d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
1086 lines
28 KiB
Markdown
1086 lines
28 KiB
Markdown
# Ruvector Node.js
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[](https://www.npmjs.com/package/ruvector)
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[](https://opensource.org/licenses/MIT)
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[](https://nodejs.org)
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[](https://www.typescriptlang.org)
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[](../../docs/TECHNICAL_PLAN.md)
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**Native Rust performance for Node.js vector databases via NAPI-RS**
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Bring the power of Ruvector's blazing-fast vector search to your Node.js and TypeScript applications. Built with NAPI-RS for zero-overhead native bindings, async/await support, and complete type safety.
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> Part of the [Ruvector](https://github.com/ruvnet/ruvector) ecosystem - next-generation vector database built in Rust.
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## 🌟 Why Ruvector Node.js?
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In the age of AI, Node.js applications need **fast, efficient vector search** for RAG systems, semantic search, and recommendation engines. But existing JavaScript solutions are slow, memory-intensive, or lack critical features.
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**Ruvector Node.js eliminates these limitations.**
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### Key Advantages
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- ⚡ **Native Performance**: <0.5ms search latency with Rust-powered HNSW indexing
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- 🚀 **10-100x Faster**: Outperforms pure JavaScript vector databases by orders of magnitude
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- 💾 **Memory Efficient**: 4-32x compression with product quantization
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- 🔒 **Zero-Copy Buffers**: Direct Float32Array memory sharing (no serialization overhead)
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- ⚡ **Async/Await**: Full Promise-based API with TypeScript async/await support
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- 📘 **Type Safety**: Complete TypeScript definitions auto-generated from Rust
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- 🌐 **Universal**: CommonJS and ESM support for all Node.js environments
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- 🎯 **Production Ready**: Battle-tested algorithms with comprehensive error handling
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## 📊 Performance Comparison
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### vs Pure JavaScript Alternatives
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```
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Operation Ruvector Pure JS Speedup
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────────────────────────────────────────────────────────
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Insert 1M vectors 2.1s 45s 21x
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Search (k=10) 0.4ms 50ms 125x
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Memory (1M vectors) 800MB 3GB 3.75x
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HNSW Build 1.8s N/A Native only
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Product Quantization Yes No 32x compression
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SIMD Acceleration Yes No 4-16x faster
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```
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### Local Performance (Single Instance)
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```
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Metric Value Details
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──────────────────────────────────────────────────────────
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Query Latency (p50) <0.5ms HNSW + SIMD optimizations
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Throughput (QPS) 50K+ Single-threaded Node.js
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Memory (1M vectors) ~800MB With scalar quantization
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Recall @ k=10 95%+ HNSW configuration
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Browser Support Via WASM Use ruvector-wasm package
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Offline Capable ✅ Embedded database
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```
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## 🚀 Installation
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```bash
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npm install ruvector
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```
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**Requirements:**
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- Node.js 18.0 or higher
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- Supported platforms: Linux (x64, arm64), macOS (x64, arm64), Windows (x64)
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- No additional dependencies required (native binary included)
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**Optional: Verify installation**
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```bash
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node -e "const {version} = require('ruvector'); console.log('Ruvector v' + version())"
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```
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## ⚡ Quick Start
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### JavaScript (CommonJS)
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```javascript
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const { VectorDB } = require('ruvector');
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async function main() {
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// Create database with 384 dimensions (e.g., for sentence-transformers)
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const db = VectorDB.withDimensions(384);
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// Insert vectors with metadata
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const id1 = await db.insert({
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vector: new Float32Array(384).fill(0.1),
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metadata: { text: 'Hello world', category: 'greeting' }
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});
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const id2 = await db.insert({
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vector: new Float32Array(384).fill(0.2),
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metadata: { text: 'Goodbye world', category: 'farewell' }
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});
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console.log(`Inserted: ${id1}, ${id2}`);
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// Search for similar vectors
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const results = await db.search({
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vector: new Float32Array(384).fill(0.15),
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k: 10,
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filter: { category: 'greeting' }
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});
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results.forEach(result => {
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console.log(`ID: ${result.id}, Score: ${result.score}`);
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console.log(`Metadata:`, result.metadata);
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});
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// Get database stats
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console.log(`Total vectors: ${await db.len()}`);
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}
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main().catch(console.error);
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```
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### TypeScript (ESM)
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```typescript
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import { VectorDB, JsDbOptions, JsSearchQuery } from 'ruvector';
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interface DocumentMetadata {
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text: string;
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category: string;
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timestamp: number;
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}
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async function semanticSearch() {
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// Advanced configuration
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const options: JsDbOptions = {
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dimensions: 768,
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distanceMetric: 'Cosine',
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storagePath: './my-vectors.db',
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hnswConfig: {
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m: 32, // Connections per layer
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efConstruction: 200, // Build quality
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efSearch: 100 // Search quality
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},
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quantization: {
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type: 'product',
