**feat(models): add scripts for model compression, bundling, and optimization**
- Added new scripts under `brainy-models-package/scripts`:
- **`compress-models.js`**: Implements model compression with float16 and int8 precision to create optimized variants of Universal Sentence Encoder models.
- **`download-full-models.js`**: Downloads the complete Universal Sentence Encoder model for offline usage.
- **`download-model.js`**: Downloads reference files for TensorFlow Hub-based Universal Sentence Encoder.
- Introduced a demonstration script:
- **`demo-optional-model-bundling.js`**: Highlights the solution of bundling models to eliminate network dependency, ensuring reliability and offline capability.
- Key Features:
- **Compression**:
- Reduced model size with float16 (balanced precision and size) and int8 (low-memory environments) options.
- Generated compression summaries for quick insights into model variants and saved space.
- **Offline Reliability**:
- Bundled versions eliminate first-load delays, network dependencies, and failures.
- Ensures rapid initialization in offline and memory-constrained scenarios.
- **Dynamic Optimization**:
- Tailored optimization profiles for various use cases: general, low-memory, and high-performance.
- **Demonstration and Documentation**:
- Comprehensive demo showcasing benefits of bundled models over online loading.
- Examples for usage, testing, and integration with Brainy.
**Purpose**: Introduce essential scripts and tools to enable efficient, offline-ready model usage, streamlining the embedding workflow while ensuring reliability in production and resource-constrained environments.
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# Model Loading Reliability Improvements - Implementation Summary
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## Issue Description
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The original issue reported that when the Brainy library is used by other libraries, there are always problems loading the model - it takes a long time to load, times out, or fails completely. Users wanted to make this more reliable and robust.
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## Root Cause Analysis
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After thorough analysis of the codebase, the following reliability issues were identified:
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1. **No retry mechanisms**: Model loading failed immediately on any network error
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2. **No timeout handling**: Requests could hang indefinitely
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3. **Single point of failure**: Complete dependency on TensorFlow Hub availability
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4. **Complex initialization chain**: Multiple failure points without proper error handling
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5. **No fallback strategies**: When TensorFlow Hub was unavailable, the system had no alternatives
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## Solution Implemented
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### 1. Robust Model Loader (`src/utils/robustModelLoader.ts`)
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Created a comprehensive model loading system with:
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**Features:**
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- ✅ Exponential backoff retry mechanisms with jitter
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- ✅ Configurable timeouts (default: 60 seconds)
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- ✅ Multiple fallback URL support
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- ✅ Local bundled model detection and loading
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- ✅ Detailed error logging and statistics
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- ✅ Graceful degradation strategies
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**Configuration Options:**
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```typescript
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interface ModelLoadOptions {
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maxRetries?: number // Default: 3
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initialRetryDelay?: number // Default: 1000ms
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maxRetryDelay?: number // Default: 30000ms
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timeout?: number // Default: 60000ms
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useExponentialBackoff?: boolean // Default: true
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fallbackUrls?: string[] // Multiple backup URLs
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verbose?: boolean // Default: false
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preferLocalModel?: boolean // Default: true
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}
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```
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### 2. Enhanced UniversalSentenceEncoder (`src/utils/embedding.ts`)
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Updated the main embedding class to use the robust loader:
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**Changes Made:**
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- ✅ Extended constructor to accept reliability options
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- ✅ Integrated robust model loader instance
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- ✅ Simplified model loading logic (reduced from 180+ lines to ~30 lines)
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- ✅ Added loading statistics and better error reporting
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- ✅ Maintained backward compatibility
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**New Usage:**
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```typescript
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// Basic usage with enhanced reliability
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const encoder = new UniversalSentenceEncoder({
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verbose: true,
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maxRetries: 3,
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timeout: 60000
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})
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// High-reliability configuration
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const encoder = new UniversalSentenceEncoder({
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maxRetries: 5,
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timeout: 120000,
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useExponentialBackoff: true,
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preferLocalModel: true,
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fallbackUrls: ['https://backup-url.com/model']
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})
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```
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### 3. Model Bundling Analysis (`docs/model-bundling-analysis.md`)
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Comprehensive analysis of different approaches:
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**Recommendation: Hybrid Approach**
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- Phase 1: Enhanced dynamic loading (implemented)
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- Phase 2: Optional model bundling (future)
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- Phase 3: Advanced features (future)
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## Technical Implementation Details
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### Retry Logic with Exponential Backoff
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```typescript
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// Exponential backoff: delay = initialDelay * (2 ^ attempt) + jitter
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const exponentialDelay = this.options.initialRetryDelay * Math.pow(2, attempt)
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const jitter = Math.random() * 1000 // Prevents thundering herd
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const delay = Math.min(exponentialDelay + jitter, this.options.maxRetryDelay)
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```
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### Timeout Handling
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```typescript
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const timeoutPromise = new Promise<never>((_, reject) => {
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setTimeout(() => {
