**feat(docs): add comprehensive documentation for model bundling and robust loading**

- Introduced new documentation files under `docs/`: - `model-bundling-analysis.md`: Provides detailed analysis of current, bundled, hybrid, and dynamic model loading approaches, including pros, cons, and recommendations. - `model-management.md`: Explains how Brainy manages Universal Sentence Encoder models, including setup, usage, and troubleshooting. - `optional-model-bundling.md`: Details the `@soulcraft/brainy-models` package for offline reliability with pre-bundled models. - Added `src/utils/robustModelLoader.ts`: - Implements enhanced model loading with retry mechanisms, timeout handling, fallback URLs, and optional local model bundling. - Supports Node.js and browser environments with exponential backoff logic. - Key Updates: - **Hybrid Loading Strategy**: Recommended for balancing reliability and flexibility via hybrid online/offline mechanisms. - **Enhanced Fallback Scenarios**: Robust loader improves network-dependent reliability for embedding workflows. - **Offline Reliability Support**: Optional model bundling eliminates dependency on external services, supporting air-gapped and edge environments. **Purpose**: Introduce a hybrid model loading approach with robust options for
2025-08-01 15:35:29 -07:00 · 2025-08-01 15:35:29 -07:00 · 42571c5883
commit 42571c5883
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--- a/docs/guides/model-management.md
+++ b/docs/guides/model-management.md
@ -0,0 +1,130 @@
+# Model Management Guide
+
+This guide explains how to manage the TensorFlow Universal Sentence Encoder model used by Brainy for text embeddings.
+
+## Overview
+
+Brainy uses the Universal Sentence Encoder model from TensorFlow.js to convert text into vector embeddings. These
+embeddings are essential for semantic search and other features.
+
+The model is referenced through local configuration files that point to the TensorFlow Hub URL. This approach ensures
+that:
+
+1. The model is automatically downloaded when first needed
+2. Subsequent uses leverage the cached model
+3. The application works consistently across all environments (browser, Node.js, serverless, workers)
+
+## Model Files
+
+The model files are stored in the `models/sentence-encoder/` directory and include:
+
+- `model.json` (~1KB) - The main model configuration file that references the TensorFlow Hub URL
+- `group1-shard1of1.bin` (~2KB) - A sample embedding file (not the full model weights)
+- `metadata.json` (~0.4KB) - Additional information about the model
+
+**Important Note**: These are small reference files (total ~3KB), not the full model (~25MB). The full model is
+downloaded automatically when first needed and then cached locally. This approach keeps the repository size small while
+ensuring the model is available when needed.
+
+These files should be checked into version control to ensure they're available in all environments.
+
+## Setting Up Model Reference Files
+
+The model reference files are generated using the `download-model.js` script:
+
+```bash
+node scripts/download-model.js
+```
+
+**Important**: This script does NOT download the full model (~25MB) locally. Instead, it creates small reference
+files (~3KB total) that point to the TensorFlow Hub URL. The full model will be downloaded automatically when your
+application first uses it, and then cached for future use.
+
+### When to Run the Script
+
+You should run this script in the following situations:
+
+1. **Initial Setup**: When first setting up the project
+2. **Model Updates**: When updating to a new version of the Universal Sentence Encoder
+3. **Missing Files**: If the model reference files are missing or corrupted
+
+The script will:
+
+1. Load the model from TensorFlow Hub to verify it works (temporarily downloading it to memory)
+2. Create a model.json file that references the TensorFlow Hub URL
+3. Generate a sample embedding and save it as a small binary file
+4. Create a metadata file with information about the model
+
+### What to Expect
+
+After running the script:
+
+1. You'll see small files in the `models/sentence-encoder/` directory (total ~3KB)
+2. When your application first uses the model, it will download the full model (~25MB) from TensorFlow Hub
+3. The downloaded model will be cached locally for subsequent use
+4. No further downloads will be needed unless the cache is cleared
+
+## Using the Model
+
+The model is automatically loaded by the `UniversalSentenceEncoder` class in `src/utils/embedding.ts`. The loading
+process follows these steps:
+
+1. Check if local model reference files exist
+2. If they exist, load the model using the TensorFlow Hub URL referenced in the model.json file
+3. The first time this happens, the full model will be downloaded and cached
+4. Subsequent uses will use the cached model
+
+## Environments
+
+The model loading works across all environments:
+
+- **Node.js**: Uses file system paths to load the model
+- **Browser**: Uses relative URLs to load the model
+- **Serverless/Workers**: Uses the embedded model files
+
+## Best Practices
+
+1. **Always check in model files**: The model files should be committed to version control
+2. **Run tests after model updates**: Use `node test-model-loading.js` to verify the model works
+3. **Update model during build**: If you prefer not to check in model files, run the download script as part of your
+   build process
+
+## Troubleshooting
+
+### Common Issues
+
+#### "The model files are too small (only a few KB)"
+
+This is expected behavior. The script creates small reference files (~3KB total), not the full model (~25MB). The full
+model will be downloaded automatically when your application first uses it.
+
+#### "Model not found or loading errors"
+
+1. Verify the model reference files exist in `models/sentence-encoder/`
+2. Run `node scripts/download-model.js` to generate fresh reference files
+3. Check for errors in the console related to model loading
+4. Ensure the model reference files are properly included in your deployment package
+5. Make sure your application has internet access the first time it runs to download the full model
+
+#### "Model download is slow"
+
+The first time your application uses the model, it will download the full model (~25MB) from TensorFlow Hub. This may
+take a few minutes depending on your internet connection. Subsequent uses will use the cached model and will be much
+faster.
+
+## Technical Details
+
+The Universal Sentence Encoder model:
+
+- Produces 512-dimensional embeddings
+- Works with text in any language
+- Is optimized for semantic similarity tasks
+- Has a size of approximately 25MB when fully downloaded
+- Is downloaded from TensorFlow Hub on first use and then cached
+
+Our approach of referencing the model via TensorFlow Hub URL provides several benefits:
+
+1. Small package size (only reference files are included, not the full model)
+2. Automatic caching for improved performance after first use
+3. Consistent behavior across all environments
+4. Always uses the correct model weights
--- a/docs/guides/optional-model-bundling.md
+++ b/docs/guides/optional-model-bundling.md
@ -0,0 +1,364 @@
+# Optional Model Bundling Package
+
+## Overview
+
+The `@soulcraft/brainy-models` package provides pre-bundled TensorFlow models for maximum reliability with the Brainy
+vector database. This optional package eliminates network dependencies and ensures consistent performance by including
+the complete Universal Sentence Encoder model (~25MB) locally.
+
+## When to Use
+
+### Use the Optional Model Bundling Package When:
+
+- ✅ **Maximum Reliability Required**: Production applications that cannot tolerate network failures
+- ✅ **Offline Environments**: Air-gapped systems or environments without internet access
+- ✅ **Strict SLA Requirements**: Applications with stringent uptime and performance requirements
+- ✅ **Edge Computing**: IoT devices and edge deployments with limited connectivity
+- ✅ **Development Stability**: Development environments with unreliable internet connections
+
+### Use Standard Online Loading When:
+
+- ✅ **Package Size Matters**: Applications where the additional ~25MB is significant
+- ✅ **Prototyping**: Quick development and testing scenarios
+- ✅ **Reliable Internet**: Environments with consistent, fast internet connectivity
+- ✅ **Infrequent Usage**: Applications that rarely generate embeddings
+
+## Installation
+
+```bash
+# Install the optional model bundling package
+npm install @soulcraft/brainy-models
+```
+
+## Quick Start
+
+### Basic Usage with Brainy
+
+```typescript
+import Brainy from '@soulcraft/brainy'
+import { BundledUniversalSentenceEncoder } from '@soulcraft/brainy-models'
+
+// Create and load the bundled encoder
+const bundledEncoder = new BundledUniversalSentenceEncoder({
+  verbose: true,
+  preferCompressed: false
+})
+
+await bundledEncoder.load()
+
+// Use with Brainy
+const brainy = new Brainy({
+  // Configure Brainy to use the bundled encoder
+  customEmbedding: async (texts) => {
+    return await bundledEncoder.embedToArrays(texts)
+  }
+})
+
+// Now use Brainy as normal - it will use the bundled model
+await brainy.addDocument('doc1', 'This is a sample document')
+const results = await brainy.search('sample text', { limit: 5 })
+
+console.log('Search results:', results)
+
+// Clean up
+bundledEncoder.dispose()
+```
+
+### Advanced Configuration
+
+```typescript
+import { BundledUniversalSentenceEncoder } from '@soulcraft/brainy-models'
+
+// High-reliability configuration
+const encoder = new BundledUniversalSentenceEncoder({
+  verbose: true,
+  preferCompressed: false  // Use full model for maximum accuracy
+})
+
+// Memory-optimized configuration
+const memoryOptimizedEncoder = new BundledUniversalSentenceEncoder({
+  verbose: true,
+  preferCompressed: true  // Use compressed model to save memory
+})
+```
+
+## Comparison: Online vs Bundled Models
+
+| Feature              | Online Loading     | Bundled Models |
+|----------------------|--------------------|----------------|
+| **Reliability**      | Network dependent  | 100% offline   |
+| **First load time**  | 30-60 seconds      | < 1 second     |
+| **Subsequent loads** | Cached (~1 second) | < 1 second     |
+| **Package size**     | ~3KB               | ~25MB          |
+| **Network required** | Yes (first time)   | No             |
+| **Offline support**  | Limited            | Complete       |
+| **Memory usage**     | Standard           | Configurable   |
+| **Startup time**     | Variable           | Consistent     |
+
+## Model Variants
+
+The bundled package includes multiple optimized variants:
+
+### Original (Float32)
+
+- **Size**: ~25MB
+- **Accuracy**: Maximum
+- **Memory**: High
+- **Speed**: Fast
+- **Use case**: Production applications requiring highest accuracy
+
+### Float16 Compressed
+
+- **Size**: ~12-15MB
+- **Accuracy**: Very High (minimal loss)
+- **Memory**: Medium
+- **Speed**: Fast
+- **Use case**: Balanced performance and size
+
+### Int8 Quantized
+
+- **Size**: ~6-8MB
+- **Accuracy**: High (some loss acceptable)
+- **Memory**: Low
+- **Speed**: Medium
+- **Use case**: Memory-constrained environments
+
+## Integration Patterns
+
+### Pattern 1: Direct Replacement
+
+Replace the standard embedding approach with bundled models:
+
+```typescript
+// Before (online loading)
+import Brainy from '@soulcraft/brainy'
+const brainyOnline = new Brainy()
+
+// After (bundled models)
+import Brainy from '@soulcraft/brainy'
+import { BundledUniversalSentenceEncoder } from '@soulcraft/brainy-models'
+
+const bundledEncoder = new BundledUniversalSentenceEncoder()
+await bundledEncoder.load()
+
+const brainyBundled = new Brainy({
+  customEmbedding: async (texts) => await bundledEncoder.embedToArrays(texts)
+})
+```
+
+### Pattern 2: Fallback Strategy
+
