SQLite Vector

SQLite Vector is a cross-platform, ultra-efficient SQLite extension that brings vector search capabilities to your embedded database. It works seamlessly on iOS, Android, Windows, Linux, and macOS, using just 30MB of memory by default. With support for Float32, Float16, BFloat16, Int8, and UInt8, and highly optimized distance functions, it's the ideal solution for Edge AI applications.

🚀 Highlights

• ✅ No virtual tables required – store vectors directly as BLOBs in ordinary tables
• ✅ Blazing fast – optimized C implementation with SIMD acceleration
• ✅ Low memory footprint – defaults to just 30MB of RAM usage
• ✅ Zero preindexing needed – no long preprocessing or index-building phases
• ✅ Works offline – perfect for on-device, privacy-preserving AI workloads
• ✅ Plug-and-play – drop into existing SQLite workflows with minimal effort
• ✅ Cross-platform – works out of the box on all major OSes

Why Use SQLite-Vector?

Feature	SQLite-Vector	Traditional Solutions
Works with ordinary tables	✅	❌ (usually require special virtual tables)
Requires preindexing	❌	✅ (can take hours for large datasets)
Requires external server	❌	✅ (often needs Redis/FAISS/Weaviate/etc.)
Memory-efficient	✅	❌
Easy to use SQL	✅	❌ (often complex JOINs, subqueries)
Offline/Edge ready	✅	❌
Cross-platform	✅	❌

Unlike other vector databases or extensions that require complex setup, SQLite-Vector just works with your existing database schema and tools.

📦 Installation

Pre-built Binaries

Download the appropriate pre-built binary for your platform from the official Releases page:

• Linux: x86 and ARM
• macOS: x86 and ARM
• Windows: x86
• Android
• iOS

Loading the Extension

-- In SQLite CLI
.load ./vector

-- In SQL
SELECT load_extension('./vector');

Or embed it directly into your application.

Example Usage

-- Create a regular SQLite table
CREATE TABLE images (
  id INTEGER PRIMARY KEY,
  embedding BLOB, -- store Float32/UInt8/etc.
  label TEXT
);

-- Insert a BLOB vector (Float32, 384 dimensions) using bindings
INSERT INTO images (embedding, label) VALUES (?, 'cat');

-- Insert a JSON vector (Float32, 384 dimensions)
INSERT INTO images (embedding, label) VALUES (vector_convert_f32('[0.3, 1.0, 0.9, 3.2, 1.4,...]'), 'dog');

-- Initialize the vector. By default, the distance function is L2.
-- To use a different metric, specify one of the following options:
-- distance=L1, distance=COSINE, distance=DOT, or distance=SQUARED_L2.
SELECT vector_init('images', 'embedding', 'type=FLOAT32,dimension=384');

-- Quantize vector
SELECT vector_quantize('images', 'embedding');

-- Optional preload quantized version in memory (for a 4x/5x speedup) 
SELECT vector_quantize_preload('images', 'embedding');

-- Run a nearest neighbor query on the quantized version (returns top 20 closest vectors)
SELECT e.id, v.distance FROM images AS e
   JOIN vector_quantize_scan('images', 'embedding', ?, 20) AS v
   ON e.id = v.rowid;

📋 Documentation

Extensive API documentation can be found in the API page.

More information about the quantization process can be found in the QUANTIZATION document.

🧩 Features

Instant Vector Search – No Preindexing Required

Unlike other SQLite vector extensions that rely on complex indexing algorithms such as DiskANN, HNSW, or IVF, which often require preprocessing steps that can take hours or even days, sqlite-vector works out of the box with your existing data. There’s no need to preindex your vectors—you can start performing fast, approximate or exact vector searches immediately.

This means:

• 🕒 No waiting time before your app or service is usable
• 🔄 Zero-cost updates – you can add, remove, or modify vectors on the fly without rebuilding any index
• ⚡ Works directly with BLOB columns in ordinary SQLite tables – no special schema or virtual table required
• 🌍 Ideal for edge and mobile use cases, where preprocessing large datasets is not practical or possible

By eliminating the need for heavyweight indexing, sqlite-vector offers a simpler, faster, and more developer-friendly approach to embedding vector search in your applications.

Supported Vector Types

You can store your vectors as BLOB columns in ordinary tables. Supported formats include:

• float32 (4 bytes per element)
• float16 (2 bytes per element)
• bfloat16 (2 bytes per element)
• int8 (1 byte per element)
• uint8 (1 byte per element)

Simply insert a vector as a binary blob into your table. No special table types or schemas are required.

Supported Distance Metrics

Optimized implementations available:

• L2 Distance (Euclidean)
• Squared L2
• L1 Distance (Manhattan)
• Cosine Distance
• Dot Product

These are implemented in pure C and optimized for SIMD when available, ensuring maximum performance on modern CPUs and mobile devices.

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🧠 What Is Vector Search?

Vector search is the process of finding the closest match(es) to a given vector (a point in high-dimensional space) based on a similarity or distance metric. It is essential for AI and machine learning applications where data is often encoded into vector embeddings.

Common Use Cases

• Semantic Search: find documents, emails, or messages similar to a query
• Image Retrieval: search for visually similar images
• Recommendation Systems: match users with products, videos, or music
• Voice and Audio Search: match voice queries or environmental sounds
• Anomaly Detection: find outliers in real-time sensor data
• Robotics: localize spatial features or behaviors using embedded observations

In the AI era, embeddings are everywhere – from language models like GPT to vision transformers. Storing and searching them efficiently is the foundation of intelligent applications.

🌍 Perfect for Edge AI

SQLite-Vector is designed with the Edge AI use case in mind:

• 📴 Runs offline – no internet required
• 📱 Works on mobile devices – iOS/Android friendly
• 🔒 Keeps data local – ideal for privacy-focused apps
• ⚡ Extremely fast – real-time performance on device

You can deploy powerful similarity search capabilities right inside your app or embedded system – no cloud needed.

📦 Integrations

Use SQLite-AI alongside:

• SQLite-AI – on-device inference, embedding generation, and model interaction directly into your database
• SQLite-Sync – sync on-device databases with the cloud
• SQLite-JS – define SQLite functions in JavaScript

License

This project is licensed under the Elastic License 2.0. You can use, copy, modify, and distribute it under the terms of the license for non-production use. For production or managed service use, please contact SQLite Cloud, Inc for a commercial license.