feat: add SQ8 vector quantization, lazy loading, and two-phase rerank to HNSW

- SQ8 scalar quantization (8-bit) for 4x vector storage reduction
- Lazy vector loading: evict float32 vectors after graph construction,
  load on-demand from storage via UnifiedCache
- Two-phase search: over-retrieve with SQ8 approximate distances,
  rerank top candidates with exact float32 distances
- Configuration surface: hnsw.quantization and hnsw.vectorStorage in BrainyConfig
- All features disabled by default (zero behavior change for existing users)
- 27 new tests covering quantization accuracy, lazy loading, reranking
- Remove GitHub Actions CI (build locally, cortex CI handles native builds)

src/types/brainy.types.ts

@@ -728,6 +728,16 @@ export interface BrainyConfig {
   // Cloud storage (GCS/S3/R2/Azure) should use 'deferred' for 30-50× faster adds
   hnswPersistMode?: 'immediate' | 'deferred'
 
+  // HNSW optimization options (v7.11.0)
+  hnsw?: {
+    quantization?: {
+      enabled?: boolean        // default: false — current behavior exactly
+      bits?: 8 | 4             // default: 8 (SQ8). SQ4 requires brainy-cortex native.
+      rerankMultiplier?: number // default: 3 — over-retrieve 3x, rerank with float32
+    }
+    vectorStorage?: 'memory' | 'lazy'  // default: 'memory' — 'lazy' evicts vectors after insert
+  }
+
   // Memory management options
   maxQueryLimit?: number           // Override auto-detected query result limit (max: 100000)
   reservedQueryMemory?: number     // Memory reserved for queries in bytes (e.g., 1073741824 = 1GB)