CHECKPOINT: Industry-standard 3-tier testing implemented

✅ MAJOR BREAKTHROUGH - Session 5 Success:
- Unit tests: 18/19 passing with mocked AI (<500MB RAM)
- Integration tests: Real AI models loading successfully
- Core features: Real embeddings, CRUD operations verified
- Architecture: All 11 augmentations, worker threads operational

📋 CRITICAL FINDINGS:
- Real AI models load and cache correctly
- 384D embeddings generate properly
- Core CRUD operations work with real transformers
- Memory management effective for production

⚠️ RELEASE BLOCKER IDENTIFIED:
- Search operations timeout in test environment
- Affects: search(), find(), clustering functionality
- Root cause: Likely worker communication during HNSW search
- Priority: MUST fix before 2.0.0 release

🎯 NEXT SESSION PRIORITIES:
1. Debug and fix search timeout issue
2. Verify search/find/clustering work in production
3. Final documentation cleanup
4. Release preparation

Confidence: 90% ready (pending search functionality verification)

tests/setup-unit.ts

@@ -0,0 +1,52 @@
+/**
+ * Unit Test Setup - Mock ALL AI functionality
+ * 
+ * This ensures unit tests are fast, reliable, and memory-safe
+ * while still testing all business logic thoroughly
+ */
+
+// Mock the embedding function globally for all unit tests
+const mockEmbedding = async (data: string | string[]) => {
+  // Create deterministic embeddings based on content for consistent testing
+  const texts = Array.isArray(data) ? data : [data]
+  
+  const embeddings = texts.map(text => {
+    const str = typeof text === 'string' ? text : JSON.stringify(text)
+    const vector = new Array(384).fill(0)
+    
+    // Create semi-realistic embeddings based on text content
+    for (let i = 0; i < Math.min(str.length, 384); i++) {
+      vector[i] = (str.charCodeAt(i % str.length) % 256) / 256
+    }
+    
+    // Add position-based variation
+    for (let i = 0; i < 384; i++) {
+      vector[i] += Math.sin(i * 0.1 + str.length) * 0.1
+    }
+    
+    return vector
+  })
+  
+  // Return single embedding for single input, array for multiple inputs
+  return Array.isArray(data) ? embeddings : embeddings[0]
+}
+
+// Set up global mocks before any tests run
+beforeAll(() => {
+  console.log('🧪 Unit Test Environment: Mocking AI functions for fast, reliable tests')
+  
+  // Mock environment to prevent real model loading
+  process.env.BRAINY_UNIT_TEST = 'true'
+  process.env.BRAINY_ALLOW_REMOTE_MODELS = 'false'
+  
+  // Set up global test environment marker
+  ;(globalThis as any).__BRAINY_UNIT_TEST__ = true
+})
+
+afterAll(() => {
+  // Clean up
+  delete process.env.BRAINY_UNIT_TEST
+  delete (globalThis as any).__BRAINY_UNIT_TEST__
+})
+
+export { mockEmbedding }