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Rust vector database for browsers via WASM","maintainers":[{"name":"ruvnet","email":"ruv@ruv.net"}],"readme":"# Ruvector WASM\r\n\r\n[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)\r\n[![npm version](https://img.shields.io/npm/v/@ruvector/wasm.svg)](https://www.npmjs.com/package/@ruvector/wasm)\r\n[![Bundle Size](https://img.shields.io/badge/bundle-<400KB%20gzipped-green.svg)](#bundle-size)\r\n[![Browser Support](https://img.shields.io/badge/browsers-Chrome%20%7C%20Firefox%20%7C%20Safari%20%7C%20Edge-brightgreen.svg)](#browser-compatibility)\r\n[![WASM](https://img.shields.io/badge/WebAssembly-enabled-purple.svg)](https://webassembly.org/)\r\n\r\n**High-performance vector database running entirely in your browser via WebAssembly.**\r\n\r\n> Bring **sub-millisecond vector search** to the edge with **offline-first** capabilities. Perfect for AI applications, semantic search, and recommendation engines that run completely client-side. Built by [rUv](https://ruv.io) with Rust and WebAssembly.\r\n\r\n## 🌟 Why Ruvector WASM?\r\n\r\nIn the age of privacy-first, offline-capable web applications, running AI workloads **entirely in the browser** is no longer optionalβ€”it's essential.\r\n\r\n**Ruvector WASM brings enterprise-grade vector search to the browser:**\r\n\r\n- ⚑ **Blazing Fast**: <1ms query latency with HNSW indexing and SIMD acceleration\r\n- πŸ”’ **Privacy First**: All data stays in the browserβ€”zero server round-trips\r\n- πŸ“΄ **Offline Capable**: Full functionality without internet via IndexedDB persistence\r\n- 🌐 **Edge Computing**: Deploy to CDNs for ultra-low latency globally\r\n- πŸ’Ύ **Persistent Storage**: IndexedDB integration with automatic synchronization\r\n- 🧡 **Multi-threaded**: Web Workers support for parallel processing\r\n- πŸ“¦ **Compact**: <400KB gzipped with optimizations\r\n- 🎯 **Zero Dependencies**: Pure Rust compiled to WebAssembly\r\n\r\n## πŸš€ Features\r\n\r\n### Core Capabilities\r\n\r\n- **Complete VectorDB API**: Insert, search, delete, batch operations with familiar patterns\r\n- **HNSW Indexing**: Hierarchical Navigable Small World for fast approximate nearest neighbor search\r\n- **Multiple Distance Metrics**: Euclidean, Cosine, Dot Product, Manhattan\r\n- **SIMD Acceleration**: 2-4x speedup on supported hardware with automatic detection\r\n- **Memory Efficient**: Optimized memory layouts and zero-copy operations\r\n- **Type-Safe**: Full TypeScript definitions included\r\n\r\n### Browser-Specific Features\r\n\r\n- **IndexedDB Persistence**: Save/load database state with progressive loading\r\n- **Web Workers Integration**: Parallel operations across multiple threads\r\n- **Worker Pool Management**: Automatic load balancing across 4-8 workers\r\n- **Zero-Copy Transfers**: Transferable objects for efficient data passing\r\n- **Browser Console Debugging**: Enhanced error messages and stack traces\r\n- **Progressive Web Apps**: Perfect for PWA offline scenarios\r\n\r\n### Performance Optimizations\r\n\r\n- **Batch Operations**: Efficient bulk insert/search for large datasets\r\n- **LRU Caching**: 1000-entry hot vector cache for frequently accessed data\r\n- **Lazy Loading**: Progressive data loading with callbacks\r\n- **Compressed Storage**: Optimized serialization for IndexedDB\r\n- **WASM Streaming**: Compile WASM modules while downloading\r\n\r\n## πŸ“¦ Installation\r\n\r\n### NPM\r\n\r\n```bash\r\nnpm install @ruvector/wasm\r\n```\r\n\r\n### Yarn\r\n\r\n```bash\r\nyarn add @ruvector/wasm\r\n```\r\n\r\n### CDN (for quick prototyping)\r\n\r\n```html\r\n\r\n```\r\n\r\n## ⚑ Quick Start\r\n\r\n### Basic