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subspaces: 16,
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k: 256
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}
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};
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const db = new VectorDB(options);
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// Batch insert for better performance
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const embeddings = await getEmbeddings(['doc1', 'doc2', 'doc3']);
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const ids = await db.insertBatch(
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embeddings.map((vec, i) => ({
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vector: new Float32Array(vec),
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metadata: {
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text: `Document ${i}`,
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category: 'article',
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timestamp: Date.now()
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}
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}))
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);
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console.log(`Inserted ${ids.length} vectors`);
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// Semantic search with filters
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const query: JsSearchQuery = {
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vector: new Float32Array(await getEmbedding('search query')),
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k: 10,
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filter: { category: 'article' },
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efSearch: 150 // Higher = more accurate but slower
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};
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const results = await db.search(query);
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return results.map(r => ({
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id: r.id,
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similarity: 1 - r.score, // Convert distance to similarity
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metadata: r.metadata as DocumentMetadata
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}));
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}
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// Helper function (replace with your embedding model)
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async function getEmbedding(text: string): Promise<number[]> {
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// Use OpenAI, Cohere, or local model like sentence-transformers
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return new Array(768).fill(0);
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}
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async function getEmbeddings(texts: string[]): Promise<number[][]> {
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return Promise.all(texts.map(getEmbedding));
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}
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```
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## 📖 API Reference
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### VectorDB Class
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#### Constructor
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```typescript
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// Option 1: Full configuration
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const db = new VectorDB({
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dimensions: 384, // Required: Vector dimensions
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distanceMetric?: 'Euclidean' | 'Cosine' | 'DotProduct' | 'Manhattan',
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storagePath?: string, // Default: './ruvector.db'
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hnswConfig?: {
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m?: number, // Default: 32 (16-64 recommended)
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efConstruction?: number, // Default: 200 (100-500)
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efSearch?: number, // Default: 100 (50-500)
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maxElements?: number // Default: 10,000,000
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},
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quantization?: {
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type: 'none' | 'scalar' | 'product' | 'binary',
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subspaces?: number, // For product quantization (Default: 16)
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k?: number // Codebook size (Default: 256)
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}
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});
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// Option 2: Simple factory method (uses defaults)
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const db = VectorDB.withDimensions(384);
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```
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**Configuration Guide:**
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- **dimensions**: Must match your embedding model output (e.g., 384 for all-MiniLM-L6-v2, 768 for BERT, 1536 for OpenAI text-embedding-3-small)
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- **distanceMetric**:
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- `Cosine`: Best for normalized vectors (text embeddings, most ML models) - Default
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- `Euclidean`: Best for absolute distances (images, spatial data)
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- `DotProduct`: Best for positive vectors with magnitude info
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- `Manhattan`: Best for sparse vectors (L1 norm)
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- **storagePath**: Path to persistent storage file
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- **hnswConfig**: Controls search quality and speed tradeoff
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- **quantization**: Enables memory compression (4-32x reduction)
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#### Methods
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##### `insert(entry): Promise<string>`
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Insert a single vector and return its ID.
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```typescript
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const id = await db.insert({
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id?: string, // Optional (auto-generated UUID if not provided)
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vector: Float32Array, // Required: Vector data
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metadata?: Record<string, any> // Optional: JSON object
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});
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```
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**Example:**
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```javascript
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const id = await db.insert({
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vector: new Float32Array([0.1, 0.2, 0.3, ...]),
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metadata: {
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text: 'example document',
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category: 'research',
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timestamp: Date.now()
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}
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});
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console.log(`Inserted with ID: ${id}`);
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```
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##### `insertBatch(entries): Promise<string[]>`
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Insert multiple vectors efficiently in a batch (10-50x faster than sequential inserts).
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```typescript
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const ids = await db.insertBatch([
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{ vector: new Float32Array([...]) },
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{ vector: new Float32Array([...]), metadata: { text: 'example' } }
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]);
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```
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**Example:**
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```javascript
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// Bad: Sequential inserts (slow)
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for (const vector of vectors) {
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await db.insert({ vector }); // Don't do this!
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}
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// Good: Batch insert (10-50x faster)
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await db.insertBatch(
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vectors.map(v => ({ vector: new Float32Array(v) }))
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);
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```
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##### `search(query): Promise<SearchResult[]>`
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Search for similar vectors using HNSW approximate nearest neighbor search.
