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Add ANN search support for vec0 virtual table (#273)

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Add approximate nearest neighbor infrastructure to vec0: shared distance
dispatch (vec0_distance_full), flat index type with parser, NEON-optimized
cosine/Hamming for float32/int8, amalgamation script, and benchmark suite
(benchmarks-ann/) with ground-truth generation and profiling tools. Remove
unused vec_npy_each/vec_static_blobs code, fix missing stdint.h include.
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Alex Garcia 2026-03-31 01:03:32 -07:00 • committed by GitHub
parent e9f598abfa
commit 0de765f457
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27 changed files with 2177 additions and 2116 deletions
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57
benchmarks/exhaustive-memory/bench.py
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@ -248,59 +248,6 @@ def bench_libsql(base, query, page_size, k) -> BenchResult:
return BenchResult(f"libsql ({page_size})", build_time, times)
def register_np(db, array, name):
ptr = array.__array_interface__["data"][0]
nvectors, dimensions = array.__array_interface__["shape"]
element_type = array.__array_interface__["typestr"]
assert element_type == "<f4"
name_escaped = db.execute("select printf('%w', ?)", [name]).fetchone()[0]
db.execute(
"insert into temp.vec_static_blobs(name, data) select ?, vec_static_blob_from_raw(?, ?, ?, ?)",
[name, ptr, element_type, dimensions, nvectors],
)
db.execute(
f'create virtual table "{name_escaped}" using vec_static_blob_entries({name_escaped})'
)
def bench_sqlite_vec_static(base, query, k) -> BenchResult:
print(f"sqlite-vec static...")
db = sqlite3.connect(":memory:")
db.enable_load_extension(True)
db.load_extension("../../dist/vec0")
t = time.time()
register_np(db, base, "base")
build_time = time.time() - t
times = []
results = []
for (
idx,
q,
) in enumerate(query):
t0 = time.time()
result = db.execute(
"""
select
rowid
from base
where vector match ?
and k = ?
order by distance
""",
[q.tobytes(), k],
).fetchall()
assert len(result) == k
times.append(time.time() - t0)
return BenchResult(f"sqlite-vec static", build_time, times)
def bench_faiss(base, query, k) -> BenchResult:
import faiss
dimensions = base.shape[1]
@ -438,8 +385,6 @@ def suite(name, base, query, k, benchmarks):
for b in benchmarks:
if b == "faiss":
results.append(bench_faiss(base, query, k=k))
elif b == "vec-static":
results.append(bench_sqlite_vec_static(base, query, k=k))
elif b.startswith("vec-scalar"):
_, page_size = b.split('.')
results.append(bench_sqlite_vec_scalar(base, query, page_size, k=k))
@ -541,7 +486,7 @@ def parse_args():
help="Number of queries to use. Defaults all",
)
parser.add_argument(
"-x", help="type of runs to make", default="faiss,vec-scalar.4096,vec-static,vec-vec0.4096.16,usearch,duckdb,hnswlib,numpy"
"-x", help="type of runs to make", default="faiss,vec-scalar.4096,vec-vec0.4096.16,usearch,duckdb,hnswlib,numpy"
)
args = parser.parse_args()