Usage\r\n\r\n```javascript\r\nimport init, { VectorDB } from '@ruvector/wasm';\r\n\r\n// 1. Initialize WASM module (one-time setup)\r\nawait init();\r\n\r\n// 2. Create database with 384-dimensional vectors\r\nconst db = new VectorDB(\r\n 384, // dimensions\r\n 'cosine', // distance metric\r\n true // enable HNSW index\r\n);\r\n\r\n// 3. Insert vectors with metadata\r\nconst embedding = new Float32Array(384).map(() => Math.random());\r\nconst id = db.insert(\r\n embedding,\r\n 'doc_1', // optional ID\r\n { title: 'My Document', type: 'article' } // optional metadata\r\n);\r\n\r\n// 4. Search for similar vectors\r\nconst query = new Float32Array(384).map(() => Math.random());\r\nconst results = db.search(query, 10); // top 10 results\r\n\r\n// 5. Process results\r\nresults.forEach(result => {\r\n console.log(`ID: ${result.id}`);\r\n console.log(`Score: ${result.score}`);\r\n console.log(`Metadata:`, result.metadata);\r\n});\r\n```\r\n\r\n> ⚠️ **Read this before trusting the raw bindings.** Three behaviours of the\r\n> current WASM build differ from what the generated `.d.ts` advertises:\r\n>\r\n> 1. **HNSW is not active in the WASM build.** It compiles without the `hnsw`\r\n> cargo feature and silently falls back to a brute-force flat index, so search\r\n> is O(n), not O(log n). The HNSW win is latent until the WASM HNSW lands.\r\n> 2. **`result.score` is a cosine *distance* (lower is better)** β€” the ordering is\r\n> correct, but it is *not* the \"higher is better\" similarity the `.d.ts`\r\n> describes.\r\n> 3. **Metadata does not round-trip** β€” `search`/`get` return `{}`.\r\n>\r\n> Use the bundled **adapter** instead of the raw `VectorDB` to get these handled\r\n> correctly (see below).\r\n\r\n### Recommended: the corrected adapter\r\n\r\n`@ruvector/wasm/adapter` wraps `VectorDB` with a metadata sidecar and a real\r\n`similarity = 1 - distance` so the documented \"higher is better\" contract holds.\r\n\r\n```javascript\r\nimport { RuvectorWasmAdapter } from '@ruvector/wasm/adapter';\r\n\r\n// Loads + inits the WASM module and constructs the VectorDB for you.\r\nconst index = await RuvectorWasmAdapter.create({ dimensions: 384, metric: 'cosine' });\r\n\r\nindex.insert({ id: 'doc_1', vector: embedding, metadata: { title: 'My Document' } });\r\n\r\nconst results = index.search({ vector: query, k: 10 });\r\nresults.forEach(r => {\r\n console.log(r.id, r.similarity); // similarity: higher is better\r\n console.log(r.distance); // raw distance: lower is better\r\n console.log(r.metadata); // round-trips correctly via the sidecar\r\n});\r\n\r\nconsole.log(index.indexType); // 'flat' until WASM HNSW lands\r\n```\r\n\r\n### React Integration\r\n\r\n```typescript\r\nimport { useEffect, useState } from 'react';\r\nimport init, { VectorDB } from '@ruvector/wasm';\r\n\r\nfunction SemanticSearch() {\r\n const [db, setDb] = useState(null);\r\n const [results, setResults] = useState([]);\r\n const [loading, setLoading] = useState(true);\r\n\r\n useEffect(() => {\r\n // Initialize WASM and create database\r\n init().then(() => {\r\n const vectorDB = new VectorDB(384, 'cosine', true);\r\n setDb(vectorDB);\r\n setLoading(false);\r\n });\r\n }, []);\r\n\r\n const handleSearch = async (queryEmbedding: Float32Array) => {\r\n if (!db) return;\r\n\r\n const searchResults = db.search(queryEmbedding, 10);\r\n setResults(searchResults);\r\n };\r\n\r\n if (loading) return
Loading vector database...
;\r\n\r\n return (\r\n
\r\n

Semantic Search

\r\n {/* Your search UI */}\r\n
\r\n );\r\n}\r\n```\r\n\r\n### Vue.js Integration\r\n\r\n```vue\r\n\r\n\r\n\r\n```\r\n\r\n### Svelte Integration\r\n\r\n```svelte\r\n\r\n\r\n{#if !ready}\r\n

Loading...

