feat: replace transformers.js with direct ONNX WASM for Bun compatibility

- Remove @huggingface/transformers dependency (539MB native binaries)
- Add direct ONNX Runtime Web embedding engine
- Bundle all-MiniLM-L6-v2-q8 model (24MB, no runtime downloads)
- Works with Node.js, Bun, and bun build --compile
- Air-gap compatible: fully self-contained, no internet required

New WASM embedding components:
- WASMEmbeddingEngine: Main integration class
- WordPieceTokenizer: Pure TypeScript tokenizer
- EmbeddingPostProcessor: Mean pooling + L2 normalization
- ONNXInferenceEngine: Direct ONNX Runtime Web wrapper
- AssetLoader: Model file loading

Tests added:
- 11 WASM embedding integration tests
- 8 Bun compatibility tests

New npm scripts:
- test:wasm - Run WASM embedding tests
- test:bun - Run tests with Bun
- test:bun:compile - Build and run compiled binary

tests/integration/wasm-embeddings.test.ts

@@ -0,0 +1,142 @@
+/**
+ * WASM Embedding Integration Test
+ *
+ * Tests the actual WASM embedding engine with real model inference.
+ * NO mocks - this loads the real ONNX model and generates real embeddings.
+ */
+
+import { describe, it, expect, beforeAll } from 'vitest'
+import { WASMEmbeddingEngine } from '../../src/embeddings/wasm/WASMEmbeddingEngine.js'
+import { embeddingManager } from '../../src/embeddings/EmbeddingManager.js'
+
+// Ensure we're NOT in mock mode for these tests
+beforeAll(() => {
+  delete process.env.BRAINY_UNIT_TEST
+  ;(globalThis as any).__BRAINY_UNIT_TEST__ = false
+})
+
+describe('WASM Embedding Engine - Real Embeddings', () => {
+  it('should initialize the WASM engine', async () => {
+    const engine = WASMEmbeddingEngine.getInstance()
+    await engine.initialize()
+    expect(engine.isInitialized()).toBe(true)
+  })
+
+  it('should generate 384-dimensional embeddings', async () => {
+    const engine = WASMEmbeddingEngine.getInstance()
+    const embedding = await engine.embed('Hello world')
+
+    expect(embedding).toBeInstanceOf(Array)
+    expect(embedding.length).toBe(384)
+    expect(typeof embedding[0]).toBe('number')
+  })
+
+  it('should produce consistent embeddings for same input', async () => {
+    const engine = WASMEmbeddingEngine.getInstance()
+
+    const emb1 = await engine.embed('test consistency')
+    const emb2 = await engine.embed('test consistency')
+
+    expect(emb1).toEqual(emb2)
+  })
+
+  it('should produce normalized embeddings (unit length)', async () => {
+    const engine = WASMEmbeddingEngine.getInstance()
+    const embedding = await engine.embed('normalize test')
+
+    // Calculate L2 norm
+    const norm = Math.sqrt(embedding.reduce((sum, v) => sum + v * v, 0))
+
+    // Should be approximately 1.0 (normalized)
+    expect(norm).toBeCloseTo(1.0, 4)
+  })
+
+  it('should produce semantically meaningful embeddings', async () => {
+    const engine = WASMEmbeddingEngine.getInstance()
+
+    // Similar sentences
+    const catEmb = await engine.embed('The cat sat on the mat')
+    const felineEmb = await engine.embed('A feline rests on a rug')
+
+    // Dissimilar sentence
+    const stockEmb = await engine.embed('The stock market crashed today')
+
+    // Cosine similarity function
+    const cosineSim = (a: number[], b: number[]) => {
+      let dot = 0, normA = 0, normB = 0
+      for (let i = 0; i < a.length; i++) {
+        dot += a[i] * b[i]
+        normA += a[i] * a[i]
+        normB += b[i] * b[i]
+      }
+      return dot / (Math.sqrt(normA) * Math.sqrt(normB))
+    }
+
+    const similarSim = cosineSim(catEmb, felineEmb)
+    const dissimilarSim = cosineSim(catEmb, stockEmb)
+
+    // Similar sentences should have higher similarity than dissimilar
+    expect(similarSim).toBeGreaterThan(dissimilarSim)
+    expect(similarSim).toBeGreaterThan(0.4) // Reasonably similar
+    expect(dissimilarSim).toBeLessThan(0.3) // Not very similar
+  })
+
+  it('should handle empty string', async () => {
+    const engine = WASMEmbeddingEngine.getInstance()
+    const embedding = await engine.embed('')
+
+    expect(embedding.length).toBe(384)
+  })
+
+  it('should handle long text', async () => {
+    const engine = WASMEmbeddingEngine.getInstance()
+    const longText = 'word '.repeat(1000)
+    const embedding = await engine.embed(longText)
+
+    expect(embedding.length).toBe(384)
+  })
+
+  it('should work through EmbeddingManager', async () => {
+    await embeddingManager.init()
+
+    const embedding = await embeddingManager.embed('test via manager')
+
+    expect(embedding.length).toBe(384)
+    expect(embeddingManager.isInitialized()).toBe(true)
+  })
+
+  it('should report correct stats', async () => {
+    const engine = WASMEmbeddingEngine.getInstance()
+    const stats = engine.getStats()
+
+    expect(stats.initialized).toBe(true)
+    expect(stats.modelName).toBe('all-MiniLM-L6-v2')
+    expect(stats.embedCount).toBeGreaterThan(0)
+  })
+})
+
+describe('WASM Embedding - Batch Operations', () => {
+  it('should embed multiple texts', async () => {
+    const engine = WASMEmbeddingEngine.getInstance()
+
+    const texts = [
+      'First document about cats',
+      'Second document about dogs',
+      'Third document about birds'
+    ]
+
+    const embeddings = await engine.embedBatch(texts)
+
+    expect(embeddings.length).toBe(3)
+    expect(embeddings[0].length).toBe(384)
+    expect(embeddings[1].length).toBe(384)
+    expect(embeddings[2].length).toBe(384)
+  })
+
+  it('should handle empty batch', async () => {
+    const engine = WASMEmbeddingEngine.getInstance()
+    const embeddings = await engine.embedBatch([])
+
+    expect(embeddings).toEqual([])
+  })
+})