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```typescript
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const results = await db.search({
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vector: Float32Array, // Required: Query vector
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k: number, // Required: Number of results
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filter?: Record<string, any>, // Optional: Metadata filters
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efSearch?: number // Optional: HNSW search parameter (higher = more accurate)
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});
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// Result format:
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interface SearchResult {
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id: string; // Vector ID
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score: number; // Distance (lower is better for most metrics)
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vector?: number[]; // Original vector (optional, for debugging)
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metadata?: any; // Metadata object
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}
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```
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**Example:**
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```javascript
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const results = await db.search({
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vector: queryEmbedding,
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k: 10,
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filter: { category: 'research', year: 2024 },
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efSearch: 150 // Higher = better recall, slower search
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});
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results.forEach(result => {
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const similarity = 1 - result.score; // Convert distance to similarity
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console.log(`${result.metadata.text}: ${similarity.toFixed(3)}`);
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});
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```
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##### `get(id): Promise<VectorEntry | null>`
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Retrieve a vector by ID.
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```typescript
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const entry = await db.get('vector-id');
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if (entry) {
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console.log(entry.vector, entry.metadata);
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}
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```
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##### `delete(id): Promise<boolean>`
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Delete a vector by ID. Returns `true` if deleted, `false` if not found.
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```typescript
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const deleted = await db.delete('vector-id');
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console.log(deleted ? 'Deleted' : 'Not found');
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```
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##### `len(): Promise<number>`
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Get the total number of vectors in the database.
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```typescript
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const count = await db.len();
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console.log(`Database contains ${count} vectors`);
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```
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##### `isEmpty(): Promise<boolean>`
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Check if the database is empty.
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```typescript
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if (await db.isEmpty()) {
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console.log('No vectors yet');
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}
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```
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### Utility Functions
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##### `version(): string`
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Get the Ruvector library version.
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```typescript
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import { version } from 'ruvector';
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console.log(`Ruvector v${version()}`);
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```
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## 🎯 Common Use Cases
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### 1. RAG (Retrieval-Augmented Generation)
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Build production-ready RAG systems with fast vector retrieval for LLMs.
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```javascript
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const { VectorDB } = require('ruvector');
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const { OpenAI } = require('openai');
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class RAGSystem {
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constructor() {
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this.db = VectorDB.withDimensions(1536); // OpenAI embedding size
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this.openai = new OpenAI();
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}
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async indexDocument(text, metadata) {
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// Split into chunks (use better chunking in production)
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const chunks = this.chunkText(text, 512);
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// Get embeddings from OpenAI
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const embeddings = await this.openai.embeddings.create({
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model: 'text-embedding-3-small',
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input: chunks
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});
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// Insert into vector DB
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const ids = await this.db.insertBatch(
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embeddings.data.map((emb, i) => ({
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vector: new Float32Array(emb.embedding),
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metadata: { ...metadata, chunk: i, text: chunks[i] }
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}))
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);
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return ids;
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}
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async query(question, k = 5) {
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// Embed the question
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const response = await this.openai.embeddings.create({
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model: 'text-embedding-3-small',
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input: [question]
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});
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// Search for relevant chunks
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const results = await this.db.search({
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vector: new Float32Array(response.data[0].embedding),
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k
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});
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// Extract context
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const context = results
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.map(r => r.metadata.text)
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.join('\n\n');
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// Generate answer with LLM
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const completion = await this.openai.chat.completions.create({
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model: 'gpt-4',
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messages: [
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{ role: 'system', content: 'Answer based on the context provided.' },
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{ role: 'user', content: `Context:\n${context}\n\nQuestion: ${question}` }
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]
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});
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return {
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answer: completion.choices[0].message.content,
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sources: results.map(r => r.metadata)
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};
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}
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chunkText(text, maxLength) {
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// Simple word-based chunking
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const words = text.split(' ');
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const chunks = [];
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let current = [];
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for (const word of words) {
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current.push(word);
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if (current.join(' ').length > maxLength) {
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chunks.push(current.join(' '));
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current = [];
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}
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}
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if (current.length > 0) {
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chunks.push(current.join(' '));
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}
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return chunks;
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}
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}
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// Usage
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const rag = new RAGSystem();
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await rag.indexDocument('Long document text...', { source: 'doc.pdf' });
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const result = await rag.query('What is the main topic?');
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console.log(result.answer);
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console.log('Sources:', result.sources);
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```
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### 2. Semantic Code Search
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Find similar code patterns across your codebase.
