HNSW vector index: 100K search in 27ms (58x faster than brute-force)

- instant-distance HNSW implementation for approximate nearest neighbors
- HnswStore: build from stored embeddings, in-memory index, thread-safe
- POST /vectors/hnsw/build — build index from Parquet (100K in 35s release)
- POST /vectors/hnsw/search — fast ANN search
- GET /vectors/hnsw/list — list loaded indexes

Benchmark (100K × 768d, release build):
  Brute-force: 1,567ms
  HNSW:           31ms (50x)
  HNSW warm:      27ms (58x)

Build cost: 35s one-time for 100K vectors (release mode)
ef_construction=40, ef_search=50 — good recall/speed balance

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

crates/gateway/src/main.rs

@@ -72,6 +72,7 @@ async fn main() {
                 ai_client: ai_client.clone(),
                 job_tracker: vectord::jobs::JobTracker::new(),
                 index_registry: index_reg,
+                hnsw_store: vectord::hnsw::HnswStore::new(),
             }
         }))
         .nest("/workspaces", queryd::workspace_service::router(workspace_mgr))