# DigiTwin Implementation Roadmap

## Current Status: ✅ Production Ready
- ✅ Core application functionality
- ✅ Data upload and processing
- ✅ FPSO visualizations
- ✅ Pivot table analytics
- ✅ Database persistence
- ✅ Responsive UI with custom styling
- ✅ Sidebar layout optimizations

---

## Phase 1: Data Preprocessing Module
**Timeline**: Week 1-2
**Status**: 🔄 Planned

### Tasks:
- [ ] Create `preprocessing.py` module
- [ ] Implement column analysis functionality
- [ ] Add data cleaning pipeline
- [ ] Integrate with main application
- [ ] Test with existing datasets

### Deliverables:
- Preprocessing module with column removal logic
- Data size reduction by 40-60%
- Improved loading performance

---

## Phase 2: Feature Engineering
**Timeline**: Week 3-4
**Status**: 🔄 Planned

### Tasks:
- [ ] Create `feature_engineering.py` module
- [ ] Implement Main WorkCtr categorization
- [ ] Add temporal and spatial features
- [ ] Update database schema
- [ ] Integrate with analytics

### Deliverables:
- Enhanced dataset with derived features
- Improved analytics capabilities
- Better insights generation

---

## Phase 3: LLM Integration with RAG
**Timeline**: Week 5-8
**Status**: 🔄 Planned

### Tasks:
- [ ] Set up vector database (Chroma/FAISS)
- [ ] Implement LLM model integration
- [ ] Create RAG query system
- [ ] Add chat interface to Streamlit
- [ ] Test and optimize

### Deliverables:
- Natural language query capability
- Intelligent insights generation
- Enhanced user experience

---

## Phase 4: Integration & Testing
**Timeline**: Week 9-10
**Status**: 🔄 Planned

### Tasks:
- [ ] Integrate all modules
- [ ] Performance testing
- [ ] User acceptance testing
- [ ] Documentation updates
- [ ] Production deployment

### Deliverables:
- Fully integrated enhanced application
- Performance benchmarks
- User documentation

---

## Success Metrics

### Performance Targets:
- ⚡ 50% faster data loading
- 💾 40-60% data size reduction
- 🧠 <3 second RAG query response
- 📊 >90% query accuracy

### User Experience:
- 🎯 Natural language interaction
- 📈 Enhanced analytics insights
- 🔍 Improved data discovery
- 🚀 Better overall performance

---

## Notes
- All enhancements maintain backward compatibility
- Modular design for easy integration
- Focus on user experience and performance
- Scalable architecture for future growth

---

*Last Updated: December 2024*
*Next Review: [Date]*