sqlite-vec

Note

Community Fork Notice: This is a temporary fork of asg017/sqlite-vec Created to merge pending upstream PRs and provide community support while the original author is unavailable. Once development resumes on the original repository, users are encouraged to switch back. All credit for the original implementation goes to Alex Garcia.

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

From Original Package Registries

The original packages on PyPI, npm, RubyGems, and crates.io are maintained by the original author. For the latest features from this fork, see "Installing from This Fork" below.

Language	Install	More Info
Python	pip install sqlite-vec	sqlite-vec with Python
Node.js	npm install sqlite-vec	sqlite-vec with Node.js
Ruby	gem install sqlite-vec	sqlite-vec with Ruby
Rust	cargo add sqlite-vec	sqlite-vec with Rust
Datasette	datasette install datasette-sqlite-vec	sqlite-vec with Datasette
rqlite	rqlited -extensions-path=sqlite-vec.tar.gz	sqlite-vec with rqlite
sqlite-utils	sqlite-utils install sqlite-utils-sqlite-vec	sqlite-vec with sqlite-utils

Installing from This Fork

Install directly from GitHub to get the latest features from this community fork.

Available Languages

Language	Install Latest (main branch)	Install Specific Version
Go	go get github.com/vlasky/sqlite-vec/bindings/go/cgo@main	go get github.com/vlasky/sqlite-vec/bindings/go/cgo@v0.2.0-alpha
Python	pip install git+https://github.com/vlasky/sqlite-vec.git	pip install git+https://github.com/vlasky/sqlite-vec.git@v0.2.0-alpha
Rust	cargo add sqlite-vec --git https://github.com/vlasky/sqlite-vec	cargo add sqlite-vec --git https://github.com/vlasky/sqlite-vec --tag v0.2.0-alpha
Node.js	npm install vlasky/sqlite-vec	npm install vlasky/sqlite-vec#v0.2.0-alpha
Ruby	gem 'sqlite-vec', git: 'https://github.com/vlasky/sqlite-vec'	gem 'sqlite-vec', git: 'https://github.com/vlasky/sqlite-vec', tag: 'v0.2.0-alpha'

Python Note: Requires Python built with loadable extension support (--enable-loadable-sqlite-extensions). If you encounter an error about extension support not being available:

• Use uv to create virtual environments (automatically uses system Python which typically has extension support)
• Or use system Python instead of pyenv/custom builds
• Or rebuild your Python with ./configure --enable-loadable-sqlite-extensions

Available version tags: See Releases

Build from Source

For direct C usage or other languages:

git clone https://github.com/vlasky/sqlite-vec.git
cd sqlite-vec
./scripts/vendor.sh  # Download vendored dependencies
make loadable        # Builds dist/vec0.so (or .dylib/.dll)

Not Yet Available

• Pre-built binaries via GitHub Releases
• Package registry publications (PyPI, npm, RubyGems, crates.io)
• Datasette/sqlite-utils plugins

For these, use the original packages until this fork's CI/CD is configured.

See the original documentation for detailed usage information.

What's New

See CHANGELOG.md for a complete list of improvements, bug fixes, and merged upstream PRs.

Basic Usage

Vector types: sqlite-vec supports three vector types with different trade-offs:

-- Float vectors (32-bit floating point, most common)
CREATE VIRTUAL TABLE vec_floats USING vec0(embedding float[384]);

-- Int8 vectors (8-bit integers, smaller memory footprint)
CREATE VIRTUAL TABLE vec_int8 USING vec0(embedding int8[384]);

-- Binary vectors (1 bit per dimension, maximum compression)
CREATE VIRTUAL TABLE vec_binary USING vec0(embedding bit[384]);

Usage example:

.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, '[0.279, -0.95, -0.45, -0.554, 0.473, 0.353, 0.784, -0.826]'),
    (2, '[-0.156, -0.94, -0.563, 0.011, -0.947, -0.602, 0.3, 0.09]'),
    (3, '[-0.559, 0.179, 0.619, -0.987, 0.612, 0.396, -0.319, -0.689]'),
    (4, '[0.914, -0.327, -0.815, -0.807, 0.695, 0.207, 0.614, 0.459]'),
    (5, '[0.072, 0.946, -0.243, 0.104, 0.659, 0.237, 0.723, 0.155]'),
    (6, '[0.409, -0.908, -0.544, -0.421, -0.84, -0.534, -0.798, -0.444]'),
    (7, '[0.271, -0.27, -0.26, -0.581, -0.466, 0.873, 0.296, 0.218]'),
    (8, '[-0.658, 0.458, -0.673, -0.241, 0.979, 0.28, 0.114, 0.369]'),
    (9, '[0.686, 0.552, -0.542, -0.936, -0.369, -0.465, -0.578, 0.886]'),
    (10, '[0.753, -0.371, 0.311, -0.209, 0.829, -0.082, -0.47, -0.507]'),
    (11, '[0.123, -0.475, 0.169, 0.796, -0.201, -0.561, 0.995, 0.019]'),
    (12, '[-0.818, -0.906, -0.781, 0.255, 0.584, -0.156, -0.873, -0.237]'),
    (13, '[0.992, 0.058, 0.942, 0.722, -0.977, 0.441, 0.363, 0.074]'),
    (14, '[-0.466, 0.282, -0.777, -0.13, -0.093, 0.908, 0.752, -0.473]'),
    (15, '[0.001, -0.643, 0.825, 0.741, -0.403, 0.278, 0.218, -0.694]'),
    (16, '[0.525, 0.079, 0.557, 0.061, -0.999, -0.352, -0.961, 0.858]'),
    (17, '[0.757, 0.663, -0.385, -0.884, 0.756, 0.894, -0.829, -0.028]'),
    (18, '[-0.862, 0.521, 0.532, -0.743, -0.049, 0.1, -0.47, 0.745]'),
    (19, '[-0.154, -0.576, 0.079, 0.46, -0.598, -0.377, 0.99, 0.3]'),
    (20, '[-0.124, 0.035, -0.758, -0.551, -0.324, 0.177, -0.54, -0.56]');


-- Find 3 nearest neighbors using LIMIT
select
  rowid,
  distance
from vec_examples
where sample_embedding match '[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]'
order by distance
limit 3;
/*
┌───────┬──────────────────┐
│ rowid │     distance     │
├───────┼──────────────────┤
│ 5     │ 1.16368770599365 │
│ 13    │ 1.75137972831726 │
│ 11    │ 1.83941268920898 │
└───────┴──────────────────┘
*/

How vector search works: The MATCH operator finds vectors similar to your query vector. In the example above, sample_embedding MATCH '[0.5, ...]' searches for vectors closest to [0.5, ...] and returns them ordered by distance (smallest = most similar).

Note: All vector similarity queries require LIMIT or k = ? (where k is the number of nearest neighbors to return). This prevents accidentally returning too many results on large datasets, since finding all vectors within a distance threshold requires calculating distance to every vector in the table.

Advanced Usage

This fork adds several powerful features for production use:

Distance Constraints for KNN Queries

Filter results by distance thresholds using >, >=, <, <= operators on the distance column:

-- KNN query with distance constraint
-- Requests k=10 neighbors, but only returns those with distance < 1.5
select rowid, distance
from vec_examples
where sample_embedding match '[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]'
  and k = 10
  and distance < 1.5
order by distance;
/*
┌───────┬──────────────────┐
│ rowid │     distance     │
├───────┼──────────────────┤
│ 5     │ 1.16368770599365 │
└───────┴──────────────────┘
*/

-- KNN query with range constraint: find vectors in a specific distance range
select rowid, distance
from vec_examples
where sample_embedding match '[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]'
  and k = 20
  and distance between 1.5 and 2.0
order by distance;
/*
┌───────┬──────────────────┐
│ rowid │     distance     │
├───────┼──────────────────┤
│ 13    │ 1.75137972831726 │
│ 11    │ 1.83941268920898 │
│ 7     │ 1.89339029788971 │
│ 8     │ 1.92658650875092 │
│ 10    │ 1.93983662128448 │
└───────┴──────────────────┘
*/

Cursor-based Pagination

Instead of using OFFSET (which is slow for large datasets), you can use the last result's distance value as a 'cursor' to fetch the next page. This is more efficient because you're filtering directly rather than skipping rows.

-- First page: get initial results
select rowid, distance
from vec_examples
where sample_embedding match '[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]'
  and k = 3
order by distance;
/*
┌───────┬──────────────────┐
│ rowid │     distance     │
├───────┼──────────────────┤
│ 5     │ 1.16368770599365 │
│ 13    │ 1.75137972831726 │
│ 11    │ 1.83941268920898 │
└───────┴──────────────────┘
*/

-- Next page: use last distance as cursor (distance > 1.83941268920898)
select rowid, distance
from vec_examples
where sample_embedding match '[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]'
  and k = 3
  and distance > 1.83941268920898
order by distance;
/*
┌───────┬──────────────────┐
│ rowid │     distance     │
├───────┼──────────────────┤
│ 7     │ 1.89339029788971 │
│ 8     │ 1.92658650875092 │
│ 10    │ 1.93983662128448 │
└───────┴──────────────────┘
*/

Space Reclamation with Optimize

Reclaim disk space after deleting vectors:

-- Delete vectors
delete from vec_examples where rowid in (2, 4, 6, 8, 10);

-- Reclaim space by compacting shadow tables
insert into vec_examples(vec_examples) values('optimize');

-- Verify deletion
select count(*) from vec_examples;  -- Returns 15

Sponsors

Note

The sponsors listed below support the original asg017/sqlite-vec project by Alex Garcia, not this community fork.

Development of the original 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