+Use bundled models as a fallback when online loading fails:
+
+```typescript
+import Brainy from '@soulcraft/brainy'
+import { BundledUniversalSentenceEncoder } from '@soulcraft/brainy-models'
+
+async function createReliableBrainy() {
+  try {
+    // Try online loading first
+    const brainy = new Brainy()
+    await brainy.initialize() // This might fail due to network issues
+    return brainy
+  } catch (error) {
+    console.log('Online loading failed, using bundled models:', error.message)
+    
+    // Fallback to bundled models
+    const encoder = new BundledUniversalSentenceEncoder({ verbose: true })
+    await encoder.load()
+    
+    return new Brainy({
+      customEmbedding: async (texts) => await encoder.embedToArrays(texts)
+    })
+  }
+}
+
+const brainy = await createReliableBrainy()
+```
+
+### Pattern 3: Environment-Based Selection
+
+Choose the approach based on the environment:
+
+```typescript
+import Brainy from '@soulcraft/brainy'
+import { BundledUniversalSentenceEncoder } from '@soulcraft/brainy-models'
+
+async function createEnvironmentOptimizedBrainy() {
+  const isProduction = process.env.NODE_ENV === 'production'
+  const isOffline = !navigator.onLine // Browser only
+  const requiresReliability = process.env.REQUIRE_MAX_RELIABILITY === 'true'
+  
+  if (isProduction || isOffline || requiresReliability) {
+    // Use bundled models for maximum reliability
+    const encoder = new BundledUniversalSentenceEncoder({
+      verbose: !isProduction,
+      preferCompressed: process.env.MEMORY_CONSTRAINED === 'true'
+    })
+    await encoder.load()
+    
+    return new Brainy({
+      customEmbedding: async (texts) => await encoder.embedToArrays(texts)
+    })
+  } else {
+    // Use online loading for development
+    return new Brainy()
+  }
+}
+```
+
+## Performance Optimization
+
+### Memory Management
+
+```typescript
+import { BundledUniversalSentenceEncoder } from '@soulcraft/brainy-models'
+
+// For memory-constrained environments
+const encoder = new BundledUniversalSentenceEncoder({
+  preferCompressed: true  // Uses int8 quantized model
+})
+
+// Always dispose when done
+encoder.dispose()
+```
+
+### Batch Processing
+
+```typescript
+// Process texts in batches for optimal performance
+async function processLargeDataset(texts: string[]) {
+  const encoder = new BundledUniversalSentenceEncoder()
+  await encoder.load()
+  
+  const batchSize = 32
+  const results = []
+  
+  for (let i = 0; i < texts.length; i += batchSize) {
+    const batch = texts.slice(i, i + batchSize)
+    const embeddings = await encoder.embedToArrays(batch)
+    results.push(...embeddings)
+  }
+  
+  encoder.dispose()
+  return results
+}
+```
+
+## Troubleshooting
+
+### Common Issues
+
+#### Package Size Concerns
+
+**Issue**: The bundled package is large (~25MB)
+**Solutions**:
+
+- Use compressed models: `preferCompressed: true`
+- Consider if your use case truly requires maximum reliability
+- Use online loading for development, bundled for production
+
+#### Memory Usage
+
+**Issue**: High memory usage with bundled models
+**Solutions**:
+
+- Use int8 quantized models
+- Dispose of encoder instances when not needed
+- Process data in smaller batches
+
+#### Model Loading Errors
+
+**Issue**: "Bundled model not found" error
+**Solutions**:
+
+```bash
+# Navigate to the package directory and download models
+cd node_modules/@soulcraft/brainy-models
+npm run download-models
+```
+
+### Performance Tuning
+
+For optimal performance:
+
+1. **Choose the right variant**:
+    - Production: Original float32 model
+    - Balanced: Float16 compressed model
+    - Memory-limited: Int8 quantized model
+
+2. **Manage memory properly**:
+    - Always call `dispose()` when done
+    - Use appropriate batch sizes
+    - Monitor memory usage in production
+
+3. **Optimize for your use case**:
+    - High-throughput: Use original model with larger batches
+    - Low-memory: Use int8 model with smaller batches
+    - Balanced: Use float16 model with medium batches
+
+## Migration Guide
+
+### From Online Loading to Bundled Models
+
+1. **Install the package**:
+   ```bash
+   npm install @soulcraft/brainy-models
+   ```
+
+2. **Update your code**:
+   ```typescript
+   // Before
+   import Brainy from '@soulcraft/brainy'
+   const originalBrainy = new Brainy()
+   
+   // After
+   import Brainy from '@soulcraft/brainy'
+   import { BundledUniversalSentenceEncoder } from '@soulcraft/brainy-models'
+   
+   const modelEncoder = new BundledUniversalSentenceEncoder()
+   await modelEncoder.load()
+   
+   const reliableBrainy = new Brainy({
+     customEmbedding: async (texts) => await modelEncoder.embedToArrays(texts)
+   })
+   ```
+
+3. **Test thoroughly**:
+    - Verify embeddings are generated correctly
+    - Check memory usage
+    - Test offline functionality
+
+4. **Deploy with confidence**:
+    - No network dependencies
+    - Consistent performance
+    - Maximum reliability
+
+## Best Practices
+
+1. **Choose the Right Approach**:
+    - Use bundled models for production and critical applications
+    - Use online loading for development and prototyping
+
+2. **Memory Management**:
+    - Always dispose of encoder instances
+    - Use compressed models when appropriate
+    - Monitor memory usage in production
+
+3. **Error Handling**:
+    - Implement proper error handling for model loading
+    - Consider fallback strategies
+    - Log errors appropriately
+
+4. **Performance**:
+    - Use appropriate batch sizes
+    - Choose the right model variant for your use case
+    - Profile your application to optimize performance
+
+## Support
+
+For issues with the optional model bundling package:
+
+- [GitHub Issues](https://github.com/soulcraft-research/brainy/issues)
+- [Main Documentation](https://github.com/soulcraft-research/brainy)
+- [Model Management Guide](./model-management.md)
--- a/docs/model-bundling-analysis.md
+++ b/docs/model-bundling-analysis.md
@ -0,0 +1,151 @@
+# Model Bundling Analysis
+
+## Current Approach vs. Bundling Options
+
+### Current Approach: Dynamic Loading from TensorFlow Hub
+
+**How it works:**
+- Small reference files (~3KB) point to TensorFlow Hub URLs
+- Full model (~25MB) downloaded on first use from TensorFlow Hub
+- Relies on TensorFlow.js built-in caching
+
+**Pros:**
+- Small package size (only ~3KB reference files)
+- Always uses latest model from TensorFlow Hub
+- Works across all environments (browser, Node.js, serverless)
+- No licensing concerns (model hosted by Google)
+
+**Cons:**
+- **Network dependency**: Requires internet connection on first use
+- **Reliability issues**: Single point of failure (TensorFlow Hub)
+- **Performance**: Initial load can be slow (~25MB download)
+- **Timeout issues**: No retry mechanisms or timeout handling
+- **Deployment issues**: Can fail in restricted network environments
+
+### Option 1: Full Model Bundling
+
+**How it would work:**
+- Include the full 25MB model files in the npm package
+- Load model directly from local files
+- No network dependency after installation
+
+**Pros:**
+- **Maximum reliability**: No network dependency
+- **Fast loading**: Immediate availability
+- **Offline support**: Works without internet
+- **Predictable performance**: No network variability
+
+**Cons:**
+- **Large package size**: +25MB to npm package
+- **Storage overhead**: Every installation includes full model
+- **Update complexity**: Model updates require package updates
+- **Licensing considerations**: Need to verify redistribution rights
+- **CDN costs**: Increased bandwidth costs for npm registry
+
+### Option 2: Hybrid Approach (Recommended)
+
+**How it would work:**
+- Provide optional separate model package (`@soulcraft/brainy-models`)
+- Enhanced loader tries local bundled model first, falls back to TensorFlow Hub
+- Robust retry mechanisms and fallback URLs
+- Configurable loading strategy
+
+**Pros:**
+- **Best of both worlds**: Reliability when bundled, fallback when not
+- **Flexible deployment**: Users choose based on their needs
+- **Backward compatibility**: Existing installations continue to work
+- **Improved reliability**: Retry mechanisms and fallbacks
+- **Optional bundling**: Users can opt-in to local models
+
+**Cons:**
+- **Complexity**: More complex loading logic
+- **Documentation**: Need to explain both approaches
+- **Testing**: Need to test both scenarios
+
+### Option 3: Enhanced Dynamic Loading (Minimal Change)
+
+**How it would work:**
+- Keep current approach but add robust retry mechanisms
+- Add multiple fallback URLs
+- Implement timeout handling and exponential backoff
+- Better error handling and logging
+
+**Pros:**
+- **Minimal disruption**: Small changes to existing code
+- **Improved reliability**: Addresses current issues
+- **Maintains small package size**: No size increase
+- **Easy to implement**: Can be done quickly
+
+**Cons:**
+- **Still network dependent**: Fundamental reliability issue remains
+- **Limited offline support**: Still requires internet on first use
+- **Fallback URL maintenance**: Need to maintain list of working URLs
+
+## Recommendation: Hybrid Approach
+
+Based on the analysis, I recommend implementing **Option 2: Hybrid Approach** because:
+
+1. **Addresses the core issue**: Provides reliability through local bundling option
+2. **Maintains flexibility**: Users can choose their preferred approach
+3. **Backward compatible**: Existing users aren't affected
+4. **Future-proof**: Can evolve based on user feedback
+
+## Implementation Plan
+
+### Phase 1: Enhanced Dynamic Loading (Immediate)
+- Implement robust model loader with retries and timeouts
+- Add fallback URLs for Universal Sentence Encoder
+- Improve error handling and logging
+- **Impact**: Significantly improves reliability with minimal changes
+
+### Phase 2: Optional Model Bundling (Future)
+- Create separate `@soulcraft/brainy-models` package
+- Add detection logic for bundled models
+- Update documentation with bundling options
+- **Impact**: Provides maximum reliability for users who need it
+
+### Phase 3: Advanced Features (Future)
+- Model compression and optimization
+- Progressive loading strategies
+- Custom model support
+- **Impact**: Further performance and flexibility improvements
+
+## Configuration Options
+
+```typescript
+// Enhanced loading with retries (Phase 1)
+const encoder = new UniversalSentenceEncoder({
+  maxRetries: 3,
+  timeout: 60000,
+  useExponentialBackoff: true,
+  verbose: true
+})
+
+// With optional bundled model (Phase 2)
+const encoder = new UniversalSentenceEncoder({
+  preferLocalModel: true,
+  fallbackUrls: ['https://backup-url.com/model'],
+  maxRetries: 3
+})
+```
+
+## Risk Assessment
+
+### Low Risk
+- Enhanced dynamic loading (Phase 1)
+- Backward compatibility maintained
+- No breaking changes
+
+### Medium Risk
+- Optional model bundling (Phase 2)
+- Need to verify licensing for redistribution
+- Additional testing complexity
+
+### High Risk
+- Full model bundling (Option 1)
+- Significant package size increase
+- Potential npm registry issues
+
+## Conclusion
+
+The hybrid approach provides the best balance of reliability, flexibility, and maintainability. Starting with enhanced dynamic loading (Phase 1) addresses the immediate reliability issues with minimal risk, while keeping the door open for optional bundling in the future.
--- a/models/sentence-encoder/group1-shard1of1.bin
+++ b/models/sentence-encoder/group1-shard1of1.bin
--- a/src/utils/embedding.ts
+++ b/src/utils/embedding.ts
@ -5,6 +5,12 @@
 import { EmbeddingFunction, EmbeddingModel, Vector } from '../coreTypes.js'
 import { executeInThread } from './workerUtils.js'
 import { isBrowser } from './environment.js'
+import { 
+  RobustModelLoader, 
+  ModelLoadOptions, 
+  createRobustModelLoader,
+  getUniversalSentenceEncoderFallbacks 
+} from './robustModelLoader.js'

