sqlite-vss (SQLite Vector Similarity Search) is a SQLite extension that brings vector search capabilities to SQLite, based on Faiss. It 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!

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.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(

sqlite-vss is a Bring-your-own-vectors database, it is compatable with any embedding or vector 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 or raw bytes.

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(
  (select headline_embedding from articles where rowid = 123)
limit 100;

You can INSERT and DELETE into these tables as necessary, but 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.


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)


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)
  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 instructions to compile sqlite-vss yourself, as well as a full SQL API reference.


The Releases page contains pre-built binaries for Linux x86_64 and MacOS x86_64 (MacOS Big Sur 11 or higher). More pre-compiled targets will be available in the future. Additionally, sqlite-vss is distributed on common package managers like pip for Python and npm for Node.js, see below for details.

Do note that on Linux machines, you’ll have to install some packages to make these options work:

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

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.

With the sqlite3 CLI

For using sqlite-vss with the official SQLite command line shell, download the vector0.dylib/vss0.dylib (for MacOS Big Sur 11 or higher) or vector0.so/vss0.so (Linux) files from a release and load it into your SQLite environment.

The vector0 extension is a required dependency, so make sure to load that before vss0.

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


For Python developers, install the sqlite-vss package with:

import sqlite3
import sqlite_vss

db = sqlite3.connect(':memory:')

version, = db.execute('select vss_version()').fetchone()

See python/sqlite_vss for more details.


For Node.js developers, install the sqlite-vss npm package with:

import Database from "better-sqlite3"; // also compatible with node-sqlite3
import * as sqlite_vss from "sqlite-ulid";

const db = new Database(":memory:");

const version = db.prepare("select vss_version()").pluck().get();

See npm/sqlite-vss/README.md for more details.


For Deno developers, use the deno.land/x/sqlite_vss module:


console.log(version);” dir=”auto”>

// Requires all permissions (-A) and the --unstable flag

import { Database } from "https://deno.land/x/sqlite3@0.8.0/mod.ts";
import * as sqlite_vss from "https://deno.land/x/sqlite_vss/mod.ts";

const db = new Database(":memory:");

db.enableLoadExtension = true;

const [version] = db.prepare("select vss_version()").value<[string]>()!;


See deno/sqlite-vss/README.md for more details.


And for Datasette, install the datasette-sqlite-vss plugin with:

datasette install datasette-sqlite-vss

See python/datasette_sqlite_vss for more details.


  • 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 sponsoring my work, sharing this project with a friend, or hiring me for contract/consulting work!

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