBedrockConnection class, define fields for accessKeyId and secretAccessKey.
   • Set these values in Unity’s inspector for easy access and management.
2. Build the UI:
   • Create two text fields for displaying the prompt and response, an input field for typing prompts, and a button to send the prompt.
   • Reference Meta’s Voice SDK text-to-speech (TTS) component to speak the AI’s response.
3. Integrate AI Prompt Logic:
   • Create a SendPrompt method to handle the core functionality:
     • Update the UI with the user’s input.
     • Format the input for the AI model and send it using InvokeModelAsync.
     • Process the AI’s response and display it in the UI.
     • Use the TTS component to vocalize the response.
4. Configure Unity Scene:
   • Add the AmazonBedrockConnection script to an empty GameObject.
   • Input your AWS credentials directly from the console.
   • Set up the UI elements (text fields, input field, button).
   • Add a TTS Speaker by navigating to Assets > Create > Voice SDK > TTS > Add TTS Speaker to scene.
   • Customize the speaker’s voice if desired.

Combining with Wake Word Detection

1. Enhance the Voice Manager:
   • Open your Voice Manager script from the previous tutorial.
   • Add a reference to the AmazonBedrockConnection script.
   • After receiving the full transcription, pass it as a prompt to the SendPrompt method.
2. Test the Integration:
   • Reference the AmazonBedrockConnection script in your Voice Manager within Unity.
   • Test the entire workflow: use a wake word, speak a command, and receive a response from the AI model.

Final Thoughts

Congratulations! You’ve successfully set up a system where you can speak commands, process them through an LLM like Meta’s Llama, and have the response spoken back to you. This powerful combination opens up exciting possibilities for creating interactive experiences in your Unity projects.

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