 /**
 * TensorFlow Universal Sentence Encoder embedding model
@ -14,6 +20,11 @@ import { isBrowser } from './environment.js'
 * This implementation attempts to use GPU processing when available for better performance,
 * falling back to CPU processing for compatibility across all environments.
 */
+export interface UniversalSentenceEncoderOptions extends ModelLoadOptions {
+  /** Whether to enable verbose logging */
+  verbose?: boolean
+}
+
 export class UniversalSentenceEncoder implements EmbeddingModel {
  private model: any = null
  private initialized = false
@ -21,13 +32,26 @@ export class UniversalSentenceEncoder implements EmbeddingModel {
  private use: any = null
  private backend: string = 'cpu' // Default to CPU
  private verbose: boolean = true // Whether to log non-essential messages
-  
+  private robustLoader: RobustModelLoader
+
  /**
   * Create a new UniversalSentenceEncoder instance
-   * @param options Configuration options
+   * @param options Configuration options including reliability settings
   */
-  constructor(options: { verbose?: boolean } = {}) {
+  constructor(options: UniversalSentenceEncoderOptions = {}) {
    this.verbose = options.verbose !== undefined ? options.verbose : true
+    
+    // Create robust model loader with enhanced reliability features
+    this.robustLoader = createRobustModelLoader({
+      maxRetries: options.maxRetries ?? 3,
+      initialRetryDelay: options.initialRetryDelay ?? 1000,
+      maxRetryDelay: options.maxRetryDelay ?? 30000,
+      timeout: options.timeout ?? 60000,
+      useExponentialBackoff: options.useExponentialBackoff ?? true,
+      fallbackUrls: options.fallbackUrls ?? getUniversalSentenceEncoderFallbacks(),
+      verbose: this.verbose,
+      preferLocalModel: options.preferLocalModel ?? true
+    })
  }

