EdgeVec


> The first WASM-native vector database.
> Binary quantization, metadata filtering, memory management — 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: 32x memory reduction via binary quantization, metadata filtering, soft delete, persistence, and sub-millisecond search.

---

Why EdgeVec?

| Feature | EdgeVec | hnswlib-wasm | Pinecone |
|:--------|:-------:|:------------:|:--------:|
| Vector Search | Yes | Yes | Yes |
| Binary Quantization | Yes (32x) | No | No |
| Metadata Filtering | Yes | No | Yes |
| SQL-like Queries | Yes | No | Yes |
| Memory Pressure API | Yes | No | No |
| Soft Delete | Yes | No | Yes |
| Persistence | Yes | No | Yes |
| Browser-native | Yes | Yes | No |
| No server required | Yes | Yes | No |
| Offline capable | Yes | Yes | No |

EdgeVec is the only WASM vector database with binary quantization and filtered search.

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Try It Now

Build filters visually, see live results, copy-paste ready code:

Filter Playground - Interactive filter builder with live sandbox

- Visual filter construction
- 10 ready-to-use examples
- Live WASM execution
- Copy-paste code snippets (JS/TS/React)

---

Quick Start

``bash
npm install edgevec
`

`typescript
import init, { EdgeVec } from 'edgevec';

await init();

// Create index (768D for embeddings like OpenAI, Cohere)
const db = new EdgeVec({ dimensions: 768 });

// Insert vectors with metadata (v0.6.0)
const vector = new Float32Array(768).map(() => Math.random());
const id = db.insertWithMetadata(vector, {
category: "books",
price: 29.99,
inStock: true
});

// Search with filter expression (v0.6.0)
const query = new Float32Array(768).map(() => Math.random());
const results = db.searchWithFilter(query, 'category = "books" AND price < 50', 10);

// Fast BQ search with rescoring — 32x less memory, 95% recall (v0.6.0)
const fastResults = db.searchBQ(query, 10);

// Monitor memory pressure (v0.6.0)
const pressure = db.getMemoryPressure();
if (pressure.level === 'warning') {
db.compact(); // Free deleted vectors
}
`

---

Interactive Demos

Try EdgeVec directly in your browser:

| Demo | Description |
|:-----|:------------|
| Filter Playground v0.7.0 | Visual filter builder with live sandbox (NEW!) |
| v0.6.0 Cyberpunk Demo | BQ vs F32 comparison, metadata filtering, memory pressure |
| Demo Hub | All demos in one place |

Run locally:
| Demo | Path |
|:-----|:-----|
| SIMD Benchmark | wasm/examples/simd_benchmark.html |
| Benchmark Dashboard | wasm/examples/benchmark-dashboard.html |
| Soft Delete Demo | wasm/examples/soft_delete.html |
| Main Demo | wasm/examples/index.html |

`bash

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

EdgeVec v0.7.0 uses SIMD instructions for 2x+ faster vector operations on modern browsers.

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| Dimension | Dot Product | L2 Distance | Throughput |
|:----------|:------------|:------------|:-----------|
| 128 | 55 ns | 66 ns | 2.3 Gelem/s |
| 384 | 188 ns | 184 ns | 2.1 Gelem/s |
| 768 | 374 ns | 358 ns | 2.1 Gelem/s |
| 1536 | 761 ns | 693 ns | 2.1 Gelem/s |

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| Scale | EdgeVec | Target | Status |
|:------|:--------|:-------|:-------|
| 1k vectors | 380 us | <1 ms | 2.6x under |
| 10k vectors | 938 us | <1 ms | PASS |

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| Operation | Time | Throughput |
|:----------|:-----|:-----------|
| 768-bit pair | 4.5 ns | 40 GiB/s |
| Batch 10k | 79 us | 127 Melem/s |

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| Browser | SIMD | Performance |
|:--------|:-----|:------------|
| Chrome 91+ | YES | Full speed |
| Firefox 89+ | YES | Full speed |
| Safari 16.4+ | YES | Full speed (macOS) |
| Edge 91+ | YES | Full speed |
| iOS Safari | NO | Scalar fallback |

> Note: iOS Safari doesn't support WASM SIMD. EdgeVec automatically uses scalar
> fallback, which is ~2x slower but still functional.

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| Package | Size (gzip) | Notes |
|:--------|:------------|:------|
| edgevec | 217 KB | SIMD enabled (541 KB uncompressed) |

Full benchmark report ->

---

Database Features

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32x memory reduction with minimal recall loss:

`javascript
// BQ is auto-enabled for dimensions divisible by 8
const db = new EdgeVec({ dimensions: 768 });

// Raw BQ search (~85% recall, ~5x faster)
const bqResults = db.searchBQ(query, 10);

// BQ + rescore (~95% recall, ~3x faster)
const rescoredResults = db.searchBQRescored(query, 10, 5);
`

| Mode | Memory (100k × 768D) | Speed | Recall@10 |
|:-----|:---------------------|:------|:----------|
| F32 (baseline) | ~300 MB | 1x | 100% |
| BQ raw | ~10 MB | 5x | ~85% |
| BQ + rescore(5) | ~10 MB | 3x | ~95% |

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Insert vectors with metadata, search with SQL-like filter expressions:

`javascript
// Insert with metadata
db.insertWithMetadata(vector, {
category: "electronics",
price: 299.99,
tags: ["featured", "sale"]
});

// Search with filter
db.searchWithFilter(query, 'category = "electronics" AND price < 500', 10);
db.searchWithFilter(query, 'tags ANY ["featured"]', 10); // Array membership

// Complex expressions
db.searchWithFilter(query,
'(category = "electronics" OR category = "books") AND price < 100',
10
);
`

Operators: =, !=, >, <, >=, <=, AND, OR, NOT, ANY

Filter syntax documentation ->

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For functional composition style, use standalone filter functions:

`typescript
import { filter, and, or, eq, gt, lt, between, contains } from 'edgevec';

// Simple conditions
const byCategory = eq('category', 'electronics');
const expensive = gt('price', 1000);

// Compose with and/or
const query = filter(
and(
eq('category', 'electronics'),
gt('price', 100),
lt('price', 1000)
)
);

// Nested logic
const complex = filter(
and(
eq('status', 'active'),
or(
eq('brand', 'Apple'),
eq('brand', 'Samsung'),
eq('brand', 'Google')
)
)
);

// Use in search
const results = await index.search(embedding, 10, { filter: query });
`

Available Functions:
- Comparison: eq, ne, gt, lt, ge, le, between
- String: contains, startsWith, endsWith, like
- Array: inArray, notInArray, any, all, none
- Null: isNull, isNotNull
- Logic: and, or, not, filter
- Special: matchAll, matchNone

25 copy-paste ready filter examples ->

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Monitor and control WASM heap usage:

`javascript
const pressure = db.getMemoryPressure();
// { level: 'normal', usedBytes: 52428800, totalBytes: 268435456, usagePercent: 19.5 }

if (pressure.level === 'warning') {
db.compact(); // Free deleted vectors
}

if (!db.canInsert()) {
console.warn('Memory critical, inserts blocked');
}
`

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`javascript
// 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);
}
`

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

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

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`javascript
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

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

fn main() -> Result<(), Box> {
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(())
}
`

---

React Integration (v0.8.0)

EdgeVec provides React hooks for seamless integration with React 18+ applications.

$3

`bash
npm install edgevec react
`

$3

Initialize an EdgeVec database with automatic WASM loading:

`tsx
import { useEdgeVec } from 'edgevec/react';

function App() {
const { db, isReady, isLoading, error, stats, save } = useEdgeVec({
dimensions: 384,
persistName: 'my-vectors', // Optional: enables IndexedDB persistence
});

if (isLoading) return

Loading EdgeVec...

;
if (error) return

Error: {error.message}

;
if (!isReady) return null;

return

{stats?.count} vectors indexed

;
}
`

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Perform reactive searches that automatically update when the query changes:

`tsx
import { useState } from 'react';
import { useEdgeVec, useSearch } from 'edgevec/react';
import { eq, and, gt } from 'edgevec';

function SearchComponent() {
const { db, isReady } = useEdgeVec({ dimensions: 384 });
const [queryVector, setQueryVector] = useState(null);

const { results, isSearching, searchTime } = useSearch(db, {
vector: queryVector,
k: 10,
filter: and(eq('category', 'documents'), gt('score', 0.5)),
enabled: isReady && queryVector !== null,
debounceMs: 300, // Debounce rapid changes
});

return (

{isSearching && Searching...}
{searchTime && Found in {searchTime.toFixed(1)}ms}

{results.map(result => (

• 
  Score: {result.score.toFixed(4)}
  

))}

);
}
`

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`tsx
import { useState, useCallback } from 'react';
import { useEdgeVec, useSearch } from 'edgevec/react';
import { and, eq, gt } from 'edgevec';

export function SemanticSearch() {
const { db, isReady, stats, save } = useEdgeVec({
dimensions: 384,
persistName: 'semantic-search-demo'
});

const [query, setQuery] = useState('');
const [queryVector, setQueryVector] = useState(null);

const { results, isSearching, searchTime } = useSearch(db, {
vector: queryVector,
k: 10,
filter: and(eq('type', 'document'), gt('relevance', 0.5)),
enabled: isReady,
debounceMs: 300
});

const handleSearch = useCallback(async () => {
if (query.trim()) {
// Your embedding function here
const embedding = await getEmbedding(query);
setQueryVector(embedding);
}
}, [query]);

if (!isReady) return

Initializing...

