AI Assistant Development Lecture Notes

Introduction

• Presenter holds a can of Red Bull and a magazine with Spanish text "Las Florida Delo."
• Discusses building an AI assistant using microphone and webcam.
• Previous video received positive feedback.

Collaboration with LifeKit

• A company called LifeKit reached out to collaborate.
• LifeKit provides the platform for OpenAI's ChatGPT.
• The presenter rewrote the agent from scratch using LifeKit's platform.

Demonstration of AI Assistant

• The assistant interacts with visual cues (e.g., pointing at a light fixture).
• Example of using the assistant to recognize a card saying "Happy Father's Day."

Building the AI Assistant

Source Code Overview

• The source code consists of about 139 lines with extensive comments for clarity.
• Instructions in the README file must be followed:
  • Create a virtual environment
  • Install required libraries
  • Set up environment variables:
    • LifeKit API keys
    • DeepGram API key for voice-to-text conversion
    • OpenAI API key for GPT-4

Assistant Functionality

• The assistant will only access the webcam when necessary, to save bandwidth:
  • Chat interactions will typically be in text.
  • Images are sent only if required for a specific question.
  • Example scenarios where images are not needed (e.g., jokes, simple math).

Function Calling Mechanism

1. The assistant determines if an image is needed by evaluating user requests.
2. If so, it suggests a function call instead of answering directly.
3. This preserves network efficiency and speeds up interactions.

Class Structure and Functionality

• The class for the assistant leverages LifeKit's function context to manage requests and responses.
• Metadata such as user messages is stored for context.
• The assistant is designed to maintain conversational context through a chat system.

Key Components of the Code

Initialization

• The main entry point initializes the chat context and system message (personality settings).
• It sets up the necessary libraries and tracks (audio/video).

Event Handling

• Different events are captured:
  • Message Receive: Captures speech input converted to text.
  • Function Call Finish: Triggered when a function call completes, allowing for new requests.

Answer Function

• The answer function processes input messages and determines if an image should be included in the response.
• It manages communication with the LLM (Large Language Model) and plays audio responses.

Running the Assistant

• Instructions for running the assistant:
  • Use command python assistant.py start to initiate the program.
  • Connect to the LifeKit playground for real-time interaction.

Example Interactions

• Demonstrated interactions where the assistant can recognize objects and provide feedback based on webcam input.
• The assistant can respond to questions about visual prompts (e.g., colors, objects).

Conclusion

• Encouragement to like and subscribe for more content.
• Presentation ends with a humorous remark.