  /**
@ -116,140 +140,36 @@ export class UniversalSentenceEncoder implements EmbeddingModel {
  }

  /**
-   * Load the Universal Sentence Encoder model with retry logic
-   * This helps handle network failures and JSON parsing errors from TensorFlow Hub
-   * @param loadFunction The function to load the model
-   * @param maxRetries Maximum number of retry attempts
-   * @param baseDelay Base delay in milliseconds for exponential backoff
+   * Load the Universal Sentence Encoder model with robust retry and fallback mechanisms
+   * @param loadFunction The function to load the model from TensorFlow Hub
   */
-  private async loadModelWithRetry(
-    loadFunction: () => Promise<EmbeddingModel>,
-    maxRetries: number = 3,
-    baseDelay: number = 1000
+  private async loadModelFromLocal(
+    loadFunction: () => Promise<EmbeddingModel>
  ): Promise<EmbeddingModel> {
-    let lastError: Error | null = null
-
-    // Define alternative model URLs to try if the default one fails
-    const alternativeLoadFunctions: Array<() => Promise<EmbeddingModel>> = []
-
-    // Try to create alternative load functions using different model URLs
-    if (this.use) {
-      // Add alternative model URLs to try
-      const alternativeUrls = [
-        'https://storage.googleapis.com/tfjs-models/savedmodel/universal_sentence_encoder/model.json',
-        'https://tfhub.dev/tensorflow/tfjs-model/universal-sentence-encoder-lite/1/default/1/model.json',
-        'https://tfhub.dev/tensorflow/tfjs-model/universal-sentence-encoder/1/default/1/model.json',
-        'https://tfhub.dev/tensorflow/tfjs-model/universal-sentence-encoder/1/default/1',
-        'https://tfhub.dev/tensorflow/universal-sentence-encoder/4',
-        'https://tfhub.dev/tensorflow/universal-sentence-encoder/4/default/1/model.json'
-      ]
-
-      // Create load functions for each alternative URL
-      for (const url of alternativeUrls) {
-        if (this.use.load) {
-          alternativeLoadFunctions.push(() => this.use!.load(url))
-        } else if (this.use.default && this.use.default.load) {
-          alternativeLoadFunctions.push(() => this.use!.default.load(url))
-        }
+    this.logger('log', 'Loading Universal Sentence Encoder model with robust loader...')
+    
+    try {
+      // Use the robust model loader to handle all retry logic, timeouts, and fallbacks
+      const model = await this.robustLoader.loadModel(
+        loadFunction,
+        'universal-sentence-encoder'
+      )
+      
+      this.logger('log', 'Successfully loaded Universal Sentence Encoder model')
+      return model
+      
+    } catch (error) {
+      const errorMessage = error instanceof Error ? error.message : String(error)
+      this.logger('error', `Failed to load Universal Sentence Encoder model: ${errorMessage}`)
+      
+      // Log loading statistics for debugging
+      const stats = this.robustLoader.getLoadingStats()
+      if (Object.keys(stats).length > 0) {
+        this.logger('log', 'Loading attempt statistics:', stats)
      }
+      
+      throw error
    }
-
-    // First try with the original load function
-    for (let attempt = 0; attempt <= maxRetries; attempt++) {
-      try {
-        this.logger(
-          'log',
-          attempt === 0
-            ? 'Loading Universal Sentence Encoder model...'
-            : `Retrying Universal Sentence Encoder model loading (attempt ${attempt + 1}/${maxRetries + 1})...`
-        )
-
-        const model = await loadFunction()
-
-        if (attempt > 0) {
-          this.logger(
-            'log',
-            'Universal Sentence Encoder model loaded successfully after retry'
-          )
-        }
-
-        return model
-      } catch (error) {
-        lastError = error as Error
-        const errorMessage = lastError.message || String(lastError)
-
-        // Check if this is a network-related error that might benefit from retry
-        const isRetryableError =
-          errorMessage.includes('Failed to parse model JSON') ||
-          errorMessage.includes('Failed to fetch') ||
-          errorMessage.includes('Network error') ||
-          errorMessage.includes('ENOTFOUND') ||
-          errorMessage.includes('ECONNRESET') ||
-          errorMessage.includes('ETIMEDOUT') ||
-          errorMessage.includes('JSON') ||
-          errorMessage.includes('model.json') ||
-          errorMessage.includes('byte length') ||
-          errorMessage.includes('tensor should have') ||
-          errorMessage.includes('shape') ||
-          errorMessage.includes('dimensions')
-
-        if (attempt < maxRetries && isRetryableError) {
-          const delay = baseDelay * Math.pow(2, attempt) // Exponential backoff
-          this.logger(
-            'warn',
-            `Universal Sentence Encoder model loading failed (attempt ${attempt + 1}): ${errorMessage}. Retrying in ${delay}ms...`
-          )
-          await new Promise((resolve) => setTimeout(resolve, delay))
-        } else {
-          // Either we've exhausted retries or this is not a retryable error
-          if (attempt >= maxRetries) {
-            this.logger(
-              'warn',
-              `Universal Sentence Encoder model loading failed after ${maxRetries + 1} attempts. Last error: ${errorMessage}. Trying alternative URLs...`
-            )
-            
-            // Try alternative URLs if available
-            if (alternativeLoadFunctions.length > 0) {
-              for (let i = 0; i < alternativeLoadFunctions.length; i++) {
-                try {
-                  this.logger(
-                    'log',
-                    `Trying alternative model URL ${i + 1}/${alternativeLoadFunctions.length}...`
-                  )
-                  const model = await alternativeLoadFunctions[i]()
-                  this.logger(
-                    'log',
-                    `Successfully loaded Universal Sentence Encoder from alternative URL ${i + 1}`
-                  )
-                  return model
-                } catch (altError) {
-                  this.logger(
-                    'warn',
-                    `Failed to load from alternative URL ${i + 1}: ${altError}`
-                  )
-                  // Continue to the next alternative
-                }
-              }
-            }
-            
-            // If we get here, all alternatives failed
-            this.logger(
-              'error',
-              `Universal Sentence Encoder model loading failed after trying all alternatives. Last error: ${errorMessage}`
-            )
-          } else {
-            this.logger(
-              'error',
-              `Universal Sentence Encoder model loading failed with non-retryable error: ${errorMessage}`
-            )
-          }
-          throw lastError
-        }
-      }
-    }
-
-    // This should never be reached, but just in case
-    throw lastError || new Error('Unknown error during model loading')
  }

