A lightweight, high-performance WebAssembly vector database with machine learning capabilities optimized for Apple platforms. Run ML inference, vector search, and personalized recommendations entirely on-device without sending user data to servers.

Key Features

Feature	Description
Privacy-First	All data stays on-device. No PII, coordinates, or content sent anywhere
Dual Target	Single codebase for native iOS (WasmKit) and browser (Safari/Chrome/Firefox)
HNSW Index	Hierarchical Navigable Small World graph for O(log n) similarity search
Q-Learning	Adaptive recommendation engine that learns from user behavior
SIMD Acceleration	Auto-detects and uses WASM SIMD (iOS 16.4+/Safari 16.4+/Chrome 91+)
Memory Efficient	Scalar (4x), Binary (32x), and Product (variable) quantization
Self-Learning	Health, Location, Calendar, App Usage pattern learning
Tiny Footprint	~100KB optimized native / ~200KB browser with all features

Capabilities

Vector Database

HNSW Index: Fast approximate nearest neighbor search
Distance Metrics: Euclidean, Cosine, Manhattan, Dot Product
Persistence: Serialize/deserialize to bytes for storage
Capacity: 100K+ vectors at <50ms search latency

Machine Learning

Embeddings: Hash-based text embeddings (64-512 dims)
Attention: Multi-head attention for ranking
Q-Learning: Adaptive recommendations with exploration/exploitation
Pattern Recognition: Time-based behavioral patterns

Privacy-Preserving Learning

Module	What It Learns	What It NEVER Stores
Health	Activity patterns, sleep schedules	Actual health values, medical data
Location	Place categories, time at venues	GPS coordinates, addresses
Calendar	Busy times, meeting patterns	Event titles, attendees, content
Communication	Response patterns, quiet hours	Message content, contact names
App Usage	Screen time, category patterns	App names, usage details

Quick Start

Browser (Safari/Chrome/Firefox)

<script type="module">
import init, { VectorDatabaseJS, dot_product } from './ruvector_ios_wasm.js';

await init();

// Create vector database
const db = new VectorDatabaseJS(128, 'cosine', 'none');

// Insert vectors
const embedding = new Float32Array(128);
embedding.fill(0.5);
db.insert(1n, embedding);

// Search
const results = db.search(embedding, 10);
console.log('Nearest neighbors:', results);
</script>

Native iOS (WasmKit)

import Foundation

// Load WASM module
let ruvector = RuvectorWasm.shared
try ruvector.load(from: Bundle.main.path(forResource: "ruvector", ofType: "wasm")!)

// Initialize learners
try ruvector.initIOSLearner()

// Record app usage
let session = AppUsageSession(
    category: .productivity,
    durationSeconds: 1800,
    hour: 14,
    dayOfWeek: 2,
    isActiveUse: true
)
try ruvector.learnAppSession(session)

// Get recommendations
let context = IOSContext(
    hour: 15,
    dayOfWeek: 2,
    batteryLevel: 80,
    networkType: 1,
    locationCategory: .work,
    recentAppCategory: .productivity,
    activityLevel: 5,
    healthScore: 0.8
)
let recommendations = try ruvector.getRecommendations(context)
print("Suggested: \(recommendations.suggestedAppCategory)")

SwiftUI Integration

import SwiftUI

struct ContentView: View {
    @StateObject private var ruvector = RuvectorViewModel()

    var body: some View {
        VStack {
            if ruvector.isReady {
                Text("Screen Time: \(ruvector.screenTimeHours, specifier: "%.1f")h")
                Text("Focus Score: \(Int(ruvector.focusScore * 100))%")
            } else {
                ProgressView("Loading AI...")
            }
        }
        .task {
            try? await ruvector.load(from: Bundle.main.path(forResource: "ruvector", ofType: "wasm")!)
        }
    }
}

Building

Prerequisites

Rust 1.70+ with WASM targets
wasm-opt (optional, for size optimization)

Native WASI Build (for WasmKit/iOS)

# Add WASI target
rustup target add wasm32-wasip1

# Build optimized native WASM
cargo build --release --target wasm32-wasip1

# Optimize size (optional)
wasm-opt -Oz -o ruvector.wasm target/wasm32-wasip1/release/ruvector_ios_wasm.wasm

Browser Build (wasm-bindgen)