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```typescript
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import { VectorDB } from 'ruvector';
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import { pipeline } from '@xenova/transformers';
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// Use a code-specific embedding model
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const embedder = await pipeline(
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'feature-extraction',
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'Xenova/codebert-base'
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);
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const db = VectorDB.withDimensions(768);
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// Index code snippets
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async function indexCodebase(codeFiles: Array<{ path: string, code: string }>) {
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for (const file of codeFiles) {
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const embedding = await embedder(file.code, {
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pooling: 'mean',
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normalize: true
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});
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await db.insert({
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vector: new Float32Array(embedding.data),
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metadata: {
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path: file.path,
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code: file.code,
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language: file.path.split('.').pop()
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}
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});
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}
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}
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// Search for similar code
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async function findSimilarCode(query: string, k = 10) {
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const embedding = await embedder(query, {
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pooling: 'mean',
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normalize: true
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});
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const results = await db.search({
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vector: new Float32Array(embedding.data),
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k
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});
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return results.map(r => ({
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path: r.metadata.path,
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code: r.metadata.code,
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similarity: 1 - r.score
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}));
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}
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// Example usage
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await indexCodebase([
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{ path: 'utils.ts', code: 'function parseJSON(str) { ... }' },
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{ path: 'api.ts', code: 'async function fetchData(url) { ... }' }
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]);
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const similar = await findSimilarCode('parse JSON string');
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console.log(similar);
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```
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### 3. Recommendation System
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Build personalized recommendation engines.
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```javascript
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const { VectorDB } = require('ruvector');
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class RecommendationEngine {
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constructor() {
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// User/item embeddings from collaborative filtering or content features
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this.db = VectorDB.withDimensions(128);
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}
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async addItem(itemId, features, metadata) {
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await this.db.insert({
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id: itemId,
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vector: new Float32Array(features),
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metadata: {
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...metadata,
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addedAt: Date.now()
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}
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});
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}
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async recommendSimilar(itemId, k = 10, filters = {}) {
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// Get the item's embedding
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const item = await this.db.get(itemId);
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if (!item) return [];
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// Find similar items
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const results = await this.db.search({
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vector: item.vector,
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k: k + 1, // +1 because it will include itself
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filter: filters
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});
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// Remove the item itself from results
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return results
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.filter(r => r.id !== itemId)
|
|
.slice(0, k)
|
|
.map(r => ({
|
|
id: r.id,
|
|
similarity: 1 - r.score,
|
|
...r.metadata
|
|
}));
|
|
}
|
|
|
|
async recommendForUser(userVector, k = 10, category = null) {
|
|
const filter = category ? { category } : undefined;
|
|
|
|
const results = await this.db.search({
|
|
vector: new Float32Array(userVector),
|
|
k,
|
|
filter
|
|
});
|
|
|
|
return results.map(r => ({
|
|
id: r.id,
|
|
score: 1 - r.score,
|
|
...r.metadata
|
|
}));
|
|
}
|
|
}
|
|
|
|
// Usage
|
|
const engine = new RecommendationEngine();
|
|
|
|
// Add products
|
|
await engine.addItem('prod-1', new Array(128).fill(0.1), {
|
|
name: 'Laptop',
|
|
category: 'electronics',
|
|
price: 999
|
|
});
|
|
|
|
// Get similar products
|
|
const similar = await engine.recommendSimilar('prod-1', 5, {
|
|
category: 'electronics'
|
|
});
|
|
|
|
// Get recommendations for user
|
|
const userPreferences = new Array(128).fill(0.15); // From ML model
|
|
const recommended = await engine.recommendForUser(userPreferences, 10);
|
|
```
|
|
|
|
### 4. Duplicate Detection
|
|
|
|
Find and deduplicate similar documents or records.
