RAY-AI-application

RAY AI - Offline AI Inference for Android

RAY AI is a high-performance, private, and secure offline AI assistant for Android. It leverages llama.cpp and ONNX Runtime to provide state-of-the-art LLM inference directly on your device without requiring an internet connection.

🚀 Features

• 100% Offline Inference: No data leaves your device. Private by design.
• llama.cpp Integration: High-performance C++ backend optimized for mobile ARM processors.
• Dual Engine Support: Supports both GGUF (via llama.cpp) and ONNX models.
• Modern Copilot UI: Commercial-grade Material 3 design with smooth animations.
• Model Management: Download and manage lightweight models optimized for mobile (TinyLlama, Qwen, etc.).
• Real-time Streaming: Watch responses generate token-by-token.
• Secure Storage: Models and sensitive data are protected with hardware-backed encryption.

Screenshots

Here’s how RAY AI looks in action:

From left to right, top to bottom:
Chat · Model management · Thinking → Response · Complete answer streaming

🛠 Tech Stack

• Language: Java / C++ (JNI)
• UI Framework: Android Material Components 3
• Inference Engines:
  • llama.cpp (Native C++)
  • ONNX Runtime
• Build System: CMake & Gradle
• Min SDK: Android 24 (Nougat)

📥 Getting Started

Prerequisites

• Android Studio Koala or newer
• Android NDK 26.1.10909125+
• CMake 3.22.1+

Installation

1. Clone the repository:

   git clone https://github.com/Rohithdgrr/working-RAY-AI.git
   

2. Open the project in Android Studio.
3. Sync project with Gradle files.
4. Build and run on a physical device (arm64-v8a recommended).

📄 Documentation

• Architecture - Deep dive into JNI and engine integration.
• Installation Guide - Detailed build instructions and troubleshooting.
• User Guide - How to use the app and manage models.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.