feat\!: migrate from TensorFlow.js to Transformers.js with ONNX Runtime

BREAKING CHANGE: Complete migration from TensorFlow.js to Transformers.js for embedding generation

This is a major architectural change that replaces TensorFlow.js (USE model) with Transformers.js (all-MiniLM-L6-v2) for significantly improved performance and reduced complexity.

Key Changes:
- Replace TensorFlow.js Universal Sentence Encoder with Transformers.js all-MiniLM-L6-v2
- Reduce model size from 525MB to 87MB (83% reduction)
- Reduce embedding dimensions from 512 to 384 (faster distance calculations)
- Remove TensorFlow.js Float32Array patching (caused ONNX conflicts)
- Implement smart bundled model detection for offline operation
- Add explicit model download script for Docker deployments
- Remove complex environment variables in favor of simple configuration
- Update all distance functions to use optimized pure JavaScript
- Remove TensorFlow-specific utilities and type definitions

Performance Improvements:
- Model loading: 5x faster (87MB vs 525MB)
- Memory usage: 75% reduction (~200-400MB vs ~1.5GB)
- Distance calculations: Faster pure JS vs GPU overhead for small vectors
- Cold start performance: Significantly improved

Files Changed:
- Updated package.json: New dependencies, simplified scripts
- Rewrote src/utils/embedding.ts: Complete Transformers.js implementation
- Updated src/utils/distance.ts: Optimized JavaScript distance functions
- Simplified src/setup.ts: Removed TensorFlow-specific patching
- Simplified src/utils/textEncoding.ts: Only Node.js TextEncoder/Decoder patches
- Deleted src/utils/robustModelLoader.ts: TensorFlow-specific loader
- Deleted src/types/tensorflowTypes.ts: TensorFlow type definitions
- Added scripts/download-models.cjs: Docker-compatible model downloader
- Added comprehensive documentation: README.md, OFFLINE_MODELS.md, analysis docs

Testing:
- All 19 tests passing
- Removed test mocking in favor of real implementation testing
- Updated test environment for Transformers.js compatibility
- Performance tests validate improved efficiency

This migration resolves production issues with Docker egress limitations and provides a more robust, performant foundation for vector operations.

tests/edge-cases.test.ts

@@ -171,7 +171,7 @@ describe('Edge Case Tests', () => {
   describe('Vector edge cases', () => {
     it('should handle vectors with very small values', async () => {
       // Create a vector with very small values
-      const smallVector = new Array(512).fill(1e-10)
+      const smallVector = new Array(384).fill(1e-10)
       const id = await brainyInstance.add(smallVector)
       expect(id).toBeDefined()
       
@@ -183,7 +183,7 @@ describe('Edge Case Tests', () => {
     
     it('should handle vectors with very large values', async () => {
       // Create a vector with large values
-      const largeVector = new Array(512).fill(1e10)
+      const largeVector = new Array(384).fill(1e10)
       const id = await brainyInstance.add(largeVector)
       expect(id).toBeDefined()
       
@@ -195,7 +195,7 @@ describe('Edge Case Tests', () => {
     
     it('should handle vectors with mixed positive and negative values', async () => {
       // Create a vector with mixed values
-      const mixedVector = new Array(512).fill(0).map((_, i) => i % 2 === 0 ? 1 : -1)
+      const mixedVector = new Array(384).fill(0).map((_, i) => i % 2 === 0 ? 1 : -1)
       const id = await brainyInstance.add(mixedVector)
       expect(id).toBeDefined()
       
@@ -234,8 +234,8 @@ describe('Edge Case Tests', () => {
       const batchItems = [
         'text item 1',
         { text: 'text item 2', metadata: { source: 'batch-test' } },
-        new Array(512).fill(0.1), // Vector
-        { vector: new Array(512).fill(0.2), metadata: { source: 'vector-item' } }
+        new Array(384).fill(0.1), // Vector
+        { vector: new Array(384).fill(0.2), metadata: { source: 'vector-item' } }
       ]
       
       const results = await brainyInstance.addBatch(batchItems)