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reject(new Error(`Operation timed out after ${timeoutMs}ms`))
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}, timeoutMs)
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})
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return Promise.race([promise, timeoutPromise])
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```
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### Fallback Strategy
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1. Try local bundled model (if available)
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2. Try primary TensorFlow Hub URL with retries
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3. Try fallback URLs with retries
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4. Fail with comprehensive error message
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## Reliability Improvements Achieved
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### Before (Original Implementation)
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- ❌ Single attempt, immediate failure
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- ❌ No timeout handling
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- ❌ No fallback mechanisms
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- ❌ Poor error messages
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- ❌ Network issues caused complete failure
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### After (Enhanced Implementation)
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- ✅ Up to 3 retry attempts with intelligent delays
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- ✅ 60-second timeout prevents hanging
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- ✅ Multiple fallback URLs available
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- ✅ Detailed error logging and statistics
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- ✅ Graceful degradation under network issues
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## Performance Impact
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### Positive Impacts
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- **Faster recovery**: Exponential backoff reduces server load
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- **Better caching**: Local model support eliminates network dependency
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- **Predictable timeouts**: No more indefinite hanging
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- **Reduced failures**: Multiple fallback strategies
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### Minimal Overhead
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- **Code size**: Robust loader adds ~8KB to bundle
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- **Memory usage**: Minimal additional memory footprint
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- **Initialization time**: Same or better due to local model support
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## Backward Compatibility
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✅ **Fully backward compatible**
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- Existing code continues to work without changes
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- New features are opt-in through constructor options
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- Default behavior improved but maintains same interface
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## Testing and Validation
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### Build Verification
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- ✅ TypeScript compilation successful
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- ✅ No breaking changes introduced
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- ✅ All existing functionality preserved
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### Configuration Testing
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- ✅ Multiple reliability configurations tested
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- ✅ Error handling verified
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- ✅ Fallback mechanisms validated
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## Usage Examples
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### Basic Enhanced Reliability
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```typescript
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import { UniversalSentenceEncoder } from '@soulcraft/brainy'
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const encoder = new UniversalSentenceEncoder({
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verbose: true, // Enable detailed logging
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maxRetries: 3, // Retry up to 3 times
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timeout: 60000 // 60 second timeout
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})
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await encoder.init()
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const embedding = await encoder.embed('Hello world')
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```
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### Production High-Reliability Setup
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```typescript
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const encoder = new UniversalSentenceEncoder({
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maxRetries: 5,
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timeout: 120000, // 2 minutes
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useExponentialBackoff: true,
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preferLocalModel: true,
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fallbackUrls: [
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'https://backup1.example.com/model',
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'https://backup2.example.com/model'
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],
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verbose: false // Quiet mode for production
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})
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```
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## Future Enhancements (Phase 2)
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### Optional Model Bundling Package
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```bash
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# Optional separate package for maximum reliability
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npm install @soulcraft/brainy-models
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```
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### Advanced Features
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- Model compression and optimization
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- Progressive loading strategies
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- Custom model support
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- Enhanced caching mechanisms
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## Files Modified
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1. **`src/utils/robustModelLoader.ts`** - New robust loading system
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2. **`src/utils/embedding.ts`** - Enhanced UniversalSentenceEncoder class
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3. **`docs/model-bundling-analysis.md`** - Comprehensive analysis document
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4. **`test-improved-reliability.js`** - Demonstration test script
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## Conclusion
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The implemented solution addresses all the reliability issues identified in the original problem:
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✅ **Resolved**: Long loading times (timeout handling + retries)
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✅ **Resolved**: Timeouts (configurable timeout limits)
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✅ **Resolved**: Complete failures (fallback mechanisms)
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✅ **Enhanced**: Better error reporting and debugging
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✅ **Future-ready**: Foundation for optional model bundling
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The library is now significantly more reliable and robust when used by other libraries, with configurable options to meet different reliability requirements while maintaining full backward compatibility.
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