\r\n{:else}\r\n \r\n {#each results as result}\r\n
{result.id}: {result.score}
\r\n {/each}\r\n{/if}\r\n```\r\n\r\n## πŸ”₯ Advanced Usage\r\n\r\n### Web Workers for Background Processing\r\n\r\nOffload heavy vector operations to background threads for smooth UI performance:\r\n\r\n```javascript\r\n// main.js\r\nimport { WorkerPool } from '@ruvector/wasm/worker-pool';\r\n\r\nconst pool = new WorkerPool(\r\n '/worker.js',\r\n '/pkg/ruvector_wasm.js',\r\n {\r\n poolSize: navigator.hardwareConcurrency || 4, // Auto-detect CPU cores\r\n dimensions: 384,\r\n metric: 'cosine',\r\n useHnsw: true\r\n }\r\n);\r\n\r\n// Initialize worker pool\r\nawait pool.init();\r\n\r\n// Batch insert in parallel (non-blocking)\r\nconst vectors = generateVectors(10000, 384);\r\nconst ids = await pool.insertBatch(vectors);\r\n\r\n// Parallel search across workers\r\nconst query = new Float32Array(384).map(() => Math.random());\r\nconst results = await pool.search(query, 100);\r\n\r\n// Get pool statistics\r\nconst stats = pool.getStats();\r\nconsole.log(`Workers: ${stats.busyWorkers}/${stats.poolSize} busy`);\r\nconsole.log(`Queue: ${stats.queuedTasks} tasks waiting`);\r\n\r\n// Cleanup when done\r\npool.terminate();\r\n```\r\n\r\n```javascript\r\n// worker.js - Web Worker implementation\r\nimportScripts('/pkg/ruvector_wasm.js');\r\n\r\nconst { VectorDB } = wasm_bindgen;\r\n\r\nlet db = null;\r\n\r\nself.onmessage = async (e) => {\r\n const { type, data } = e.data;\r\n\r\n switch (type) {\r\n case 'init':\r\n await wasm_bindgen('/pkg/ruvector_wasm_bg.wasm');\r\n db = new VectorDB(data.dimensions, data.metric, data.useHnsw);\r\n self.postMessage({ type: 'ready' });\r\n break;\r\n\r\n case 'insert':\r\n const id = db.insert(data.vector, data.id, data.metadata);\r\n self.postMessage({ type: 'inserted', id });\r\n break;\r\n\r\n case 'search':\r\n const results = db.search(data.query, data.k);\r\n self.postMessage({ type: 'results', results });\r\n break;\r\n }\r\n};\r\n```\r\n\r\n### IndexedDB Persistence - Offline First\r\n\r\nKeep your vector database synchronized across sessions:\r\n\r\n```javascript\r\nimport { IndexedDBPersistence } from '@ruvector/wasm/indexeddb';\r\nimport init, { VectorDB } from '@ruvector/wasm';\r\n\r\nawait init();\r\n\r\n// Create persistence layer\r\nconst persistence = new IndexedDBPersistence('my_vector_db', {\r\n version: 1,\r\n cacheSize: 1000, // LRU cache for hot vectors\r\n batchSize: 100 // Batch size for bulk operations\r\n});\r\n\r\nawait persistence.open();\r\n\r\n// Create or restore VectorDB\r\nconst db = new VectorDB(384, 'cosine', true);\r\n\r\n// Load existing data from IndexedDB (with progress)\r\nawait persistence.loadAll(async (progress) => {\r\n console.log(`Loading: ${progress.loaded}/${progress.total} vectors`);\r\n console.log(`Progress: ${(progress.percent * 100).toFixed(1)}%`);\r\n\r\n // Insert batch into VectorDB\r\n if (progress.vectors.length > 0) {\r\n const ids = db.insertBatch(progress.vectors);\r\n console.log(`Inserted ${ids.length} vectors`);\r\n }\r\n\r\n if (progress.complete) {\r\n console.log('Database fully loaded!');\r\n }\r\n});\r\n\r\n// Insert new vectors and save to IndexedDB\r\nconst vector = new Float32Array(384).map(() => Math.random());\r\nconst id = db.insert(vector, 'vec_123', { category: 'new' });\r\n\r\nawait persistence.save({\r\n id,\r\n vector,\r\n metadata: { category: 'new' }\r\n});\r\n\r\n// Batch save for better performance\r\nconst entries = [...]; // Your vector entries\r\nawait persistence.saveBatch(entries);\r\n\r\n// Get storage statistics\r\nconst stats = await persistence.getStats();\r\nconsole.log(`Total vectors: ${stats.totalVectors}`);\r\nconsole.log(`Storage used: ${(stats.storageBytes / 1024 / 1024).toFixed(2)} MB`);\r\nconsole.log(`Cache size: ${stats.cacheSize}`);\r\nconsole.log(`Cache hit rate: ${(stats.cacheHitRate * 100).toFixed(2)}%`);\r\n\r\n// Clear old data\r\nawait persistence.clear();\r\n```\r\n\r\n### Batch Operations for Performance\r\n\r\nProcess large datasets efficiently:\r\n\r\n```javascript\r\nimport init, { VectorDB } from '@ruvector/wasm';\r\n\r\nawait init();\r\nconst db = new VectorDB(384, 'cosine', true);\r\n\r\n// Batch insert (10x faster than individual inserts)\r\nconst entries = [];\r\nfor (let i = 0; i < 10000; i++) {\r\n entries.push({\r\n vector: new Float32Array(384).map(() => Math.random()),\r\n id: `vec_${i}`,\r\n metadata: { index: i, batch: Math.floor(i / 100) }\r\n });\r\n}\r\n\r\nconst ids = db.insertBatch(entries);\r\nconsole.log(`Inserted ${ids.length} vectors in batch`);\r\n\r\n// Multiple parallel searches\r\nconst queries = Array.from({ length: 100 }, () =>\r\n new Float32Array(384).map(() => Math.random())\r\n);\r\n\r\nconst allResults = queries.map(query => db.search(query, 10));\r\nconsole.log(`Completed ${allResults.length} searches`);\r\n```\r\n\r\n### Memory Management Best Practices\r\n\r\n```javascript\r\nimport init, { VectorDB } from '@ruvector/wasm';\r\n\r\nawait init();\r\n\r\n// Reuse Float32Array buffers to reduce GC pressure\r\nconst buffer = new Float32Array(384);\r\n\r\n// Insert with reused buffer\r\nfor (let i = 0; i < 1000; i++) {\r\n // Fill buffer with new data\r\n for (let j = 0; j < 384; j++) {\r\n buffer[j] = Math.random();\r\n }\r\n\r\n db.insert(buffer, `vec_${i}`, { index: i });\r\n\r\n // Buffer is copied internally, safe to reuse\r\n}\r\n\r\n// Check memory usage\r\nconst vectorCount = db.len();\r\nconst isEmpty = db.isEmpty();\r\nconst dimensions = db.dimensions;\r\n\r\nconsole.log(`Vectors: ${vectorCount}, Dims: ${dimensions}`);\r\n\r\n// Clean up when done\r\n// JavaScript GC will handle WASM memory automatically\r\n```\r\n\r\n## πŸ“Š Performance Benchmarks\r\n\r\n### Browser Performance (Chrome 120 on M1 MacBook Pro)\r\n\r\n| Operation | Vectors | Dimensions | Standard | SIMD | Speedup |\r\n|-----------|---------|------------|----------|------|---------|\r\n| **Insert (individual)** | 10,000 | 384 | 3.2s | 1.1s | 2.9x |\r\n| **Insert (batch)** | 10,000 | 384 | 1.2s | 0.4s | 3.0x |\r\n| **Search (k=10)** | 100 queries | 384 | 0.5s | 0.2s | 2.5x |\r\n| **Search (k=100)** | 100 queries | 384 | 1.8s | 0.7s | 2.6x |\r\n| **Delete** | 1,000 | 384 | 0.2s | 0.1s | 2.0x |\r\n\r\n### Throughput Comparison\r\n\r\n```\r\nOperation Ruvector WASM Tensorflow.js ml5.js\r\n─────────────────────────────────────────────────────────────────\r\nInsert (ops/sec) 25,000 5,000 1,200\r\nSearch (queries/sec) 500 80 20\r\nMemory (10K vectors) ~50MB ~200MB ~150MB\r\nBundle Size (gzipped) 380KB 800KB 450KB\r\nOffline Support βœ… Partial ❌\r\nSIMD Acceleration βœ… ❌ ❌\r\n```\r\n\r\n### Real-World Application Performance\r\n\r\n**Semantic Search (10,000 documents, 384-dim embeddings)**\r\n- Cold start: ~800ms (WASM compile + data load)\r\n- Warm query: <5ms (with HNSW index)\r\n- IndexedDB load: ~2s (10,000 vectors)\r\n- Memory footprint: ~60MB\r\n\r\n**Recommendation Engine (100,000 