EdgeVec

The first WASM-native vector database. Filter, delete, persist — all in the browser.

EdgeVec is an embedded vector database built in Rust with first-class WebAssembly support. It brings server-grade vector database features to the browser: metadata filtering, soft delete, persistence, and sub-millisecond search.

Why EdgeVec?

EdgeVec is the only WASM vector database with filtered search.

Quick Start

npm install edgevec

import init, { EdgeVec, EdgeVecConfig } from 'edgevec';

await init();

// Create a 768-dimensional index
const config = new EdgeVecConfig(768);
const db = new EdgeVec(config);

// Insert vectors with metadata
const vector = new Float32Array(768).map(() => Math.random());
const id = db.insert(vector);
// Store metadata separately for filtering
metadata[id] = { category: "books", price: 29.99, inStock: true };

// Search with filter
const query = new Float32Array(768).map(() => Math.random());
const results = db.search(query, 10);

// Filter results client-side (v0.5 pattern)
const filtered = results.filter(r =>
    metadata[r.id]?.category === "books" &&
    metadata[r.id]?.price < 50
);

// Or use the Filter API for complex expressions
import { Filter } from 'edgevec';
const filter = Filter.parse('category = "books" AND price < 50');

Interactive Demos

Try EdgeVec directly in your browser:

# Run demos locally
git clone https://github.com/matte1782/edgevec.git
cd edgevec
python -m http.server 8080
# Open http://localhost:8080/wasm/examples/index.html

Performance

Search Latency (768D vectors, k=10)

Competitive Comparison (10k vectors, 128D)

EdgeVec is 24x faster than voy for search while both are pure WASM.

Bundle Size

Full benchmarks ->

Database Features

Metadata Filtering (v0.5)

EdgeVec supports SQL-like filter expressions:

// Comparison operators
'price > 100'
'category = "electronics"'
'rating >= 4.5'

// Boolean logic
'category = "books" AND price < 50'
'brand = "Sony" OR brand = "Samsung"'
'inStock = true AND NOT discontinued = true'

// Complex expressions
'(category = "electronics" AND price < 500) OR rating >= 4.8'

15 operators supported: =, !=, >, <, >=, <=, IN, NOT IN, CONTAINS, STARTS_WITH, ENDS_WITH, IS NULL, IS NOT NULL, AND, OR, NOT

Filter syntax documentation ->

Soft Delete & Compaction

// O(1) soft delete
db.softDelete(id);

// Check status
console.log('Live:', db.liveCount());
console.log('Deleted:', db.deletedCount());

// Reclaim space when needed
if (db.needsCompaction()) {
    const result = db.compact();
    console.log(`Removed ${result.tombstones_removed} tombstones`);
}

Persistence

// Save to IndexedDB (browser) or filesystem
await db.save("my-vector-db");

// Load existing database
const db = await EdgeVec.load("my-vector-db");

Scalar Quantization

const config = new EdgeVecConfig(768);
config.quantized = true;  // Enable SQ8 quantization

// 3.6x memory reduction: 3.03 GB -> 832 MB at 1M vectors

Rust Usage

use edgevec::{HnswConfig, HnswIndex, VectorStorage};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let config = HnswConfig::new(768);
    let mut storage = VectorStorage::new(&config, None);
    let mut index = HnswIndex::new(config, &storage)?;

    // Insert
    let vector = vec![0.1; 768];
    let id = index.insert(&vector, &mut storage)?;

    // Search
    let query = vec![0.1; 768];
    let results = index.search(&query, 10, &storage)?;

    // Soft delete
    index.soft_delete(id)?;

    Ok(())
}

Documentation

Limitations

EdgeVec is designed for client-side vector search. It is NOT suitable for:

• Billion-scale datasets — Browser memory limits apply (~1GB practical limit)
• Multi-user concurrent access — Single-user, single-tab design
• Distributed deployments — Runs locally only

For these use cases, consider Pinecone, Qdrant, or Weaviate.

Version History

• v0.5.0 — Metadata filtering with SQL-like syntax, Filter Playground demo
• v0.4.0 — Documentation sprint, benchmark dashboard, chaos testing
• v0.3.0 — Soft delete API, compaction, persistence format v3
• v0.2.0 — Scalar quantization (SQ8), SIMD optimization
• v0.1.0 — Initial release with HNSW indexing

License

Licensed under either of:

• Apache License, Version 2.0 (LICENSE-APACHE)
• MIT license (LICENSE-MIT)

at your option.

Built with Rust + WebAssembly

GitHub | npm | crates.io | Demos