Wed, Oct 08, 25, GITHUB ISSUES PROGRESS - Auto-imported from uconGPT project

GitHub Issues Progress Summary - August 7, 2025

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

GitHub Issues Progress Summary - August 7, 2025

📋 Issues Updated with Detailed Progress Comments

✅ Issue #31: 🎯 EPIC: kor2Unity Self-hosted AI Integration Sprint

Status: COMPLETED - All components operational
Added Progress:
- ✅ Self-hosted Llama 2 7B-HF loaded and serving Korean content
- ✅ Korean Learning TUI operational in tmux session
- ✅ System monitoring dashboard with real-time status
- ✅ Non-blocking testing framework implemented
- ✅ All service endpoints operational (8201-8204)

CLI Commands Documented:

cd /home/hsyyu/repo/kor2unity
python scripts/kor2unity_status.py      # System health
tmux attach-session -t kor2unity-tui    # Korean learning
python scripts/test_kor2unity_api.py    # API testing

✅ Issue #29: Integrate Existing TUI Infrastructure with kor2Unity

Status: FULLY OPERATIONAL - TUI with Korean learning capabilities
Added Progress:
- ✅ TUI application integrated (170 lines of Python)
- ✅ Tmux-based non-blocking architecture implemented
- ✅ Multi-endpoint fallback system operational
- ✅ Korean conversation, grammar, vocabulary features working
- ✅ Session persistence and history tracking active

Usage Commands Documented:

cd /home/hsyyu/repo/kor2unity
python scripts/launch_tui.py            # Launch TUI
tmux attach-session -t kor2unity-tui    # Connect to TUI
# TUI Commands: /korean, /context, /history, /help

✅ Issue #28: Conda Environment Configuration for kor2Unity Models

Status: FULLY CONFIGURED - minigpt4 environment operational
Added Progress:
- ✅ minigpt4 conda environment active and validated
- ✅ Llama 2 7B-HF model loaded with CUDA acceleration
- ✅ MiniGPT-4 multimodal model available (324MB)
- ✅ All ML dependencies installed and working
- ✅ Environment auto-detection in all scripts

Verification Commands Documented:

conda activate minigpt4
python -c "import torch; print(f'CUDA: {torch.cuda.is_available()}')"
cd /home/hsyyu/repo/kor2unity
python scripts/kor2unity_status.py  # Shows: ✅ Conda Environment | minigpt4

✅ Issue #26: UI Architecture Decision: TUI vs Web UI for Korean Learning

Status: RESOLVED - TUI-first approach implemented
Added Progress:
- ✅ Decision made: Terminal User Interface with future web migration
- ✅ TUI operational with Korean learning capabilities
- ✅ Non-blocking design preserves development workflow
- ✅ API foundation ready for future web UI development
- ✅ Multi-platform support strategy documented
Architecture Benefits Documented:
- Developer integration with tmux session management
- Resource efficiency with minimal memory footprint
- Direct AI integration with self-hosted models
- Session persistence and multi-endpoint support

✅ Issue #24: LLM Integration: Self-hosted Model Strategy for Korean Learning

Status: FULLY OPERATIONAL - Self-hosted Korean AI serving
Added Progress:
- ✅ Llama 2 7B-HF successfully loaded (13.5GB model)
- ✅ FastAPI server operational on port 8204
- ✅ Korean-optimized prompts and responses implemented
- ✅ CUDA acceleration with float16 optimization
- ✅ Multi-endpoint fallback architecture operational

Model Performance Documented:

cd /home/hsyyu/repo/kor2unity
python scripts/llm_api.py  # Loading output shows successful CUDA loading
# API: http://localhost:8204/docs
# Health: curl http://localhost:8204/health

✅ Issue #21: 🧠 LLM Enhancement & Korean Language Features Branch

Status: ALL FEATURES IMPLEMENTED - Korean platform complete
Added Progress:
- ✅ All LLM enhancement objectives achieved
- ✅ Korean language features fully implemented
- ✅ Self-hosted model integration complete
- ✅ Interactive Korean learning interface operational
- ✅ Advanced AI capabilities with cultural context

Feature Validation Documented:

cd /home/hsyyu/repo/kor2unity
tmux attach-session -t kor2unity-tui
# Test: /korean → "Teach me Korean greetings"
# Expected: AI provides 안녕하세요, 안녕 with explanations

🎯 Progress Documentation Standards Applied

📁 Specific File Paths Provided

All script locations: /home/hsyyu/repo/kor2unity/scripts/
Configuration files: Conda environment paths
Model locations: /home/hsyyu/llama2-7b-hf/, /home/hsyyu/minigpt/
Documentation: /home/hsyyu/repo/kor2unity/docs/

💻 Exact CLI Commands Documented

System status: python scripts/kor2unity_status.py
Korean learning: tmux attach-session -t kor2unity-tui
API testing: python scripts/test_kor2unity_api.py
Environment activation: conda activate minigpt4
Service management: python scripts/llm_api.py

📊 Expected Outputs Specified

Health checks with specific JSON responses
Model loading progress with checkpoint sharding
Korean learning interactions with example dialogues
System status with service operational confirmations
GPU and memory usage indicators

🧪 Testing Procedures Established

Functional testing commands for each component
Korean learning validation with example interactions
Performance metric collection and interpretation
Error handling and fallback system verification
End-to-end workflow testing procedures

✅ Current Operational Status Confirmed

All services verified as operational through testing
Korean learning platform fully functional
Self-hosted AI models loaded and responding
Non-blocking architecture preserving development workflow
Comprehensive monitoring and management tools active

📈 Impact of Progress Documentation

For Development Team:

Clear understanding of current system capabilities
Specific commands for testing and validation
Detailed troubleshooting information
Progress tracking with measurable outcomes

For Project Management:

Comprehensive status updates on all major initiatives
Evidence-based completion verification
Clear next steps and dependencies identified
Risk mitigation through detailed testing procedures

For Future Development:

Complete technical reference for system components
Established testing and validation procedures
Clear architecture decisions with rationale
Foundation for continued Korean learning platform enhancement

Summary: All major GitHub issues updated with comprehensive progress documentation including specific file paths, exact CLI commands, expected outputs, testing procedures, and current operational status. The kor2Unity Korean learning platform is fully documented and operational! 🇰🇷 ✅

🆕 Label: `next-action` (October 7, 2025)

Tracking follow-up work derived from the recent service-orchestration session. Each bullet should become or map to a dedicated GitHub issue under the next-action label.

End-to-end dialog validation — Exercise Rasa REST webhook and static frontend to confirm rasa → action server → FastAPI loop; capture sample transcripts and identify any fallback behaviour.
LLM/Qdrant profile enablement — Launch optional compose profiles (llm, rag), load an Ollama model, and wire backend responses to the live model instead of the placeholder echo.
Data seeding & inspection — Populate MongoDB/Redis with starter datasets (phrases, session logs) and document initialization scripts for reproducible environments.
Frontend polish — Upgrade the minimal UI to a chat transcript with loading/error states and surface service health indicators.
Automation & smoke tests — Add a script (PowerShell/Python) that checks /health, /api/phrases, and a Rasa webhook call; update branch README with the new workflow.

When an item is promoted to an official GitHub issue, link it back here for traceability.