Quest Dev Copilot

Quest Dev Copilot is an AI-powered debugging assistant for Unreal Engine Quest VR development. This system combines Retrieval-Augmented Generation (RAG), Llama API integration, Gemini for embeddings, and Unreal Engine plugins to provide intelligent error analysis and potential fixes.

Project Goals

• Provide intelligent analysis of Unreal Engine logs for Quest VR development.
• Suggest potential fixes and relevant documentation for identified errors.
• Integrate with Unreal Engine via a Slate UI plugin.
• Utilize Llama API for advanced reasoning and Gemini for embeddings.
• Implement a robust backend for processing and a user-friendly CLI.

Core Technologies

• AI/ML: Llama API, Gemini, ChromaDB
• Backend: Flask, asyncio/aiohttp, BeautifulSoup
• Frontend/Client: Unreal Engine Slate (C++), Rich CLI (Python)
• Data: Forum scraping, vector embeddings, JSON processing

Getting Started

Prerequisites

• Python 3.9+
• Unreal Engine (Specify Version)
• Access to Llama API
• Access to Google Gemini API

Setup

1. Clone the repository:

   git clone <repository-url>
   cd quest-dev-copilot

2. Create and populate .env file: Copy .env.example to .env and fill in your API keys and any other necessary environment variables:

   cp .env.example .env
   # Open .env and add your LLAMA_API_KEY and GEMINI_API_KEY

3. Backend Setup:

   cd backend
   # python -m venv venv
   # source venv/bin/activate (or venv\Scripts\activate on Windows)
   # pip install -r requirements.txt
   # flask run

   (Detailed backend setup steps will be added here)

4. RAG Setup: (Detailed RAG setup and data ingestion steps will be added here)

5. Unreal Engine Plugin Setup: (Detailed steps for integrating the plugin will be added here)

6. CLI Setup: (Detailed CLI setup steps will be added here)

Usage

(Usage instructions for the Unreal Engine plugin and CLI will be added here)

Project Structure

quest-dev-copilot/
├── backend/            # Flask backend application
│   ├── app.py          # Main Flask application
│   ├── routes/         # API route definitions
│   ├── models/         # Data models and schemas
│   └── utils/          # Helper functions
├── rag/                # Retrieval-Augmented Generation components
│   ├── vector_store.py # ChromaDB interface
│   ├── embeddings.py   # Embedding utilities (using Gemini)
│   └── retrieval.py    # Document retrieval logic
├── llama/              # Llama API integration
│   ├── client.py       # Main Llama API client
│   ├── models.py       # Response models
│   └── cost_tracker.py # Usage analytics
├── unreal_plugin/      # Unreal Engine Slate plugin (C++)
│   └── ...
├── cli/                # Python Rich CLI
│   └── ...
├── tests/              # Unit and integration tests
│   ├── unit/
│   ├── integration/
│   └── fixtures/
├── .env.example        # Example environment variables
├── requirements.txt    # Python dependencies for the backend/CLI
└── README.md           # This file

Contributing

(Contribution guidelines will be added here)

License

(License information will be added here)