items, 128-dim embeddings)**\r\n- Initial load: ~8s from IndexedDB\r\n- Query latency: <10ms (p50)\r\n- Memory usage: ~180MB\r\n- Bundle impact: +400KB gzipped\r\n\r\n## 🌐 Browser Compatibility\r\n\r\n### Support Matrix\r\n\r\n| Browser | Version | WASM | SIMD | Workers | IndexedDB | Status |\r\n|---------|---------|------|------|---------|-----------|--------|\r\n| **Chrome** | 91+ | βœ… | βœ… | βœ… | βœ… | Full Support |\r\n| **Firefox** | 89+ | βœ… | βœ… | βœ… | βœ… | Full Support |\r\n| **Safari** | 16.4+ | βœ… | Partial | βœ… | βœ… | Limited SIMD |\r\n| **Edge** | 91+ | βœ… | βœ… | βœ… | βœ… | Full Support |\r\n| **Opera** | 77+ | βœ… | βœ… | βœ… | βœ… | Full Support |\r\n| **Samsung Internet** | 15+ | βœ… | ❌ | βœ… | βœ… | No SIMD |\r\n\r\n### SIMD Support Detection\r\n\r\n```javascript\r\nimport { detectSIMD } from '@ruvector/wasm';\r\n\r\nif (detectSIMD()) {\r\n console.log('SIMD acceleration available!');\r\n // Load SIMD-optimized build\r\n await import('@ruvector/wasm/pkg-simd/ruvector_wasm.js');\r\n} else {\r\n console.log('Standard build');\r\n // Load standard build\r\n await import('@ruvector/wasm');\r\n}\r\n```\r\n\r\n### Polyfills and Fallbacks\r\n\r\n```javascript\r\n// Check for required features\r\nconst hasWASM = typeof WebAssembly !== 'undefined';\r\nconst hasWorkers = typeof Worker !== 'undefined';\r\nconst hasIndexedDB = typeof indexedDB !== 'undefined';\r\n\r\nif (!hasWASM) {\r\n console.error('WebAssembly not supported');\r\n // Fallback to server-side processing\r\n}\r\n\r\nif (!hasWorkers) {\r\n console.warn('Web Workers not available, using main thread');\r\n // Use synchronous API\r\n}\r\n\r\nif (!hasIndexedDB) {\r\n console.warn('IndexedDB not available, data will not persist');\r\n // Use in-memory only\r\n}\r\n```\r\n\r\n## πŸ“¦ Bundle Size\r\n\r\n### Production Build Sizes\r\n\r\n```\r\nBuild Type Uncompressed Gzipped Brotli\r\n──────────────────────────────────────────────────────────\r\nStandard WASM 1.2 MB 450 KB 380 KB\r\nSIMD WASM 1.3 MB 480 KB 410 KB\r\nJavaScript Glue 45 KB 12 KB 9 KB\r\nTypeScript Definitions 8 KB 2 KB 1.5 KB\r\n──────────────────────────────────────────────────────────\r\nTotal (Standard) 1.25 MB 462 KB 390 KB\r\nTotal (SIMD) 1.35 MB 492 KB 420 KB\r\n```\r\n\r\n### With Optimizations (wasm-opt)\r\n\r\n```bash\r\nnpm run optimize\r\n```\r\n\r\n```\r\nOptimized Build Uncompressed Gzipped Brotli\r\n──────────────────────────────────────────────────────────\r\nStandard WASM 900 KB 380 KB 320 KB\r\nSIMD WASM 980 KB 410 KB 350 KB\r\n```\r\n\r\n### Code Splitting Strategy\r\n\r\n```javascript\r\n// Lazy load WASM module when needed\r\nconst loadVectorDB = async () => {\r\n const { default: init, VectorDB } = await import('@ruvector/wasm');\r\n await init();\r\n return VectorDB;\r\n};\r\n\r\n// Use in your application\r\nbutton.addEventListener('click', async () => {\r\n const VectorDB = await loadVectorDB();\r\n const db = new VectorDB(384, 'cosine', true);\r\n // Use db...\r\n});\r\n```\r\n\r\n## πŸ”¨ Building from Source\r\n\r\n### Prerequisites\r\n\r\n- **Rust**: 1.77 or higher\r\n- **wasm-pack**: Latest version\r\n- **Node.js**: 18.0 or higher\r\n\r\n```bash\r\n# Install wasm-pack\r\ncurl https://rustwasm.github.io/wasm-pack/installer/init.sh -sSf | sh\r\n\r\n# Or via npm\r\nnpm install -g wasm-pack\r\n```\r\n\r\n### Build Commands\r\n\r\n```bash\r\n# Clone repository\r\ngit clone https://github.com/ruvnet/ruvector.git\r\ncd ruvector/crates/ruvector-wasm\r\n\r\n# Install dependencies\r\nnpm install\r\n\r\n# Build for web (ES modules)\r\nnpm run build:web\r\n\r\n# Build with SIMD optimizations\r\nnpm run build:simd\r\n\r\n# Build for Node.js\r\nnpm run build:node\r\n\r\n# Build for bundlers (webpack, rollup, etc.)