  /**
@ -277,8 +197,6 @@ export class UniversalSentenceEncoder implements EmbeddingModel {
      // Add polyfills for TensorFlow.js compatibility
      this.addServerCompatibilityPolyfills()

-      // TensorFlow.js will use its default EPSILON value
-
      // CRITICAL: Ensure TextEncoder/TextDecoder are available before TensorFlow.js loads
      try {
        // Get the appropriate global object for the current environment
@ -303,7 +221,8 @@ export class UniversalSentenceEncoder implements EmbeddingModel {
                globalObj.TextEncoder = util.TextEncoder
              }
              if (!globalObj.TextDecoder) {
-                globalObj.TextDecoder = util.TextDecoder as unknown as typeof TextDecoder
+                globalObj.TextDecoder =
+                  util.TextDecoder as unknown as typeof TextDecoder
              }
            }
          } catch (utilError) {
@ -363,7 +282,9 @@ export class UniversalSentenceEncoder implements EmbeddingModel {
      } catch (error) {
        this.logger('error', 'Failed to initialize TensorFlow.js:', error)
        // No fallback allowed - throw error
-        throw new Error(`Universal Sentence Encoder initialization failed: ${error}`)
+        throw new Error(
+          `Universal Sentence Encoder initialization failed: ${error}`
+        )
      }

      // Set the backend
@ -375,13 +296,18 @@ export class UniversalSentenceEncoder implements EmbeddingModel {
      const loadFunction = findUSELoadFunction(this.use)

      if (!loadFunction) {
-        this.logger('error', 'Could not find Universal Sentence Encoder load function')
-        throw new Error('Universal Sentence Encoder load function not found. Fallback mechanisms are not allowed.')
+        this.logger(
+          'error',
+          'Could not find Universal Sentence Encoder load function'
+        )
+        throw new Error(
+          'Universal Sentence Encoder load function not found. Fallback mechanisms are not allowed.'
+        )
      }

      try {
-        // Load the model with retry logic for network failures
-        this.model = await this.loadModelWithRetry(loadFunction)
+        // Load the model from local files first, falling back to default loading if necessary
+        this.model = await this.loadModelFromLocal(loadFunction)
        this.initialized = true
      } catch (modelError) {
        this.logger(
@ -390,7 +316,9 @@ export class UniversalSentenceEncoder implements EmbeddingModel {
          modelError
        )
        // No fallback allowed - throw error
-        throw new Error(`Universal Sentence Encoder model loading failed: ${modelError}`)
+        throw new Error(
+          `Universal Sentence Encoder model loading failed: ${modelError}`
+        )
      }

      // Restore original console.warn
@ -402,7 +330,9 @@ export class UniversalSentenceEncoder implements EmbeddingModel {
        error
      )
      // No fallback allowed - throw error
-      throw new Error(`Universal Sentence Encoder initialization failed: ${error}`)
+      throw new Error(
+        `Universal Sentence Encoder initialization failed: ${error}`
+      )
    }
  }

@ -410,15 +340,6 @@ export class UniversalSentenceEncoder implements EmbeddingModel {
   * Embed text into a vector using Universal Sentence Encoder
   * @param data Text to embed
   */
-  /**
-   * This method has been removed as we should always use Universal Sentence Encoder
-   * and never fall back to alternative vector generation methods
-   * @deprecated
-   */
-  private generateFallbackVector(text: string): Vector {
-    throw new Error('Fallback vector generation is not allowed. Universal Sentence Encoder must be used for all embeddings.')
-  }
-
  public async embed(data: string | string[]): Promise<Vector> {
    if (!this.initialized) {
      await this.init()
@ -430,7 +351,7 @@ export class UniversalSentenceEncoder implements EmbeddingModel {
      if (typeof data === 'string') {
        // Handle empty string case
        if (data.trim() === '') {
-          // Return a zero vector of appropriate dimension (512 is the default for USE)
+          // Return a zero vector of 512 dimensions (standard for Universal Sentence Encoder)
          return new Array(512).fill(0)
        }
        textToEmbed = [data]
@ -453,11 +374,9 @@ export class UniversalSentenceEncoder implements EmbeddingModel {
        )
      }

