asg017/sqlite-vec

sqlite-vec

An extremely small, "fast enough" vector search SQLite extension that runs anywhere! A successor to sqlite-vss

[!IMPORTANT] sqlite-vec is a pre-v1, so expect breaking changes!

• Store and query float, int8, and binary vectors in vec0 virtual tables
• Written in pure C, no dependencies, runs anywhere SQLite runs (Linux/MacOS/Windows, in the browser with WASM, Raspberry Pis, etc.)
• Store non-vector data in metadata, auxiliary, or partition key columns

sqlite-vec is a Mozilla Builders project, with additional sponsorship from Fly.io , Turso, SQLite Cloud, and Shinkai. See the Sponsors section for more details.

Installing

See Installing sqlite-vec for more details.

Sample usage

.load ./vec0

create virtual table vec_examples using vec0(
  sample_embedding float[8]
);

-- 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]');


-- 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;
/*
┌───────┬──────────────────┐
│ rowid │     distance     │
├───────┼──────────────────┤
│ 2     │ 2.38687372207642 │
│ 1     │ 2.38978505134583 │
└───────┴──────────────────┘
*/

Sponsors

Development of sqlite-vec is supported by multiple generous sponsors! Mozilla is the main sponsor through the new Builders project.

sqlite-vec is also sponsored by the following companies:

As well as multiple individual supporters on Github sponsors!

If your company interested in sponsoring sqlite-vec development, send me an email to get more info: https://alexgarcia.xyz

See Also

• sqlite-ecosystem, Maybe more 3rd party SQLite extensions I've developed
• sqlite-rembed, Generate text embeddings from remote APIs like OpenAI/Nomic/Ollama, meant for testing and SQL scripts
• sqlite-lembed, Generate text embeddings locally from embedding models in the .gguf format