Getting Started

Mix.install([
  {:sqlite_vec, github: "joelpaulkoch/sqlite_vec"},
  {:exqlite, "~> 0.25.0"}
])

Sample usage

This example is taken directly from the original sqlite-vec README.

First, we open a new connection, then we load the extension. Now we are ready to create the virtual table where we can store vectors.

alias Exqlite.Basic

{:ok, conn} = Basic.open(":memory:")
:ok = Basic.enable_load_extension(conn)

Basic.load_extension(conn, SqliteVec.path())
Basic.exec(conn, "create virtual table vec_examples using vec0(sample_embedding float[8]);", [])

We insert some vectors into the database.

Basic.exec(conn, """
-- vectors can be provided as JSON or in a compact binary format
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]');
""")

Then, we can query for nearest neighbors.

Basic.exec(conn, """
-- KNN style query
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;
""")