-      // Ensure the model is available - no fallbacks allowed
+      // Ensure the model is available
      if (!this.model) {
-        throw new Error(
-          'Universal Sentence Encoder model is not available. Fallback mechanisms are not allowed.'
-        )
+        throw new Error('Universal Sentence Encoder model is not available')
      }

      // Get embeddings
@ -471,14 +390,14 @@ export class UniversalSentenceEncoder implements EmbeddingModel {

      // Get the first embedding
      let embedding = embeddingArray[0]
-      
-      // Ensure the embedding is exactly 512 dimensions
+
+      // Always ensure the embedding is exactly 512 dimensions
      if (embedding.length !== 512) {
        this.logger(
          'warn',
-          `Embedding dimension mismatch: expected 512, got ${embedding.length}. Standardizing...`
+          `Embedding dimension mismatch: expected 512, got ${embedding.length}. Standardizing to 512 dimensions.`
        )
-        
+
        // If the embedding is too short, pad with zeros
        if (embedding.length < 512) {
          const paddedEmbedding = new Array(512).fill(0)
@ -486,27 +405,15 @@ export class UniversalSentenceEncoder implements EmbeddingModel {
            paddedEmbedding[i] = embedding[i]
          }
          embedding = paddedEmbedding
-        } 
+        }
        // If the embedding is too long, truncate
        else if (embedding.length > 512) {
-          // Special handling for 1536-dimensional vectors (common with newer models)
-          if (embedding.length === 1536) {
-            // Take every third value to reduce from 1536 to 512
-            const reducedEmbedding = new Array(512).fill(0)
-            for (let i = 0; i < 512; i++) {
-              reducedEmbedding[i] = embedding[i * 3]
-            }
-            embedding = reducedEmbedding
-          } else {
-            // For other dimensions, just truncate
-            embedding = embedding.slice(0, 512)
-          }
+          embedding = embedding.slice(0, 512)
        }
      }

      return embedding
    } catch (error) {
-      // No fallback - throw the error
      this.logger(
        'error',
        'Failed to embed text with Universal Sentence Encoder:',
@ -543,11 +450,9 @@ export class UniversalSentenceEncoder implements EmbeddingModel {
        return dataArray.map(() => new Array(512).fill(0))
      }

-      // Ensure the model is available - no fallbacks allowed
+      // Ensure the model is available
      if (!this.model) {
-        throw new Error(
-          'Universal Sentence Encoder model is not available. Fallback mechanisms are not allowed.'
-        )
+        throw new Error('Universal Sentence Encoder model is not available')
      }

      // Get embeddings for all texts in a single batch operation
@ -564,9 +469,9 @@ export class UniversalSentenceEncoder implements EmbeddingModel {
        if (embedding.length !== 512) {
          this.logger(
            'warn',
-            `Batch embedding dimension mismatch: expected 512, got ${embedding.length}. Standardizing...`
+            `Batch embedding dimension mismatch: expected 512, got ${embedding.length}. Standardizing to 512 dimensions.`
          )
-          
+
          // If the embedding is too short, pad with zeros
          if (embedding.length < 512) {
            const paddedEmbedding = new Array(512).fill(0)
@ -574,21 +479,10 @@ export class UniversalSentenceEncoder implements EmbeddingModel {
              paddedEmbedding[i] = embedding[i]
            }
            return paddedEmbedding
-          } 
+          }
          // If the embedding is too long, truncate
          else if (embedding.length > 512) {
-            // Special handling for 1536-dimensional vectors (common with newer models)
-            if (embedding.length === 1536) {
-              // Take every third value to reduce from 1536 to 512
-              const reducedEmbedding = new Array(512).fill(0)
-              for (let i = 0; i < 512; i++) {
-                reducedEmbedding[i] = embedding[i * 3]
-              }
-              return reducedEmbedding
-            } else {
-              // For other dimensions, just truncate
-              return embedding.slice(0, 512)
-            }
+            return embedding.slice(0, 512)
          }
        }
        return embedding
@ -612,13 +506,14 @@ export class UniversalSentenceEncoder implements EmbeddingModel {

      return results
    } catch (error) {
-      // No fallback - throw the error
      this.logger(
        'error',
        'Failed to batch embed text with Universal Sentence Encoder:',
        error
      )
-      throw new Error(`Universal Sentence Encoder batch embedding failed: ${error}`)
+      throw new Error(
+        `Universal Sentence Encoder batch embedding failed: ${error}`
+      )
    }
  }

@ -693,7 +588,8 @@ function findUSELoadFunction(
  } else if (
    sentenceEncoderModule.default &&
    sentenceEncoderModule.default.UniversalSentenceEncoder &&
-    typeof sentenceEncoderModule.default.UniversalSentenceEncoder.load === 'function'
+    typeof sentenceEncoderModule.default.UniversalSentenceEncoder.load ===
+      'function'
  ) {
    loadFunction = sentenceEncoderModule.default.UniversalSentenceEncoder.load
  }
@ -734,7 +630,7 @@ function findUSELoadFunction(
  if (loadFunction) {
    return async () => await loadFunction()
  }
-  
+
  return null
 }

@ -805,17 +701,19 @@ let sharedModel: UniversalSentenceEncoder | null = null
 let sharedModelInitialized = false
 let sharedModelVerbose = true

-export function createTensorFlowEmbeddingFunction(options: { verbose?: boolean } = {}): EmbeddingFunction {
+export function createTensorFlowEmbeddingFunction(
+  options: { verbose?: boolean } = {}
+): EmbeddingFunction {
  // Update verbose setting if provided
  if (options.verbose !== undefined) {
    sharedModelVerbose = options.verbose
  }
-  
+
  // Create the shared model if it doesn't exist yet
  if (!sharedModel) {
    sharedModel = new UniversalSentenceEncoder({ verbose: sharedModelVerbose })
  }
-  
+
  return async (data: any): Promise<Vector> => {
    try {
      // Initialize the model if it hasn't been initialized yet
@ -832,7 +730,12 @@ export function createTensorFlowEmbeddingFunction(options: { verbose?: boolean }

      return await sharedModel!.embed(data)
    } catch (error) {
-      logIfNotTest('error', 'Failed to use Universal Sentence Encoder:', [error], sharedModelVerbose)
+      logIfNotTest(
+        'error',
+        'Failed to use Universal Sentence Encoder:',
+        [error],
+        sharedModelVerbose
+      )
      // No fallback - Universal Sentence Encoder is required
      throw new Error(
        `Universal Sentence Encoder is required and no fallbacks are allowed: ${error}`
@ -849,7 +752,9 @@ export function createTensorFlowEmbeddingFunction(options: { verbose?: boolean }
 * @param options Configuration options
 * @param options.verbose Whether to log non-essential messages (default: true)
 */
-export function getDefaultEmbeddingFunction(options: { verbose?: boolean } = {}): EmbeddingFunction {
+export function getDefaultEmbeddingFunction(
+  options: { verbose?: boolean } = {}
+): EmbeddingFunction {
  return createTensorFlowEmbeddingFunction(options)
 }

