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Project Requirements Document: Virtual AI Assistant

1. Introduction

This document outlines the requirements for a virtual AI assistant, a sophisticated software application designed to interact with users through natural language, perform tasks on a computer, and learn from its interactions. The assistant will be a standalone executable that runs on a user's computer, providing a seamless and intuitive user experience.

2. Vision and Goals

The primary goal of this project is to create a powerful and user-friendly virtual AI assistant that can:

  • Understand and respond to natural language: The assistant will be able to understand and respond to user commands and questions in a conversational manner.
  • Perform tasks on the computer: The assistant will be able to execute commands, run programs, and interact with the operating system.
  • Learn and adapt: The assistant will learn from its interactions with the user, becoming more knowledgeable and personalized over time.
  • Provide a natural and intuitive user experience: The assistant will be easy to use and interact with, providing a seamless and enjoyable user experience.

3. Functional Requirements

3.1. Core Brain

  • Knowledge Graph: The assistant will use a knowledge graph (Elephant Memory) to store and retrieve information about the world, the user, and its own experiences.
  • Large Language Model (LLM): The assistant will use a powerful LLM (Gemma 3) for natural language understanding, reasoning, and response generation.
  • Thinking Process: The assistant will have a sophisticated thinking process that involves querying the knowledge graph, formulating prompts for the LLM, and processing the LLM's responses.

3.2. Perception

  • Continuous Voice Monitoring: The assistant will continuously listen to the audio stream from the microphone.
  • Voice Activity Detection (VAD): The assistant will be able to detect when a user is speaking.
  • Speaker Diarization: The assistant will be able to identify when it is being spoken to, distinguishing between user commands and other speech.
  • Speech-to-Text: The assistant will be able to transcribe spoken language into text.
  • Text Input: The assistant will provide a command-line interface for text-based input.

3.3. Action

  • Text-to-Speech (TTS): The assistant will be able to convert text into speech, allowing it to speak its responses to the user.
  • Command Execution: The assistant will be able to execute shell commands to perform tasks on the computer.
  • GUI (Optional): The assistant may have a simple GUI to display its status, thoughts, and other information.

4. Non-Functional Requirements

  • Performance: The assistant should be responsive and perform its tasks in a timely manner.
  • Reliability: The assistant should be reliable and available when the user needs it.
  • Usability: The assistant should be easy to use and interact with.
  • Extensibility: The assistant should be designed in a modular and extensible way, allowing for new features and capabilities to be added in the future.
  • Cross-Platform Support: The assistant should be designed to run on multiple operating systems (e.g., Windows, macOS, Linux).

5. Technical Specifications

  • Programming Language: C++
  • Core Libraries:
    • Knowledge Graph: Elephant Memory
    • LLM Integration: Ollama or llama.cpp
    • Audio Processing: PortAudio or RtAudio
    • Speech-to-Text: Vosk or a similar library
    • Text-to-Speech: A suitable TTS library (e.g., eSpeak, Flite)
  • Build System: CMake

6. Roadmap

The project will be developed in the following phases:

  1. Phase 1: Core Brain: Implement the core brain, including the knowledge graph and LLM integration.
  2. Phase 2: Perception: Implement the perception system, including continuous voice monitoring, VAD, speaker diarization, and speech-to-text.
  3. Phase 3: Action: Implement the action system, including TTS and command execution.
  4. Phase 4: Integration and Testing: Integrate all the components and perform thorough testing.
  5. Phase 5: Deployment: Package the assistant as a standalone executable and deploy it to users.