Add text PK, WAL concurrency tests, and fix bench-smoke config

Infrastructure improvements:
- Fix benchmarks-ann Makefile: type=baseline -> type=vec0-flat (baseline
  was never a valid INDEX_REGISTRY key)
- Add DiskANN + text primary key test: insert, KNN, delete, KNN
- Add rescore + text primary key test: insert, KNN, delete, KNN
- Add WAL concurrency test: reader sees snapshot isolation while
  writer has an open transaction, KNN works on reader's snapshot

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

Author: asg017

benchmarks-ann/Makefile

@@ -4,9 +4,9 @@ EXT = ../dist/vec0
 
 # --- Baseline (brute-force) configs ---
 BASELINES = \
-	"brute-float:type=baseline,variant=float" \
-	"brute-int8:type=baseline,variant=int8" \
-	"brute-bit:type=baseline,variant=bit"
+	"brute-float:type=vec0-flat,variant=float" \
+	"brute-int8:type=vec0-flat,variant=int8" \
+	"brute-bit:type=vec0-flat,variant=bit"
 
 # --- IVF configs ---
 IVF_CONFIGS = \
@@ -43,7 +43,7 @@ ground-truth: seed
 # --- Quick smoke test ---
 bench-smoke: seed
 	$(BENCH) --subset-size 5000 -k 10 -n 20 --dataset cohere1m -o runs \
-		"brute-float:type=baseline,variant=float" \
+		"brute-float:type=vec0-flat,variant=float" \
 		"ivf-quick:type=ivf,nlist=16,nprobe=4" \
 		"diskann-quick:type=diskann,R=48,L=64,quantizer=binary"
 

tests/test-diskann.py

@@ -1246,3 +1246,46 @@ def test_diskann_delete_interleaved_with_knn(db):
         returned = {r["rowid"] for r in rows}
         assert returned.issubset(alive), \
             f"Deleted rowid {to_del} found in KNN results"
+
+
+# ======================================================================
+# Text primary key + DiskANN
+# ======================================================================
+
+
+def test_diskann_text_pk_insert_knn_delete(db):
+    """DiskANN with text primary key: insert, KNN, delete, KNN again."""
+    db.execute("""
+        CREATE VIRTUAL TABLE t USING vec0(
+            id text primary key,
+            emb float[8] INDEXED BY diskann(neighbor_quantizer=binary, n_neighbors=8)
+        )
+    """)
+
+    vecs = {
+        "alpha": [1, 0, 0, 0, 0, 0, 0, 0],
+        "beta": [0, 1, 0, 0, 0, 0, 0, 0],
+        "gamma": [0, 0, 1, 0, 0, 0, 0, 0],
+        "delta": [0, 0, 0, 1, 0, 0, 0, 0],
+        "epsilon": [0, 0, 0, 0, 1, 0, 0, 0],
+    }
+    for name, vec in vecs.items():
+        db.execute("INSERT INTO t(id, emb) VALUES (?, ?)", [name, _f32(vec)])
+
+    # KNN should return text IDs
+    rows = db.execute(
+        "SELECT id, distance FROM t WHERE emb MATCH ? AND k=3",
+        [_f32([1, 0, 0, 0, 0, 0, 0, 0])],
+    ).fetchall()
+    assert len(rows) >= 1
+    ids = [r["id"] for r in rows]
+    assert "alpha" in ids  # closest to query
+
+    # Delete and verify
+    db.execute("DELETE FROM t WHERE id = 'alpha'")
+    rows = db.execute(
+        "SELECT id FROM t WHERE emb MATCH ? AND k=3",
+        [_f32([1, 0, 0, 0, 0, 0, 0, 0])],
+    ).fetchall()
+    ids = [r["id"] for r in rows]
+    assert "alpha" not in ids

