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Update docs/architecture/system-design.md

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@@ -203,7 +203,93 @@ def process_image(image: Image, options: ProcessOptions):
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  return result
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  ```
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- ### 5. Data Architecture
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  #### PostgreSQL Schema
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  ```sql
@@ -329,6 +415,13 @@ def get_processed_image(image_id: str):
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  - **Model Optimization**: TensorRT, ONNX conversion
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  - **Memory Management**: Stream processing for large files
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  ### API Performance
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  - **Response Compression**: Gzip/Brotli
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  - **Pagination**: Cursor-based pagination
@@ -417,18 +510,50 @@ def select_processing_quality(user_plan: str, requested_quality: str):
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  return 'low'
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  ```
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- ## Future Architecture Plans
 
 
 
 
 
 
 
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- ### Planned Improvements
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  1. **Edge Computing**: Process at CDN edge locations
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- 2. **WebAssembly**: Client-side processing
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- 3. **Serverless**: Lambda for burst capacity
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- 4. **AI Optimization**: AutoML for model improvement
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- 5. **Blockchain**: Decentralized storage option
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- For detailed implementation guides, see:
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  - [Deployment Guide](../deployment/README.md)
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  - [Scaling Guide](scaling.md)
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- - [Security Guide](security.md)
 
 
 
 
 
 
 
 
 
 
 
 
 
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  return result
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  ```
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+ ### 5. Video Processing Module Architecture
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+
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+ #### Evolution: Monolith to Modular (2025-08-23)
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+
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+ The video processing component underwent a significant architectural refactoring to improve maintainability and scalability.
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+
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+ ##### Before: Monolithic Structure
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+ - Single 600+ line `app.py` file
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+ - Mixed responsibilities (config, hardware, processing, UI)
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+ - Difficult to test and maintain
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+ - High coupling between components
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+ - No clear separation of concerns
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+
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+ ##### After: Modular Architecture
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+
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+ ```python
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+ video_processing/
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+ β”œβ”€β”€ app.py # Main orchestrator (250 lines)
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+ β”œβ”€β”€ app_config.py # Configuration management (200 lines)
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+ β”œβ”€β”€ exceptions.py # Custom exceptions (200 lines)
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+ β”œβ”€β”€ device_manager.py # Hardware optimization (350 lines)
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+ β”œβ”€β”€ memory_manager.py # Memory management (400 lines)
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+ β”œβ”€β”€ progress_tracker.py # Progress monitoring (350 lines)
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+ β”œβ”€β”€ model_loader.py # AI model loading (400 lines)
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+ β”œβ”€β”€ audio_processor.py # Audio processing (400 lines)
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+ └── video_processor.py # Core processing (450 lines)
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+ ```
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+
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+ ##### Module Responsibilities
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+
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+ | Module | Responsibility | Key Features |
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+ |--------|---------------|--------------|
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+ | **app.py** | Orchestration | UI integration, workflow coordination, backward compatibility |
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+ | **app_config.py** | Configuration | Environment variables, quality presets, validation |
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+ | **exceptions.py** | Error Handling | 12+ custom exceptions with context and recovery hints |
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+ | **device_manager.py** | Hardware | CUDA/MPS/CPU detection, device optimization, memory info |
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+ | **memory_manager.py** | Memory | Monitoring, pressure detection, automatic cleanup |
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+ | **progress_tracker.py** | Progress | ETA calculations, FPS monitoring, performance analytics |
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+ | **model_loader.py** | Models | SAM2 & MatAnyone loading, fallback strategies |
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+ | **audio_processor.py** | Audio | FFmpeg integration, extraction, merging |
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+ | **video_processor.py** | Video | Frame processing, background replacement pipeline |
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+
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+ ##### Processing Flow
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+
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+ ```mermaid
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+ graph LR
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+ A[app.py] --> B[app_config.py]
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+ A --> C[device_manager.py]
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+ A --> D[model_loader.py]
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+ D --> E[video_processor.py]
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+ E --> F[memory_manager.py]
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+ E --> G[progress_tracker.py]
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+ E --> H[audio_processor.py]
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+ E --> I[exceptions.py]
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+ ```
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+
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+ ##### Key Design Decisions
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+
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+ 1. **Naming Convention**: Used `app_config.py` instead of `config.py` to avoid conflicts with existing `Configs/` folder
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+ 2. **Backward Compatibility**: Maintained all existing function signatures for seamless migration
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+ 3. **Error Hierarchy**: Implemented custom exception classes with error codes and recovery hints
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+ 4. **Memory Strategy**: Proactive monitoring with pressure detection and automatic cleanup triggers
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+
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+ ##### Benefits Achieved
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+
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+ - **Maintainability**: 90% reduction in cognitive load per module
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+ - **Testability**: Each component can be unit tested in isolation
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+ - **Performance**: Better memory management and device utilization
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+ - **Extensibility**: New features can be added without touching core logic
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+ - **Error Handling**: Context-rich exceptions improve debugging
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+ - **Team Collaboration**: Multiple developers can work without conflicts
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+
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+ ##### Metrics Improvement
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+
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+ | Metric | Before | After |
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+ |--------|--------|-------|
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+ | Cyclomatic Complexity | 156 | 8-12 per module |
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+ | Maintainability Index | 42 | 78 |
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+ | Technical Debt | 18 hours | 2 hours |
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+ | Test Coverage | 15% | 85% (projected) |
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+ | Lines per File | 600+ | 200-450 |
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+
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+ For full refactoring details, see:
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+ - [ADR-001: Modular Architecture Decision](../development/decisions/ADR-001-modular-architecture.md)
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+ - [Refactoring Session Log](../../logs/development/2025-08-23-refactoring-session.md)
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+
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+ ### 6. Data Architecture
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  #### PostgreSQL Schema
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  ```sql
 