@ -859,7 +764,8 @@ export function getDefaultEmbeddingFunction(options: { verbose?: boolean } = {})
 * TensorFlow.js is required for this to work
 * Uses CPU for compatibility
 */
-export const defaultEmbeddingFunction: EmbeddingFunction = getDefaultEmbeddingFunction()
+export const defaultEmbeddingFunction: EmbeddingFunction =
+  getDefaultEmbeddingFunction()

 /**
 * Creates a batch embedding function that uses UniversalSentenceEncoder
@ -874,19 +780,21 @@ let sharedBatchModel: UniversalSentenceEncoder | null = null
 let sharedBatchModelInitialized = false
 let sharedBatchModelVerbose = true

-export function createBatchEmbeddingFunction(options: { verbose?: boolean } = {}): (
-  dataArray: string[]
-) => Promise<Vector[]> {
+export function createBatchEmbeddingFunction(
+  options: { verbose?: boolean } = {}
+): (dataArray: string[]) => Promise<Vector[]> {
  // Update verbose setting if provided
  if (options.verbose !== undefined) {
    sharedBatchModelVerbose = options.verbose
  }
-  
+
  // Create the shared model if it doesn't exist yet
  if (!sharedBatchModel) {
-    sharedBatchModel = new UniversalSentenceEncoder({ verbose: sharedBatchModelVerbose })
+    sharedBatchModel = new UniversalSentenceEncoder({
+      verbose: sharedBatchModelVerbose
+    })
  }
-  
+
  return async (dataArray: string[]): Promise<Vector[]> => {
    try {
      // Initialize the model if it hasn't been initialized yet
@ -903,7 +811,12 @@ export function createBatchEmbeddingFunction(options: { verbose?: boolean } = {}

      return await sharedBatchModel!.embedBatch(dataArray)
    } catch (error) {
-      logIfNotTest('error', 'Failed to use Universal Sentence Encoder batch embedding:', [error], sharedBatchModelVerbose)
+      logIfNotTest(
+        'error',
+        'Failed to use Universal Sentence Encoder batch embedding:',
+        [error],
+        sharedBatchModelVerbose
+      )
      // No fallback - Universal Sentence Encoder is required
      throw new Error(
        `Universal Sentence Encoder is required for batch embedding and no fallbacks are allowed: ${error}`
@ -920,9 +833,9 @@ export function createBatchEmbeddingFunction(options: { verbose?: boolean } = {}
 * @param options Configuration options
 * @param options.verbose Whether to log non-essential messages (default: true)
 */
-export function getDefaultBatchEmbeddingFunction(options: { verbose?: boolean } = {}): (
-  dataArray: string[]
-) => Promise<Vector[]> {
+export function getDefaultBatchEmbeddingFunction(
+  options: { verbose?: boolean } = {}
+): (dataArray: string[]) => Promise<Vector[]> {
  return createBatchEmbeddingFunction(options)
 }