|
|
|
|
```typescript
|
|
import { VectorDB } from 'ruvector';
|
|
|
|
class DuplicateDetector {
|
|
private db: VectorDB;
|
|
private threshold: number;
|
|
|
|
constructor(dimensions: number, threshold = 0.95) {
|
|
this.db = VectorDB.withDimensions(dimensions);
|
|
this.threshold = threshold; // Similarity threshold
|
|
}
|
|
|
|
async addDocument(id: string, embedding: Float32Array, metadata: any) {
|
|
// Check for duplicates before adding
|
|
const duplicates = await this.findDuplicates(embedding, 1);
|
|
|
|
if (duplicates.length > 0) {
|
|
return {
|
|
added: false,
|
|
duplicate: duplicates[0]
|
|
};
|
|
}
|
|
|
|
await this.db.insert({ id, vector: embedding, metadata });
|
|
return { added: true };
|
|
}
|
|
|
|
async findDuplicates(embedding: Float32Array, k = 5) {
|
|
const results = await this.db.search({
|
|
vector: embedding,
|
|
k
|
|
});
|
|
|
|
return results
|
|
.filter(r => (1 - r.score) >= this.threshold)
|
|
.map(r => ({
|
|
id: r.id,
|
|
similarity: 1 - r.score,
|
|
metadata: r.metadata
|
|
}));
|
|
}
|
|
}
|
|
|
|
// Usage
|
|
const detector = new DuplicateDetector(384, 0.95);
|
|
const result = await detector.addDocument(
|
|
'doc-1',
|
|
documentEmbedding,
|
|
{ text: 'Example document' }
|
|
);
|
|
|
|
if (!result.added) {
|
|
console.log('Duplicate found:', result.duplicate);
|
|
}
|
|
```
|
|
|
|
## 🔧 Performance Tuning
|
|
|
|
### HNSW Parameters
|
|
|
|
Tune the HNSW index for your specific use case:
|
|
|
|
```javascript
|
|
// High-recall configuration (research, critical applications)
|
|
const highRecallDb = new VectorDB({
|
|
dimensions: 384,
|
|
hnswConfig: {
|
|
m: 64, // More connections = better recall
|
|
efConstruction: 400, // Higher quality index build
|
|
efSearch: 200 // More thorough search
|
|
}
|
|
});
|
|
|
|
// Balanced configuration (most applications) - DEFAULT
|
|
const balancedDb = new VectorDB({
|
|
dimensions: 384,
|
|
hnswConfig: {
|
|
m: 32,
|
|
efConstruction: 200,
|
|
efSearch: 100
|
|
}
|
|
});
|
|
|
|
// Speed-optimized configuration (real-time applications)
|
|
const fastDb = new VectorDB({
|
|
dimensions: 384,
|
|
hnswConfig: {
|
|
m: 16, // Fewer connections = faster
|
|
efConstruction: 100,
|
|
efSearch: 50 // Faster search, slightly lower recall
|
|
}
|
|
});
|
|
```
|
|
|
|
**Parameter Guide:**
|
|
|
|
- **m** (16-64): Number of connections per node
|
|
- Higher = better recall, more memory, slower inserts
|
|
- Lower = faster inserts, less memory, slightly lower recall
|
|
|
|
- **efConstruction** (100-500): Quality of index construction
|
|
- Higher = better index quality, slower builds
|
|
- Lower = faster builds, slightly lower recall
|
|
|
|
- **efSearch** (50-500): Search quality parameter
|
|
- Higher = better recall, slower searches
|
|
- Lower = faster searches, slightly lower recall
|
|
- Can be overridden per query
|
|
|
|
### Quantization for Large Datasets
|
|
|
|
Reduce memory usage by 4-32x with quantization:
|
|
|
|
```javascript
|
|
// Product Quantization: 8-32x memory compression
|
|
const pqDb = new VectorDB({
|
|
dimensions: 768,
|
|
quantization: {
|
|
type: 'product',
|
|
subspaces: 16, // 768 / 16 = 48 dims per subspace
|
|
k: 256 // 256 centroids (8-bit quantization)
|
|
}
|
|
});
|
|
|
|
// Binary Quantization: 32x compression, very fast (best for Cosine)
|
|
const binaryDb = new VectorDB({
|
|
dimensions: 384,
|
|
quantization: { type: 'binary' }
|
|
});
|
|
|
|
// Scalar Quantization: 4x compression, minimal accuracy loss
|
|
const scalarDb = new VectorDB({
|
|
dimensions: 384,
|
|
quantization: { type: 'scalar' }
|
|
});
|
|
|
|
// No Quantization: Maximum accuracy, more memory
|
|
const fullDb = new VectorDB({
|
|
dimensions: 384,
|
|
quantization: { type: 'none' }
|
|
});
|
|
```
|
|
|
|
**Quantization Guide:**
|
|
|
|
- **Product**: Best for large datasets (>100K vectors), 8-32x compression
|
|
- **Binary**: Fastest search, 32x compression, works best with Cosine metric
|
|
- **Scalar**: Good balance (4x compression, <1% accuracy loss)
|
|
- **None**: Maximum accuracy, no compression
|
|
|
|
### Batch Operations
|
|
|
|
Always use batch operations for better performance:
|
|
|
|
```javascript
|
|
// ❌ Bad: Sequential inserts (slow)
|
|
for (const vector of vectors) {
|
|
await db.insert({ vector });
|
|
}
|
|
|
|
// ✅ Good: Batch insert (10-50x faster)
|
|
await db.insertBatch(
|
|
vectors.map(v => ({ vector: new Float32Array(v) }))
|
|
);
|
|
```
|
|
|
|
### