sqlite-vss (SQLite Vector Similarity Search) is a SQLite extension that brings vector search capabilities to SQLite, based on Faiss and sqlite-vector. Can be used to build semantic search engines, recommendations, or questions-and-answering tools.

See Introducing sqlite-vss: A SQLite Extension for Vector Search (February 2023) for more details and a live example!

If your company or organization finds this library useful, consider supporting my work!

sqlite-vss depends on the sqlite-vector extension, so load vector0 before loading vss0.

.load ./vector0
.load ./vss0

select vss_version(); -- 'v0.0.1'

sqlite-vss has a similar API to the fts5 Full-Text Search Extension. Use the vss0 module to create virtual tables that can efficiently store and query your vectors.

-- 384 == number of dimensions for this example
create virtual table vss_articles using vss0(
  headline_embedding(384),
  description_embedding(384),
);

sqlite-vss is a Bring-your-own-vectors database, it is compatable with any embedding or vectorization data you have. Consider using OpenAI's Embeddings API, HuggingFace's Inference API, sentence-transformers, or any of these open source model. You can insert vectors into vss0 tables as JSON, raw bytes, or any format defined in sqlite-vector.

insert into vss_articles(rowid, headline_embedding)
  select rowid, headline_embedding from articles;

To query for similar vectors ("k nearest neighbors"), use the vss_search function in the WHERE clause. Here we are searching for the 100 nearest neighbors to the embedding in row #123 in the articles table.

select rowid, distance
from vss_articles
where vss_search(
  headline_embedding, 
  (select headline_embedding from articles where rowid = 123)
)
limit 100;

You can INSERT and DELETE into these tables as necessary, but do note that UPDATE operations aren't supported yet. This can be used with triggers for automatically updated indexes. Also note that "small" INSERT/DELETE operations that only insert a few rows can be slow, so batch where necessary.

begin;

delete from vss_articles 
  where rowid between 100 and 200;

insert into vss_articles(rowd, headline_embedding, description_embedding)
  values (:rowid, :headline_embedding, :description_embedding)

commit;

You can pass in custom Faiss factory strings for specific columns to control how the Faiss index is stored and queried. By default the factory string is "Flat,IDMap2", which can be slow to query as your database grows. Here, we add an inverted file index with 4096 centroids, a non-exhaustive option that makes large database queries much faster.

create virtual table vss_ivf_articles using vss0(
  headline_embedding(384) factory="IVF4096,Flat,IDMap2",
  description_embedding(384) factory="IVF4096,Flat,IDMap2"
);

This IVF will require training! You can define training data with a INSERT command in a single transaction, with the special operation="training" constraint.

insert into vss_ivf_articles(operation, headline_embedding, description_embedding)
  select 
    'training',
    headline_embedding,
    description_embedding
  from articles;

Beware! Indexes that require training can take a long time. With the News Category Dataset (386 dimension over 210k vectors) that this example is based on, the default index would take 8 seconds to build. But with the custom "IVF4096,Flat,IDMap2" factory, it took 45 minutes to train and 4.5 minutes to insert data! This likely can be reduced with a smaller training set, but the faster queries can be helpful.

See docs.md for a full API reference.

The Releases page contains pre-built binaries for Linux x86_64 and MacOS x86_64. More pre-compiled options will be available in the future.

Do note that on Linux machines, you'll have to install some packages to make it work:

sudo apt-get update
sudo apt-get install -y libgomp1 libatlas-base-dev liblapack-dev 

If you want to use sqlite-vss as a Runtime-loadable extension, Download the vss0.dylib (for MacOS) or vss0.so (Linux) file from a release and load it into your SQLite environment.

sqlite-vector is a required dependency, so also make sure to install that loadable extension before loading vss0.

Note: The 0 in the filename (vss0.dylib/ vss0.so) denotes the major version of sqlite-vss. Currently sqlite-vss is pre v1, so expect breaking changes in future versions.

For example, if you are using the SQLite CLI, you can load the library like so:

.load ./vector0
.load ./vss0
select vss_version();
-- v0.0.1

Or in Python, using the builtin sqlite3 module:

import sqlite3
con = sqlite3.connect(":memory:")
con.enable_load_extension(True)
con.load_extension("./vector0")
con.load_extension("./vss0")
print(con.execute("select vss_version()").fetchone())
# ('v0.1.0',)

Or in Node.js using better-sqlite3:

const Database = require("better-sqlite3");
const db = new Database(":memory:");
db.loadExtension("./vector0");
db.loadExtension("./vss0");
console.log(db.prepare("select vss_version()").get());
// { 'vss_version()': 'v0.1.0' }

Or with Datasette:

datasette data.db --load-extension ./vector0 --load-extension ./vss0

• The underlying Faiss indicies are capped at 1GB. Follow #1 for updates.
• Additional filtering on top of KNN searches aren't supported yet. Follow #2 for updates.
• Only CPU Faiss indicies are supported, not GPU yet. Follow #3 for updates.
• mmap'ed indices aren't supported yet, so indicies have to fit in RAM. Follow #4 for updates.
• This extension is written in C++ and doesn't have fuzzy testing yet. Follow #5 for updates.
• UPDATE statements on vss0 virtual tables are not supported, though INSERT and DELETE statements are. Follow #7 for updates.

I (Alex 👋🏼) spent a lot of time and energy on this project and many other open source projects. If your company or organization uses this library (or you're feeling generous), then please consider supporting my work, or share this project with a friend!

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