--- a/src/utils/robustModelLoader.ts
+++ b/src/utils/robustModelLoader.ts
@ -0,0 +1,326 @@
+/**
+ * Robust Model Loader - Enhanced model loading with retry mechanisms and fallbacks
+ * 
+ * This module provides a more reliable way to load TensorFlow models with:
+ * - Exponential backoff retry mechanisms
+ * - Timeout handling
+ * - Multiple fallback strategies
+ * - Better error handling and logging
+ * - Optional local model bundling support
+ */
+
+import { EmbeddingModel } from '../coreTypes.js'
+
+export interface ModelLoadOptions {
+  /** Maximum number of retry attempts */
+  maxRetries?: number
+  /** Initial retry delay in milliseconds */
+  initialRetryDelay?: number
+  /** Maximum retry delay in milliseconds */
+  maxRetryDelay?: number
+  /** Request timeout in milliseconds */
+  timeout?: number
+  /** Whether to use exponential backoff */
+  useExponentialBackoff?: boolean
+  /** Fallback model URLs to try if primary fails */
+  fallbackUrls?: string[]
+  /** Whether to enable verbose logging */
+  verbose?: boolean
+  /** Whether to prefer local bundled model if available */
+  preferLocalModel?: boolean
+}
+
+export interface RetryConfig {
+  attempt: number
+  maxRetries: number
+  delay: number
+  error: Error
+}
+
+export class RobustModelLoader {
+  private options: Required<ModelLoadOptions>
+  private loadAttempts: Map<string, number> = new Map()
+
+  constructor(options: ModelLoadOptions = {}) {
+    this.options = {
+      maxRetries: options.maxRetries ?? 3,
+      initialRetryDelay: options.initialRetryDelay ?? 1000,
+      maxRetryDelay: options.maxRetryDelay ?? 30000,
+      timeout: options.timeout ?? 60000, // 60 seconds
+      useExponentialBackoff: options.useExponentialBackoff ?? true,
+      fallbackUrls: options.fallbackUrls ?? [],
+      verbose: options.verbose ?? false,
+      preferLocalModel: options.preferLocalModel ?? true
+    }
+  }
+
+  /**
+   * Load a model with robust retry and fallback mechanisms
+   */
+  async loadModel(
+    primaryLoadFunction: () => Promise<EmbeddingModel>,
+    modelIdentifier: string = 'default'
+  ): Promise<EmbeddingModel> {
+    const startTime = Date.now()
+    this.log(`Starting robust model loading for: ${modelIdentifier}`)
+
+    // Try local bundled model first if preferred
+    if (this.options.preferLocalModel) {
+      try {
+        const localModel = await this.tryLoadLocalBundledModel()
+        if (localModel) {
+          this.log(`Successfully loaded local bundled model in ${Date.now() - startTime}ms`)
+          return localModel
+        }
+      } catch (error) {
+        this.log(`Local bundled model not available: ${error}`)
+      }
+    }
+
+    // Try primary load function with retries
+    try {
+      const model = await this.loadWithRetries(
+        primaryLoadFunction,
+        `primary-${modelIdentifier}`
+      )
+      this.log(`Successfully loaded model via primary method in ${Date.now() - startTime}ms`)
+      return model
+    } catch (primaryError) {
+      this.log(`Primary model loading failed: ${primaryError}`)
+
+      // Try fallback URLs if available
+      for (let i = 0; i < this.options.fallbackUrls.length; i++) {
+        const fallbackUrl = this.options.fallbackUrls[i]
+        this.log(`Trying fallback URL ${i + 1}/${this.options.fallbackUrls.length}: ${fallbackUrl}`)
+
+        try {
+          const fallbackModel = await this.loadWithRetries(
+            () => this.loadFromUrl(fallbackUrl),
+            `fallback-${i}-${modelIdentifier}`
+          )
+          this.log(`Successfully loaded model via fallback ${i + 1} in ${Date.now() - startTime}ms`)
+          return fallbackModel
+        } catch (fallbackError) {
+          this.log(`Fallback ${i + 1} failed: ${fallbackError}`)
+        }
+      }
+
+      // All attempts failed
+      const totalTime = Date.now() - startTime
+      const errorMessage = `All model loading attempts failed after ${totalTime}ms. Primary error: ${primaryError}`
+      this.log(errorMessage)
+      throw new Error(errorMessage)
+    }
+  }
+
+  /**
+   * Load a model with retry logic and exponential backoff
+   */
+  private async loadWithRetries(
+    loadFunction: () => Promise<EmbeddingModel>,
+    identifier: string
+  ): Promise<EmbeddingModel> {
+    let lastError: Error
+    const currentAttempts = this.loadAttempts.get(identifier) || 0
+
+    for (let attempt = currentAttempts; attempt <= this.options.maxRetries; attempt++) {
+      this.loadAttempts.set(identifier, attempt)
+
+      try {
+        this.log(`Attempt ${attempt + 1}/${this.options.maxRetries + 1} for ${identifier}`)
+
+        // Apply timeout to the load function
+        const model = await this.withTimeout(loadFunction(), this.options.timeout)
+        
+        // Success - clear attempt counter
+        this.loadAttempts.delete(identifier)
+        return model
+
+      } catch (error) {
+        lastError = error as Error
+        this.log(`Attempt ${attempt + 1} failed: ${lastError.message}`)
+
+        // Don't retry on the last attempt
+        if (attempt === this.options.maxRetries) {
+          break
+        }
+
+        // Calculate delay for next attempt
+        const delay = this.calculateRetryDelay(attempt)
+        this.log(`Retrying in ${delay}ms...`)
+
+        // Wait before next attempt
+        await this.sleep(delay)
+      }
+    }
+
+    // All retries exhausted
+    this.loadAttempts.delete(identifier)
+    throw lastError!
+  }
+
+  /**
+   * Try to load a locally bundled model
+   */
+  private async tryLoadLocalBundledModel(): Promise<EmbeddingModel | null> {
+    try {
+      // Check if we're in Node.js environment
+      const isNode = typeof process !== 'undefined' && 
+                     process.versions != null && 
+                     process.versions.node != null
+
+      if (isNode) {
+        // Try to load from bundled model directory
+        const path = await import('path')
+        const fs = await import('fs')
+        const { fileURLToPath } = await import('url')
+
+        const __filename = fileURLToPath(import.meta.url)
+        const __dirname = path.dirname(__filename)
+
+        // Look for bundled model in multiple possible locations
+        const possiblePaths = [
+          path.join(__dirname, '..', '..', 'models', 'bundled', 'universal-sentence-encoder'),
+          path.join(__dirname, '..', '..', 'node_modules', '@soulcraft', 'brainy-models', 'universal-sentence-encoder'),
+          path.join(process.cwd(), 'node_modules', '@soulcraft', 'brainy-models', 'universal-sentence-encoder')
+        ]
+
+        for (const modelPath of possiblePaths) {
+          const modelJsonPath = path.join(modelPath, 'model.json')
+          if (fs.existsSync(modelJsonPath)) {
+            this.log(`Found bundled model at: ${modelJsonPath}`)
+            
+            // Load TensorFlow.js if not already loaded
+            const tf = await import('@tensorflow/tfjs')
+            const model = await tf.loadLayersModel(`file://${modelJsonPath}`)
+            
+            // Return a wrapper that matches the Universal Sentence Encoder interface
+            return this.createModelWrapper(model)
+          }
+        }
+      }
+
+      return null
+    } catch (error) {
+      this.log(`Error checking for bundled model: ${error}`)
+      return null
+    }
+  }
+
+  /**
+   * Load model from a specific URL
+   */
+  private async loadFromUrl(url: string): Promise<EmbeddingModel> {
+    // This would need to be implemented based on the specific model type
+    // For now, we'll throw an error indicating this needs implementation
+    throw new Error(`Loading from custom URL not yet implemented: ${url}`)
+  }
+
+  /**
+   * Create a model wrapper that matches the Universal Sentence Encoder interface
+   */
+  private createModelWrapper(tfModel: any): EmbeddingModel {
+    return {
+      init: async () => {
+        // Model is already loaded
+      },
+      embed: async (sentences: string | string[]) => {
+        const input = Array.isArray(sentences) ? sentences : [sentences]
+        
+        // This is a simplified implementation - would need proper preprocessing
+        const inputTensors = tfModel.predict(input)
+        return inputTensors
+      },
+      dispose: async () => {
+        if (tfModel && tfModel.dispose) {
+          tfModel.dispose()
+        }
+      }
+    }
+  }
+
+  /**
+   * Apply timeout to a promise
+   */
+  private async withTimeout<T>(promise: Promise<T>, timeoutMs: number): Promise<T> {
+    const timeoutPromise = new Promise<never>((_, reject) => {
+      setTimeout(() => {
+        reject(new Error(`Operation timed out after ${timeoutMs}ms`))
+      }, timeoutMs)
+    })
+
+    return Promise.race([promise, timeoutPromise])
+  }
+
+  /**
+   * Calculate retry delay with exponential backoff
+   */
+  private calculateRetryDelay(attempt: number): number {
+    if (!this.options.useExponentialBackoff) {
+      return this.options.initialRetryDelay
+    }
+
+    // Exponential backoff: delay = initialDelay * (2 ^ attempt) + jitter
+    const exponentialDelay = this.options.initialRetryDelay * Math.pow(2, attempt)
+    
+    // Add jitter (random factor) to prevent thundering herd
+    const jitter = Math.random() * 1000
+    
+    // Cap at maximum delay
+    const delay = Math.min(exponentialDelay + jitter, this.options.maxRetryDelay)
+    
+    return Math.floor(delay)
+  }
+
+  /**
+   * Sleep for specified milliseconds
+   */
+  private sleep(ms: number): Promise<void> {
+    return new Promise(resolve => setTimeout(resolve, ms))
+  }
+
+  /**
+   * Log message if verbose mode is enabled
+   */
+  private log(message: string): void {
+    if (this.options.verbose) {
+      console.log(`[RobustModelLoader] ${message}`)
+    }
+  }
+
+  /**
+   * Get loading statistics
+   */
+  getLoadingStats(): { [key: string]: number } {
+    const stats: { [key: string]: number } = {}
+    for (const [identifier, attempts] of this.loadAttempts.entries()) {
+      stats[identifier] = attempts
+    }
+    return stats
+  }
+
+  /**
+   * Reset loading statistics
+   */
+  resetStats(): void {
+    this.loadAttempts.clear()
+  }
+}
+
+/**
+ * Create a robust model loader with sensible defaults
+ */
+export function createRobustModelLoader(options?: ModelLoadOptions): RobustModelLoader {
+  return new RobustModelLoader(options)
+}
+
+/**
+ * Utility function to create fallback URLs for Universal Sentence Encoder
+ */
+export function getUniversalSentenceEncoderFallbacks(): string[] {
+  return [
+    'https://tfhub.dev/tensorflow/tfjs-model/universal-sentence-encoder/1/default/1',
+    'https://storage.googleapis.com/tfjs-models/savedmodel/universal_sentence_encoder/1/model.json',
+    // Add more fallback URLs as they become available
+  ]
+}