Add delete recall benchmark suite

New benchmarks-ann/bench-delete/ directory measures KNN recall
degradation after random row deletion across index types (flat,
rescore, IVF, DiskANN). For each config and delete percentage:
builds index, measures baseline recall, copies DB, deletes random
rows, measures post-delete recall, VACUUMs and records size savings.

Includes Makefile targets, self-contained smoke test with synthetic
data, and results DB for analysis.

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

benchmarks-ann/bench-delete/test_smoke.py

@@ -0,0 +1,124 @@
+#!/usr/bin/env python3
+"""Quick self-contained smoke test using a synthetic dataset.
+Creates a tiny base.db in a temp dir, runs the delete benchmark, verifies output.
+"""
+import os
+import random
+import sqlite3
+import struct
+import sys
+import tempfile
+
+_SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+_ROOT_DIR = os.path.join(_SCRIPT_DIR, "..", "..")
+EXT_PATH = os.path.join(_ROOT_DIR, "dist", "vec0")
+
+DIMS = 8
+N_TRAIN = 200
+N_QUERIES = 10
+K_NEIGHBORS = 5
+
+
+def _f32(vals):
+    return struct.pack(f"{len(vals)}f", *vals)
+
+
+def make_synthetic_base_db(path):
+    """Create a minimal base.db with train vectors and query vectors."""
+    rng = random.Random(123)
+    db = sqlite3.connect(path)
+    db.execute("CREATE TABLE train(id INTEGER PRIMARY KEY, vector BLOB)")
+    db.execute("CREATE TABLE query_vectors(id INTEGER PRIMARY KEY, vector BLOB)")
+
+    for i in range(N_TRAIN):
+        vec = [rng.gauss(0, 1) for _ in range(DIMS)]
+        db.execute("INSERT INTO train VALUES (?, ?)", (i, _f32(vec)))
+
+    for i in range(N_QUERIES):
+        vec = [rng.gauss(0, 1) for _ in range(DIMS)]
+        db.execute("INSERT INTO query_vectors VALUES (?, ?)", (i, _f32(vec)))
+
+    db.commit()
+    db.close()
+
+
+def main():
+    if not os.path.exists(EXT_PATH + ".dylib") and not os.path.exists(EXT_PATH + ".so"):
+        # Try bare path (sqlite handles extension)
+        pass
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        base_db = os.path.join(tmpdir, "base.db")
+        make_synthetic_base_db(base_db)
+
+        # Patch DATASETS to use our synthetic DB
+        import bench_delete
+        bench_delete.DATASETS["synthetic"] = {
+            "base_db": base_db,
+            "dimensions": DIMS,
+        }
+
+        out_dir = os.path.join(tmpdir, "runs")
+
+        # Test flat index
+        print("=== Testing flat index ===")
+        name, params = bench_delete.parse_config("flat:type=vec0-flat,variant=float")
+        params["dimensions"] = DIMS
+        results = bench_delete.run_delete_benchmark(
+            name, params, base_db, EXT_PATH,
+            subset_size=N_TRAIN, dims=DIMS,
+            delete_pcts=[25, 50], k=K_NEIGHBORS, n_queries=N_QUERIES,
+            out_dir=out_dir, seed_val=42,
+        )
+
+        bench_delete.print_report(results)
+
+        # Flat recall should be 1.0 at all delete %
+        for r in results:
+            assert r["recall"] == 1.0, \
+                f"Flat recall should be 1.0, got {r['recall']} at {r['delete_pct']}%"
+        print("\n  PASS: flat recall is 1.0 at all delete percentages\n")
+
+        # Test DiskANN
+        print("=== Testing DiskANN ===")
+        name2, params2 = bench_delete.parse_config(
+            "diskann:type=diskann,R=8,L=32,quantizer=binary"
+        )
+        params2["dimensions"] = DIMS
+        results2 = bench_delete.run_delete_benchmark(
+            name2, params2, base_db, EXT_PATH,
+            subset_size=N_TRAIN, dims=DIMS,
+            delete_pcts=[25, 50], k=K_NEIGHBORS, n_queries=N_QUERIES,
+            out_dir=out_dir, seed_val=42,
+        )
+
+        bench_delete.print_report(results2)
+
+        # DiskANN baseline (0%) should have decent recall
+        baseline = results2[0]
+        assert baseline["recall"] > 0.0, \
+            f"DiskANN baseline recall is zero"
+        print(f"  PASS: DiskANN baseline recall={baseline['recall']}")
+
+        # Test rescore
+        print("\n=== Testing rescore ===")
+        name3, params3 = bench_delete.parse_config(
+            "rescore:type=rescore,quantizer=bit,oversample=4"
+        )
+        params3["dimensions"] = DIMS
+        results3 = bench_delete.run_delete_benchmark(
+            name3, params3, base_db, EXT_PATH,
+            subset_size=N_TRAIN, dims=DIMS,
+            delete_pcts=[25, 50], k=K_NEIGHBORS, n_queries=N_QUERIES,
+            out_dir=out_dir, seed_val=42,
+        )
+
+        bench_delete.print_report(results3)
+        print(f"  PASS: rescore baseline recall={results3[0]['recall']}")
+
+    print("\n ALL SMOKE TESTS PASSED")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())