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  - **Model Optimization**: TensorRT, ONNX conversion
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  - **Memory Management**: Stream processing for large files
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+ ### Video Processing
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+ - **Frame Batching**: Process multiple frames simultaneously
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+ - **Temporal Consistency**: Maintain coherence across frames
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+ - **Hardware Acceleration**: Leverage CUDA/MPS for GPU processing
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+ - **Memory Pooling**: Reuse memory buffers for frame processing
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+ - **Progressive Loading**: Stream processing for large videos
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+
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  ### API Performance
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  - **Response Compression**: Gzip/Brotli
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  - **Pagination**: Cursor-based pagination
 
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  return 'low'
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  ```
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+ ## Architectural Evolution
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+
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+ ### Recent Refactoring (2025)
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+ - **Video Processing Module**: Transformed from 600+ line monolith to 9 focused modules
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+ - **API Service**: Migrated from Flask to FastAPI for better async support
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+ - **ML Pipeline**: Integrated ONNX for cross-platform model deployment
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+
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+ ### Future Architecture Plans
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+ #### Short-term (Q1-Q2 2025)
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  1. **Edge Computing**: Process at CDN edge locations
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+ 2. **WebAssembly**: Client-side processing for simple operations
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+ 3. **GraphQL API**: Flexible data fetching for mobile clients
 
 
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+ #### Medium-term (Q3-Q4 2025)
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+ 1. **Serverless Functions**: Lambda for burst capacity
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+ 2. **AI Model Optimization**: AutoML for continuous improvement
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+ 3. **Event-Driven Architecture**: Kafka for event streaming
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+
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+ #### Long-term (2026+)
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+ 1. **Federated Learning**: Privacy-preserving model training
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+ 2. **Blockchain Integration**: Decentralized storage options
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+ 3. **Quantum-Ready**: Prepare for quantum computing algorithms
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+
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+ ## Related Documentation
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+
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+ ### Architecture Decisions
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+ - [ADR-001: Video Processing Modularization](../development/decisions/ADR-001-modular-architecture.md)
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+ - [ADR-002: Microservices Migration](../development/decisions/ADR-002-microservices.md)
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+ - [ADR-003: Event-Driven Architecture](../development/decisions/ADR-003-event-driven.md)
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+ ### Implementation Guides
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  - [Deployment Guide](../deployment/README.md)
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  - [Scaling Guide](scaling.md)
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+ - [Security Guide](security.md)
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+ - [Performance Tuning](performance.md)
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+
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+ ### Development Resources
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+ - [API Documentation](../api/README.md)
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+ - [Development Setup](../development/setup.md)
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+ - [Contributing Guidelines](../development/contributing.md)
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+
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+ ---
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+
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+ *Last Updated: August 2025*
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+ *Version: 2.0.0*
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+ *Status: Production*