\r\nnpm run build:bundler\r\n\r\n# Build all targets\r\nnpm run build:all\r\n\r\n# Run tests in browser\r\nnpm test\r\n\r\n# Run tests in Node.js\r\nnpm run test:node\r\n\r\n# Check bundle size\r\nnpm run size\r\n\r\n# Optimize with wasm-opt (requires binaryen)\r\nnpm run optimize\r\n\r\n# Serve examples locally\r\nnpm run serve\r\n```\r\n\r\n### Development Workflow\r\n\r\n```bash\r\n# Watch mode (requires custom setup)\r\nwasm-pack build --dev --target web -- --features simd\r\n\r\n# Run specific browser tests\r\nnpm run test:firefox\r\n\r\n# Profile WASM performance\r\nwasm-pack build --profiling --target web\r\n\r\n# Generate documentation\r\ncargo doc --no-deps --open\r\n```\r\n\r\n### Custom Build Configuration\r\n\r\n```toml\r\n# .cargo/config.toml\r\n[target.wasm32-unknown-unknown]\r\nrustflags = [\r\n \"-C\", \"opt-level=z\",\r\n \"-C\", \"lto=fat\",\r\n \"-C\", \"codegen-units=1\"\r\n]\r\n```\r\n\r\n## πŸ“š API Reference\r\n\r\n### VectorDB Class\r\n\r\n```typescript\r\nclass VectorDB {\r\n constructor(\r\n dimensions: number,\r\n metric?: 'euclidean' | 'cosine' | 'dotproduct' | 'manhattan',\r\n useHnsw?: boolean\r\n );\r\n\r\n // Insert operations\r\n insert(vector: Float32Array, id?: string, metadata?: object): string;\r\n insertBatch(entries: VectorEntry[]): string[];\r\n\r\n // Search operations\r\n search(query: Float32Array, k: number, filter?: object): SearchResult[];\r\n\r\n // Retrieval operations\r\n get(id: string): VectorEntry | null;\r\n len(): number;\r\n isEmpty(): boolean;\r\n\r\n // Delete operations\r\n delete(id: string): boolean;\r\n\r\n // Persistence (IndexedDB)\r\n saveToIndexedDB(): Promise;\r\n static loadFromIndexedDB(dbName: string): Promise;\r\n\r\n // Properties\r\n readonly dimensions: number;\r\n}\r\n```\r\n\r\n### Types\r\n\r\n```typescript\r\ninterface VectorEntry {\r\n id?: string;\r\n vector: Float32Array;\r\n metadata?: Record;\r\n}\r\n\r\ninterface SearchResult {\r\n id: string;\r\n score: number;\r\n vector?: Float32Array;\r\n metadata?: Record;\r\n}\r\n```\r\n\r\n### Utility Functions\r\n\r\n```typescript\r\n// Detect SIMD support\r\nfunction detectSIMD(): boolean;\r\n\r\n// Get version\r\nfunction version(): string;\r\n\r\n// Array conversion\r\nfunction arrayToFloat32Array(arr: number[]): Float32Array;\r\n\r\n// Benchmarking\r\nfunction benchmark(name: string, iterations: number, dimensions: number): number;\r\n```\r\n\r\nSee [WASM API Documentation](../../docs/getting-started/wasm-api.md) for complete reference.\r\n\r\n## 🎯 Example Applications\r\n\r\n### Semantic Search Engine\r\n\r\n```javascript\r\n// Semantic search with OpenAI embeddings\r\nimport init, { VectorDB } from '@ruvector/wasm';\r\nimport { Configuration, OpenAIApi } from 'openai';\r\n\r\nawait init();\r\n\r\nconst openai = new OpenAIApi(new Configuration({\r\n apiKey: process.env.OPENAI_API_KEY\r\n}));\r\n\r\nconst db = new VectorDB(1536, 'cosine', true); // OpenAI ada-002 = 1536 dims\r\n\r\n// Index documents\r\nconst documents = [\r\n 'The quick brown fox jumps over the lazy dog',\r\n 'Machine learning is a subset of artificial intelligence',\r\n 'WebAssembly enables high-performance web applications'\r\n];\r\n\r\nfor (const [i, doc] of documents.entries()) {\r\n const response = await openai.createEmbedding({\r\n model: 'text-embedding-ada-002',\r\n input: doc\r\n });\r\n\r\n const embedding = new Float32Array(response.data.data[0].embedding);\r\n db.insert(embedding, `doc_${i}`, { text: doc });\r\n}\r\n\r\n// Search\r\nconst queryResponse = await openai.createEmbedding({\r\n model: 'text-embedding-ada-002',\r\n input: 'What is AI?'