Registry / database / sqlite-vec-darwin-x64

sqlite-vec-darwin-x64

library0.1.9jsnpmunverified

A platform-specific binary package for the sqlite-vec vector search SQLite extension on macOS x64 systems. Version 0.1.9 (pre-1.0, active development with monthly releases). Provides fast k-means clustering and approximate nearest neighbor search as a loadable extension. Alternative to vector extensions with minimal dependencies and no external server.

database

npm install sqlite-vec-darwin-x64

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · SQLITE-VEC-DARWIN-

sqlite-vec-darwin-x64

databasejavascriptv0.1.9

harness data pending

Install & Compatibility

Where this runs

No compatibility data collected yet for this library.

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

sqlite_vec

✓ import sqlite_vec from 'sqlite-vec-darwin-x64'

✗ import sqlite_vec from 'sqlite-vec'

This is a platform-specific package; use the parent package 'sqlite-vec' which auto-selects the correct binary.

getVectorLoadablePath

✓ import { getVectorLoadablePath } from 'sqlite-vec-darwin-x64'

✗ const { getVectorLoadablePath } = require('sqlite-vec-darwin-x64')

Package may be ESM-only; CommonJS require might not work.

default

✓ import sqlite_vec_init from 'sqlite-vec-darwin-x64'

Default export is a function to initialize the extension in Better-SQLite3.

Install the macOS x64 binary, load extension into Better-SQLite3, create a vector table, insert vectors, and perform a nearest neighbor search.

import Database from 'better-sqlite3'; import sqlite_vec from 'sqlite-vec-darwin-x64'; const db = new Database(':memory:'); db.loadExtension(sqlite_vec()); db.exec(` CREATE VIRTUAL TABLE vec_items USING vec0( item_id INTEGER PRIMARY KEY, embedding FLOAT[8] ); `); const insert = db.prepare('INSERT INTO vec_items(item_id, embedding) VALUES (?, ?)'); insert.run(1, new Float32Array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8])); insert.run(2, new Float32Array([0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2])); const rows = db.prepare(` SELECT item_id, distance FROM vec_items WHERE embedding MATCH ? ORDER BY distance LIMIT 3 `).all(new Float32Array([0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5])); console.log(rows);

Debug

Known issues

gotchaDirectly importing platform-specific package bypasses the parent package's automatic selection.

fix

Use 'sqlite-vec' as the main package instead; it will choose the correct binary for your platform.

affects: >=0.1.0

breakingPackage is pre-1.0 and may introduce breaking changes in minor versions.

fix

Pin exact version (e.g., '0.1.9') in package.json to avoid unexpected changes.

affects: >=0.0.0

deprecatedThe 'vec0' virtual table interface may change in future releases.

fix

Watch the changelog and use versioned documentation.

affects: >=0.1.0

gotchaExtension must be loaded before any vector usage; calling sqlite_vec() multiple times may cause errors.

fix

Load the extension exactly once per database connection.

affects: >=0.0.0

Errors

Common errors & fixes

Error: Cannot find module 'sqlite-vec-darwin-x64'

Package not installed or platform mismatch (e.g., Linux instead of macOS).

fix

Install the parent 'sqlite-vec' package which automatically picks the correct binary for the current platform.

Error: The extension 'vec0' is not available

SQLite extension not loaded into the database connection.

fix

Call db.loadExtension(sqlite_vec()) before creating vec0 tables.

TypeError: embeddings must be Float32Array

Attempting to insert an array or other typed array instead of Float32Array.

fix

Explicitly convert to Float32Array: new Float32Array([...])

Upgrade

Version history

0.1.9latest on npm

Audit

Dependencies

No dependency data recorded yet.

Agent activity

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Resources

githubhttps://github.com/asg017/sqlite-vec ↗

packagesqlite-vec-darwin-x64 ↗

homepagehttps://github.com/asg017/sqlite-vec#readme ↗