# Add browser target
rustup target add wasm32-unknown-unknown

# Build with browser feature
cargo build --release --target wasm32-unknown-unknown --features browser

# Generate JS bindings
wasm-bindgen target/wasm32-unknown-unknown/release/ruvector_ios_wasm.wasm \
  --out-dir pkg --target web

Build Options

Feature	Flag	Description
browser	`--features browser`	wasm-bindgen JS bindings
simd	`--features simd`	WASM SIMD acceleration
full	`--features full`	All features

Benchmarks

Tested on Apple M2 (native) and Safari 17 (browser):

Vector Operations (128 dims, 10K iterations)

Operation	Native	Browser	Ops/sec
Dot Product	0.8ms	1.2ms	8M+
L2 Distance	0.9ms	1.4ms	7M+
Cosine Similarity	1.1ms	1.6ms	6M+

HNSW Index (64 dims)

Operation	1K vectors	10K vectors	100K vectors
Insert	2.3ms	45ms	890ms
Search (k=10)	0.05ms	0.3ms	2.1ms
Search QPS	20,000	3,300	476

Memory Usage

Vectors	No Quant	Scalar (4x)	Binary (32x)
1,000	512 KB	128 KB	16 KB
10,000	5.1 MB	1.3 MB	160 KB
100,000	51 MB	13 MB	1.6 MB

Binary Size

Configuration	Size
Native WASI (optimized)	103 KB
Native WASI (debug)	141 KB
Browser (full features)	357 KB
Browser + gzip	~120 KB

Comparison

vs. Other WASM Vector DBs

Feature	Ruvector iOS	HNSWLIB-WASM	Vectra.js
Native iOS (WasmKit)	Yes	No	No
Safari Support	Yes	Partial	Yes
Quantization	3 modes	None	Scalar
ML Integration	Q-Learning, Attention	None	None
Privacy Learning	5 modules	None	None
Binary Size	103KB	450KB	280KB
SIMD	Auto-detect	Manual	No

vs. Native Swift Solutions

Aspect	Ruvector iOS WASM	Native Swift
Development	Single Rust codebase	Swift only
Cross-platform	iOS + Safari + Chrome	iOS only
Performance	90-95% native	100%
Binary Size	+100KB	Varies
Updates	Hot-loadable	App Store

Tutorials

1. Building a Recommendation Engine

import init, { RecommendationEngineJS } from './ruvector_ios_wasm.js';

await init();

// Create engine with 64-dim embeddings
const engine = new RecommendationEngineJS(64, 10000);

// Add items (products, articles, etc.)
const productEmbedding = new Float32Array(64);
productEmbedding.set([0.1, 0.2, 0.3, /* ... */]);
engine.add_item(123n, productEmbedding);

// Record user interactions
engine.record_interaction(1n, 123n, 1.0);  // User 1 clicked item 123

// Get personalized recommendations
const recs = engine.recommend(1n, 10);
for (const rec of recs) {
    console.log(`Item ${rec.item_id}: score ${rec.score.toFixed(3)}`);
}

2. Privacy-Preserving Health Insights

import init, { HealthLearnerJS, HealthMetrics } from './ruvector_ios_wasm.js';

await init();

const health = new HealthLearnerJS();

// Learn from HealthKit data (values normalized to 0-9 buckets)
health.learn_event({
    metric: HealthMetrics.STEPS,
    value_bucket: 7,  // High activity (buckets hide actual step count)
    hour: 8,
    day_of_week: 1
});

// Predict typical activity level
const predictedBucket = health.predict(HealthMetrics.STEPS, 8, 1);
console.log(`Usually active at 8am Monday: bucket ${predictedBucket}`);

// Get overall activity score
console.log(`Activity score: ${(health.activity_score() * 100).toFixed(0)}%`);

3. Smart Focus Time Suggestions

import init, { CalendarLearnerJS, CalendarEventTypes } from './ruvector_ios_wasm.js';

await init();

const calendar = new CalendarLearnerJS();

// Learn from calendar events (no titles stored)
calendar.learn_event({
    event_type: CalendarEventTypes.MEETING,
    start_hour: 10,
    duration_minutes: 60,
    day_of_week: 1,
    is_recurring: true,
    has_attendees: true
});