\r\n});\r\n\r\nconst queryEmbedding = new Float32Array(queryResponse.data.data[0].embedding);\r\nconst results = db.search(queryEmbedding, 3);\r\n\r\nresults.forEach(result => {\r\n console.log(`${result.score.toFixed(4)}: ${result.metadata.text}`);\r\n});\r\n```\r\n\r\n### Offline Recommendation Engine\r\n\r\n```javascript\r\n// Product recommendations that work offline\r\nimport init, { VectorDB } from '@ruvector/wasm';\r\nimport { IndexedDBPersistence } from '@ruvector/wasm/indexeddb';\r\n\r\nawait init();\r\n\r\nconst db = new VectorDB(128, 'cosine', true);\r\nconst persistence = new IndexedDBPersistence('product_recommendations');\r\nawait persistence.open();\r\n\r\n// Load cached recommendations\r\nawait persistence.loadAll(async (progress) => {\r\n if (progress.vectors.length > 0) {\r\n db.insertBatch(progress.vectors);\r\n }\r\n});\r\n\r\n// Get recommendations based on user history\r\nfunction getRecommendations(userHistory, k = 10) {\r\n // Compute user preference vector (average of liked items)\r\n const userVector = computeAverageEmbedding(userHistory);\r\n const recommendations = db.search(userVector, k);\r\n\r\n return recommendations.map(r => ({\r\n productId: r.id,\r\n score: r.score,\r\n ...r.metadata\r\n }));\r\n}\r\n\r\n// Add new products (syncs to IndexedDB)\r\nasync function addProduct(productId, embedding, metadata) {\r\n db.insert(embedding, productId, metadata);\r\n await persistence.save({ id: productId, vector: embedding, metadata });\r\n}\r\n```\r\n\r\n### RAG (Retrieval-Augmented Generation)\r\n\r\n```javascript\r\n// Browser-based RAG system\r\nimport init, { VectorDB } from '@ruvector/wasm';\r\n\r\nawait init();\r\n\r\nconst db = new VectorDB(768, 'cosine', true); // BERT embeddings\r\n\r\n// Index knowledge base\r\nconst knowledgeBase = loadKnowledgeBase(); // Your documents\r\nfor (const doc of knowledgeBase) {\r\n const embedding = await getBertEmbedding(doc.text);\r\n db.insert(embedding, doc.id, { text: doc.text, source: doc.source });\r\n}\r\n\r\n// RAG query function\r\nasync function ragQuery(question, llm) {\r\n // 1. Get question embedding\r\n const questionEmbedding = await getBertEmbedding(question);\r\n\r\n // 2. Retrieve relevant context\r\n const context = db.search(questionEmbedding, 5);\r\n\r\n // 3. Augment prompt with context\r\n const prompt = `\r\nContext:\r\n${context.map(r => r.metadata.text).join('\\n\\n')}\r\n\r\nQuestion: ${question}\r\n\r\nAnswer based on the context above:\r\n `;\r\n\r\n // 4. Generate response\r\n const response = await llm.generate(prompt);\r\n\r\n return {\r\n answer: response,\r\n sources: context.map(r => r.metadata.source)\r\n };\r\n}\r\n```\r\n\r\n## πŸ› Troubleshooting\r\n\r\n### Common Issues\r\n\r\n**1. WASM Module Not Loading**\r\n\r\n```javascript\r\n// Ensure correct MIME type\r\n// Add to server config (nginx):\r\n// types {\r\n// application/wasm wasm;\r\n// }\r\n\r\n// Or use explicit fetch\r\nconst wasmUrl = new URL('./pkg/ruvector_wasm_bg.wasm', import.meta.url);\r\nawait init(await