tests/test-insert-delete.py

@@ -483,3 +483,57 @@ def test_delete_one_chunk_of_two_shrinks_pages(tmp_path):
         row = db.execute("select emb from v where rowid = ?", [i]).fetchone()
         assert row[0] == _f32([float(i)] * dims)
     db.close()
+
+
+def test_wal_concurrent_reader_during_write(tmp_path):
+    """In WAL mode, a reader should see a consistent snapshot while a writer inserts."""
+    dims = 4
+    db_path = str(tmp_path / "test.db")
+
+    # Writer: create table, insert initial rows, enable WAL
+    writer = sqlite3.connect(db_path)
+    writer.enable_load_extension(True)
+    writer.load_extension("dist/vec0")
+    writer.execute("PRAGMA journal_mode=WAL")
+    writer.execute(
+        f"CREATE VIRTUAL TABLE v USING vec0(emb float[{dims}])"
+    )
+    for i in range(1, 11):
+        writer.execute("INSERT INTO v(rowid, emb) VALUES (?, ?)", [i, _f32([float(i)] * dims)])
+    writer.commit()
+
+    # Reader: open separate connection, start read
+    reader = sqlite3.connect(db_path)
+    reader.enable_load_extension(True)
+    reader.load_extension("dist/vec0")
+
+    # Reader sees 10 rows
+    count_before = reader.execute("SELECT count(*) FROM v").fetchone()[0]
+    assert count_before == 10
+
+    # Writer inserts more rows (not yet committed)
+    writer.execute("BEGIN")
+    for i in range(11, 21):
+        writer.execute("INSERT INTO v(rowid, emb) VALUES (?, ?)", [i, _f32([float(i)] * dims)])
+
+    # Reader still sees 10 (WAL snapshot isolation)
+    count_during = reader.execute("SELECT count(*) FROM v").fetchone()[0]
+    assert count_during == 10
+
+    # KNN during writer's transaction should work on reader's snapshot
+    rows = reader.execute(
+        "SELECT rowid FROM v WHERE emb MATCH ? AND k = 5",
+        [_f32([1.0] * dims)],
+    ).fetchall()
+    assert len(rows) == 5
+    assert all(r[0] <= 10 for r in rows)  # only original rows
+
+    # Writer commits
+    writer.commit()
+
+    # Reader sees new rows after re-query (new snapshot)
+    count_after = reader.execute("SELECT count(*) FROM v").fetchone()[0]
+    assert count_after == 20
+
+    writer.close()
+    reader.close()

tests/test-rescore.py

@@ -595,3 +595,40 @@ def test_corrupt_zeroblob_validity(db):
         ).fetchall()
     except sqlite3.OperationalError:
         pass  # Error is acceptable — crash is not
+
+
+def test_rescore_text_pk_insert_knn_delete(db):
+    """Rescore with text primary key: insert, KNN, delete, KNN again."""
+    db.execute(
+        "CREATE VIRTUAL TABLE t USING vec0("
+        "  id text primary key,"
+        "  embedding float[128] indexed by rescore(quantizer=bit)"
+        ")"
+    )
+
+    import random
+    random.seed(99)
+    vecs = {}
+    for name in ["alpha", "beta", "gamma", "delta", "epsilon"]:
+        v = [random.gauss(0, 1) for _ in range(128)]
+        vecs[name] = v
+        db.execute("INSERT INTO t(id, embedding) VALUES (?, ?)", [name, float_vec(v)])
+
+    # KNN should return text IDs
+    rows = db.execute(
+        "SELECT id, distance FROM t WHERE embedding MATCH ? ORDER BY distance LIMIT 3",
+        [float_vec(vecs["alpha"])],
+    ).fetchall()
+    assert len(rows) >= 1
+    ids = [r["id"] for r in rows]
+    assert "alpha" in ids
+
+    # Delete and verify
+    db.execute("DELETE FROM t WHERE id = 'alpha'")
+    rows = db.execute(
+        "SELECT id FROM t WHERE embedding MATCH ? ORDER BY distance LIMIT 3",
+        [float_vec(vecs["alpha"])],
+    ).fetchall()
+    ids = [r["id"] for r in rows]
+    assert "alpha" not in ids
+    assert len(rows) >= 1  # other results still returned