Shadow-AI

Shadow-AI is an AI engine that integrates a frontend controller (leveraging GKD) with a Large Language Model (LLM, primarily Claude 3.5). It enables users to control an Android mobile assistant to perform complex, purpose-specific operations through voice or text commands.

Recent Updates

• [2025 January 7] Initial release of Shadow-AI with core functionalities.

Contents

• Introduction
• Features
• Architecture
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Customization
  • Extending Functionality
  • Model Integration
• Roadmap
• Documentation
• Contribution
• License

Introduction

With the advancement of smartphones and the Internet of Things, computing and sensing devices have become ubiquitous, significantly expanding the capabilities of Intelligent Personal Assistants (IPAs). However, existing IPAs still face limitations in understanding user intent, task planning, tool utilization, and personal data management. Shadow-AI aims to address these challenges by integrating the GKD frontend controller and multimodal Large Language Models (such as Claude 3.5), allowing users to execute complex, multi-step operations on Android devices through voice or text inputs.

Features

• Multimodal Input: Supports both voice and text inputs, providing users with flexible interaction options.
• Complex Task Execution: Capable of performing cross-application operations on Android devices, enabling task automation.
• Context Awareness: Utilizes sensor data to understand environmental context, delivering intelligent responses.
• Memory Management: Stores and manages user preferences and history to offer personalized services.
• Extensibility: Features a plugin system that allows users to expand functionalities as needed.
• Security and Privacy: Implements robust data management and security measures to protect user privacy.

Architecture

Shadow-AI's architecture comprises the following key components:

1. Task Execution Module: Interprets user commands and executes corresponding actions on Android devices via the GKD frontend controller.
2. Context Awareness Module: Gathers environmental information through device sensors (e.g., GPS, microphone, camera) to comprehend the user's current situation.
3. Memory Management Module: Maintains user preferences, historical operations, and other personalized data to provide customized services.
4. LLM Interface Module: Interacts with Large Language Models (such as Claude 3.5) for natural language understanding and generation.
5. Security and Privacy Module: Ensures secure storage and transmission of user data, adhering to privacy protection principles.

This modular design ensures system flexibility and scalability, facilitating future feature additions and optimizations.

Getting Started

Prerequisites

• Operating System: Linux or macOS is recommended (Windows is also supported but may require additional configuration).
• Python: Version 3.8 or higher.
• Android Device or Emulator: Developer mode and USB debugging must be enabled.

Installation

1. Clone the Repository:

   git clone https://github.com/YourUsername/Shadow-AI.git
   cd Shadow-AI

2. Install Dependencies:

   It's recommended to use a virtual environment to manage dependencies:

   python3 -m venv venv
   source venv/bin/activate  # For Windows systems, use venv\Scripts\activate
   pip install -r requirements.txt

3. Configure Environment Variables:

   Create a .env file and set necessary environment variables, such as API keys, as required.

4. Run the Application:

   python main.py

   Ensure the Android device is connected with USB debugging enabled.

Usage

Once the application is running, you can interact with Shadow-AI through voice or text commands. The system will process your input and execute the corresponding tasks on your Android device.

Customization

Extending Functionality

Shadow-AI's plugin system allows developers to add new features or integrate additional services. Refer to the plugin development guide for detailed instructions.

Model Integration

To integrate a different LLM or update the existing model, modify the llm_interface module accordingly. Ensure compatibility with the system's architecture when making changes.

Roadmap

• Enhanced Context Awareness: Improve the system's ability to understand and respond to complex environmental cues.
• Expanded Device Support: Extend compatibility to a broader range of Android devices and versions.
• User Interface Improvements: Develop a more intuitive and user-friendly interface for interaction.

Documentation

Comprehensive documentation is available in the docs directory, including setup guides, API references, and development tutorials.

Contribution

We welcome contributions to Shadow-AI! Please follow these steps:

1. Fork the Repository: Click the Fork button on the top right corner of the GitHub page to create a copy under your account.
2. Create a Branch: Create