// Find best focus time blocks
const focusTimes = calendar.suggest_focus_times(2); // 2-hour blocks
for (const slot of focusTimes) {
    const days = ['Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat'];
    console.log(`${days[slot.day]} ${slot.start_hour}:00 - Score: ${slot.score.toFixed(2)}`);
}

// Check if specific time is likely busy
const busy = calendar.busy_probability(14, 2);
console.log(`Tuesday 2pm busy probability: ${(busy * 100).toFixed(0)}%`);

4. Digital Wellbeing Dashboard

import init, { AppUsageLearnerJS, AppCategories } from './ruvector_ios_wasm.js';

await init();

const usage = new AppUsageLearnerJS();

// Track app sessions (category only, not app names)
usage.learn_session({
    category: AppCategories.SOCIAL,
    duration_seconds: 1800,
    hour: 20,
    day_of_week: 5,
    is_active_use: true
});

// Get screen time summary
const summary = usage.screen_time_summary();
console.log(`Total: ${summary.total_minutes.toFixed(0)} min`);
console.log(`Top category: ${summary.top_category}`);

// Get wellbeing insights
const insights = usage.wellbeing_insights();
for (const insight of insights) {
    console.log(`[${insight.category}] ${insight.message} (score: ${insight.score})`);
}

5. Context-Aware App Launcher

// Swift example for native iOS
let context = IOSContext(
    hour: 7,
    dayOfWeek: 1,  // Monday morning
    batteryLevel: 100,
    networkType: 1,  // WiFi
    locationCategory: .home,
    recentAppCategory: .utilities,
    activityLevel: 3,
    healthScore: 0.7
)

let recommendations = try ruvector.getRecommendations(context)

// Show suggested apps based on context
switch recommendations.suggestedAppCategory {
case .productivity:
    showWidget("Work Focus")
case .health:
    showWidget("Morning Workout")
case .news:
    showWidget("Morning Brief")
default:
    break
}

// Determine notification priority
if recommendations.optimalNotificationTime {
    enableNotifications()
} else {
    enableFocusMode()
}

6. Semantic Search

import init, { VectorDatabaseJS, dot_product } from './ruvector_ios_wasm.js';

await init();

// Create database with cosine similarity
const db = new VectorDatabaseJS(384, 'cosine', 'scalar');

// In production: use a real embedding model
async function embed(text) {
    // Placeholder - use transformers.js, TensorFlow.js, or remote API
    return new Float32Array(384).fill(0.1);
}

// Index documents
const docs = [
    { id: 1, text: "Machine learning fundamentals" },
    { id: 2, text: "iOS development with Swift" },
    { id: 3, text: "Web performance optimization" },
];

for (const doc of docs) {
    const embedding = await embed(doc.text);
    db.insert(BigInt(doc.id), embedding);
}

// Search
const query = await embed("How to build iOS apps");
const results = db.search(query, 3);

for (const result of results) {
    console.log(`Doc ${result.id}: similarity ${(1 - result.distance).toFixed(3)}`);
}

API Reference

See TypeScript Definitions for complete API documentation.

Core Classes

VectorDatabaseJS - Main vector database with HNSW
HnswIndexJS - Low-level HNSW index
RecommendationEngineJS - Q-learning recommendation engine

Quantization

ScalarQuantizedJS - 8-bit quantization (4x compression)
BinaryQuantizedJS - 1-bit quantization (32x compression)
ProductQuantizedJS - Sub-vector clustering

Learning Modules

HealthLearnerJS - Health/fitness patterns
LocationLearnerJS - Location category patterns
CommLearnerJS - Communication patterns
CalendarLearnerJS - Calendar/schedule patterns
AppUsageLearnerJS - App usage/screen time
iOSLearnerJS - Unified learner with all modules

Platform Support

Platform	Version	SIMD	Notes
iOS (WasmKit)	15.0+	Yes	Native performance
Safari	16.4+	Yes	Full WASM support
Chrome	91+	Yes	Best SIMD support
Firefox	89+	Yes	Full support
Edge	91+	Yes	Chromium-based
Node.js	16+	Yes	Server-side option

License

MIT License - See LICENSE for details.

Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines.