test_vec0.py

import sqlite3
import sqlite_vec

db = sqlite3.connect(":memory:")
db.enable_load_extension(True)
sqlite_vec.load(db)
db.enable_load_extension(False)

vec_version, = db.execute("select vec_version()").fetchone()
print(f"vec_version={vec_version}")




embedding = [0.1, 0.2, 0.3, 0.4]
result = db.execute('select vec_length(?)', [sqlite_vec.serialize_float32(embedding)])

print(result.fetchone()[0]) # 4


# Create a virtual table with a float vector of 4 dimensions
db.execute("create virtual table vec_examples using vec0(sample_embedding float[8])")


db.execute("insert into vec_examples(rowid, sample_embedding) values (1, '[-0.200, 0.250, 0.341, -0.211, 0.645, 0.935, -0.316, -0.924]'), (2, '[0.443, -0.501, 0.355, -0.771, 0.707, -0.708, -0.185, 0.362]'), (3, '[0.716, -0.927, 0.134, 0.052, -0.669, 0.793, -0.634, -0.162]'), (4, '[-0.710, 0.330, 0.656, 0.041, -0.990, 0.726, 0.385, -0.958]')")

cursor = db.execute(
    """
    select
    rowid,
    distance
    from vec_examples
    where sample_embedding match '[0.890, 0.544, 0.825, 0.961, 0.358, 0.0196, 0.521, 0.175]'
    order by distance
    limit 2;
    """,
)
results = cursor.fetchall()
print(results)