Wed, Oct 08, 25, Kor2unity Project Summary - Auto-imported from uconGPT project

Auto-imported from: D:/repos/aiegoo/uconGPT/eng2Fix/kor2fix/docs/kor2unity-project-summary.md
Original filename: kor2unity-project-summary.md
Import date: Wed, Oct 08, 25

kor2Unity Project: GitHub Issues & Documentation Summary

📋 Created Issues Summary

I have successfully analyzed your kor2Unity language development project and created comprehensive GitHub issues and documentation. Here’s what has been accomplished:

🔗 Linked GitHub Issues

  • #31 — EPIC: kor2Unity self-hosted AI integration sprint
  • #29 — Integrate existing TUI infrastructure with kor2Unity
  • #28 — Conda environment configuration for kor2Unity models
  • #26 — UI architecture decision: TUI vs web UI for Korean learning
  • #24 — LLM integration: self-hosted model strategy for Korean learning
  • #21 — LLM enhancement & Korean language features branch

🚀 Main Project Issues Created:

  1. Project Reorganization & Architecture Improvement
    • Comprehensive restructuring plan
    • Current state analysis and proposed improvements
    • New directory structure with modular design
  2. LLM Backend Consolidation & FastAPI Migration
    • Analysis of current Flask-based backend
    • Migration strategy to unified FastAPI application
    • Multiple LLM model integration improvements
  3. Unity Frontend Integration & Development
    • Unity WebGL frontend development plan
    • Korean language learning interface design
    • Interactive 3D learning environment specifications
  4. Docker Containerization & Development Environment
    • Multi-container architecture design
    • Development and production environment setup
    • Automated deployment and orchestration
  5. Project Organization Script Implementation
    • Automated reorganization tool (`scripts/organize-kor2unity.sh`)
    • File migration and structure creation
    • Backup and rollback capabilities
  6. GitHub Labels Documentation & Usage Guide
    • Comprehensive label system for issue management
    • Usage guidelines and filtering examples
    • Project workflow and categorization standards

🏷️ GitHub Labels System Created

Successfully Created Labels:

  • Priority: critical, high, medium, low
  • Components: backend, frontend, llm, rasa, unity, docker, database, api, docs
  • Types: feature, bug, task, epic, spike, refactor, security, performance
  • Technology: python, fastapi, flask, langchain, ollama, mongodb, faiss, csharp, webgl
  • Educational: korean, pronunciation, grammar, vocabulary, conversation, assessment
  • Plus many more categories…

📁 Project Analysis Results

Current Project Structure:

kor2unity/
├── README.md
├── Project-Version.txt
├── environment-setup/     # Vagrant/setup documentation
├── llm-backend/          # Flask API + RAG capabilities
├── llm-frontend/         # Basic web interface
├── minigpt/             # Llama4 inference
└── rasa-files/          # Conversational AI

Key Findings:

✅ Strengths:

  • Solid LLM backend with RAG capabilities
  • Multiple model support (Mistral, Qwen, Gemma, Llama4)
  • Document processing and vector search
  • Conversational AI integration
  • Comprehensive environment setup docs

⚠️ Areas for Improvement:

  • Scattered architecture needs consolidation
  • Missing Unity frontend integration
  • No user authentication system
  • Documentation fragmentation
  • Development environment complexity

🎯 Recommended Project Structure

The reorganization script creates this improved structure:

kor2unity-organized/
├── backend/              # Unified backend services
│   ├── api/             # FastAPI endpoints
│   ├── llm/             # LLM integration modules
│   ├── database/        # Database operations
│   └── services/        # Business logic
├── frontend/            # Unity WebGL interface
├── rasa/               # Conversational AI
├── unity/              # Unity project files
├── deployment/         # Docker/K8s configurations
├── docs/               # Comprehensive documentation
├── tests/              # Test suites
├── data/               # Data storage
└── scripts/            # Utility scripts

🚀 Implementation Roadmap

Phase 1: Foundation (Weeks 1-2)

  • Execute project reorganization script
  • Set up Docker containerization
  • Migrate to FastAPI backend

Phase 2: Unity Integration (Weeks 3-4)

  • Develop Unity WebGL frontend
  • Implement Korean language support
  • Create interactive learning interface

Phase 3: Enhancement (Weeks 5-6)

  • Add user authentication
  • Implement advanced LLM features
  • Create comprehensive testing

Phase 4: Production (Weeks 7-8)

  • Performance optimization
  • Production deployment
  • Documentation completion

🔧 Immediate Next Steps

  1. Run the Organization Script: 
    cd /home/hsyyu/repo/kor2unity
./scripts/organize-kor2unity.sh
  2. Review Generated Structure: 
    cd /home/hsyyu/kor2unity-organized
./scripts/setup.sh
  3. Start Development: 
    docker-compose up -d
  4. Apply GitHub Labels:
    • Review created issues
    • Apply appropriate labels
    • Set up project boards

📊 Project Metrics & Goals

Technical Targets:

  • API response time < 500ms
  • Unity WebGL load time < 10s
  • LLM inference time < 2s
  • System uptime > 99%

Educational Goals:

  • Interactive Korean learning experience
  • Speech recognition and synthesis
  • Gamified progress tracking
  • Adaptive learning paths

🔗 Key Files Created

  1. Organization Script: /scripts/organize-kor2unity.sh
  2. Label Setup Script: /home/hsyyu/setup-github-labels.sh
  3. Master Plan: /home/hsyyu/kor2unity-master-plan.md
  4. Structure Guide: /home/hsyyu/kor2unity-reorganized-structure.md

🎉 Project Vision

Transform kor2Unity into a comprehensive Korean language learning platform that combines:

  • AI-Powered Learning with local LLM models
  • Interactive 3D Environment using Unity WebGL
  • Conversational Practice through Rasa integration
  • Personalized Progress tracking and assessment
  • Gamified Experience with achievements and rewards

The project has excellent potential and with proper organization, can become a leading Korean language learning platform with cutting-edge AI integration!

Total Issues Created: 6 Labels Created: 60+ Documents Generated: 4 Scripts Created: 2

Status: Ready for implementation ✅