;

return (

);
}
`

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#### useEdgeVec(options)

| Option | Type | Default | Description |
|:-------|:-----|:--------|:------------|
| dimensions | number | required | Vector dimensions |
| persistName | string | undefined | IndexedDB store name |
| efConstruction | number | 200 | HNSW build parameter |
| m | number | 16 | HNSW connections |

Returns: { db, isReady, isLoading, error, stats, reload, save }

#### useSearch(db, options)

| Option | Type | Default | Description |
|:-------|:-----|:--------|:------------|
| vector | Float32Array \| number[] \| null | required | Query vector |
| k | number | 10 | Number of results |
| filter | FilterExpression \| string | undefined | Filter expression |
| enabled | boolean | true | Enable/disable search |
| debounceMs | number | 0 | Debounce delay |
| includeMetadata | boolean | false | Include metadata |
| includeVectors | boolean | false | Include vectors |

Returns: { results, isSearching, error, searchTime, refetch }

---

Vue Integration (v0.8.0)

EdgeVec provides Vue 3 composables for seamless integration with Vue 3.3+ applications.

$3

`bash
npm install edgevec vue
`

$3

Initialize an EdgeVec database with automatic WASM loading:

`vue



`

$3

Perform reactive searches that automatically update when the query changes:

`vue



`

$3

`vue



`

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#### useEdgeVec(options)

| Option | Type | Default | Description |
|:-------|:-----|:--------|:------------|
| dimensions | number | required | Vector dimensions |
| persistName | string | undefined | IndexedDB store name |
| efConstruction | number | 200 | HNSW build parameter |
| m | number | 16 | HNSW connections |

Returns: { db, isReady, isLoading, error, stats, reload, save }

All reactive values are Vue refs (use .value to access).

#### useSearch(db, options)

| Option | Type | Default | Description |
|:-------|:-----|:--------|:------------|
| vector | Ref\ | required | Query vector (can be ref) |
| k | number \| Ref\ | 10 | Number of results |
| filter | FilterExpression \| string \| Ref | undefined | Filter expression |
| enabled | boolean \| Ref\ \| ComputedRef | true | Enable/disable search |
| debounceMs | number | 0 | Debounce delay |
| includeMetadata | boolean | false | Include metadata |
| includeVectors | boolean | false | Include vectors |

Returns: { results, isSearching, error, searchTime, refetch }

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| Feature | React | Vue |
|:--------|:------|:----|
| State | useState returns value | useEdgeVec returns refs (.value) |
| Enabled condition | enabled: isReady && vector !== null | enabled: computed(() => isReady.value && vector.value !== null)` |
| Reactivity | Explicit dependency arrays | Automatic tracking |
| Options | Raw values only | Supports both raw values and refs |

---

Documentation

| Document | Description |
|:---------|:------------|
| Tutorial | Getting started guide |
| Filter Syntax | Complete filter expression reference |
| Filter Examples | 25 copy-paste ready filter examples |
| Embedding Guide | Ollama, transformers.js, OpenAI integration |
| Database Operations | CRUD operations guide |
| Performance Tuning | HNSW parameter optimization |
| Migration Guide | Migrating from hnswlib, FAISS, Pinecone |
| Comparison | When to use EdgeVec vs alternatives |

---

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

- v0.7.0 — SIMD acceleration (2x+ speedup), First Community Contribution (@jsonMartin — 8.75x Hamming)
- v0.6.0 — Binary quantization (32x memory), metadata storage, memory pressure API
- v0.5.4 — iOS Safari compatibility fixes
- v0.5.3 — crates.io publishing fix (package size reduction)
- v0.5.2 — npm TypeScript compilation fix
- 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

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Contributors

Thank you to everyone who has contributed to EdgeVec!

| Contributor | Contribution |
|:------------|:-------------|
| @jsonMartin | SIMD Hamming distance (PR #4) — 8.75x speedup |

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License

Licensed under either of:

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

at your option.

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