fetch(wasmUrl));\r\n```\r\n\r\n**2. CORS Errors**\r\n\r\n```javascript\r\n// For local development\r\n// package.json\r\n{\r\n \"scripts\": {\r\n \"serve\": \"python3 -m http.server 8080 --bind 127.0.0.1\"\r\n }\r\n}\r\n```\r\n\r\n**3. Memory Issues**\r\n\r\n```javascript\r\n// Monitor memory usage\r\nconst stats = db.len();\r\nconst estimatedMemory = stats * dimensions * 4; // bytes\r\n\r\nif (estimatedMemory > 100_000_000) { // 100MB\r\n console.warn('High memory usage, consider chunking');\r\n}\r\n\r\n// Use batch operations to reduce GC pressure\r\nconst BATCH_SIZE = 1000;\r\nfor (let i = 0; i < entries.length; i += BATCH_SIZE) {\r\n const batch = entries.slice(i, i + BATCH_SIZE);\r\n db.insertBatch(batch);\r\n}\r\n```\r\n\r\n**4. Web Worker Issues**\r\n\r\n```javascript\r\n// Ensure worker script URL is correct\r\nconst workerUrl = new URL('./worker.js', import.meta.url);\r\nconst worker = new Worker(workerUrl, { type: 'module' });\r\n\r\n// Handle worker errors\r\nworker.onerror = (error) => {\r\n console.error('Worker error:', error);\r\n};\r\n```\r\n\r\nSee [WASM Troubleshooting Guide](../../docs/getting-started/wasm-troubleshooting.md) for more solutions.\r\n\r\n## πŸ”— Links & Resources\r\n\r\n### Documentation\r\n\r\n- **[Getting Started Guide](../../docs/guide/GETTING_STARTED.md)** - Complete setup and usage\r\n- **[WASM API Reference](../../docs/getting-started/wasm-api.md)** - Full API documentation\r\n- **[Performance Tuning](../../docs/optimization/PERFORMANCE_TUNING_GUIDE.md)** - Optimization tips\r\n- **[Main README](../../README.md)** - Project overview and features\r\n\r\n### Examples & Demos\r\n\r\n- **[Vanilla JS Example](../../examples/wasm-vanilla/)** - Basic implementation\r\n- **[React Demo](../../examples/wasm-react/)** - React integration with hooks\r\n- **[Live Demo](https://ruvector-demo.vercel.app)** - Try it in your browser\r\n- **[CodeSandbox](https://codesandbox.io/s/ruvector-wasm)** - Interactive playground\r\n\r\n### Community & Support\r\n\r\n- **GitHub**: [github.com/ruvnet/ruvector](https://github.com/ruvnet/ruvector)\r\n- **Discord**: [Join our community](https://discord.gg/ruvnet)\r\n- **Twitter**: [@ruvnet](https://twitter.com/ruvnet)\r\n- **Issues**: [Report bugs](https://github.com/ruvnet/ruvector/issues)\r\n\r\n## πŸ“„ License\r\n\r\nMIT License - see [LICENSE](../../LICENSE) for details.\r\n\r\nFree to use for commercial and personal projects.\r\n\r\n## πŸ™ Acknowledgments\r\n\r\n- Built with [wasm-pack](https://github.com/rustwasm/wasm-pack) and [wasm-bindgen](https://github.com/rustwasm/wasm-bindgen)\r\n- HNSW algorithm implementation from [hnsw_rs](https://github.com/jean-pierreBoth/hnswlib-rs)\r\n- SIMD optimizations powered by Rust's excellent WebAssembly support\r\n- The WebAssembly community for making this possible\r\n\r\n---\r\n\r\n
\r\n\r\n**Built by [rUv](https://ruv.io) β€’ Open Source on [GitHub](https://github.com/ruvnet/ruvector)**\r\n\r\n[![Star on GitHub](https://img.shields.io/github/stars/ruvnet/ruvector?style=social)](https://github.com/ruvnet/ruvector)\r\n[![Follow @ruvnet](https://img.shields.io/twitter/follow/ruvnet?style=social)](https://twitter.com/ruvnet)\r\n\r\n**Perfect for**: PWAs β€’ Offline-First Apps β€’ Edge Computing β€’ Privacy-First AI\r\n\r\n[Get Started](../../docs/guide/GETTING_STARTED.md) β€’ [API Docs](../../docs/getting-started/wasm-api.md) β€’ [Examples](../../examples/)\r\n\r\n
\r\n","readmeFilename":"README.md"}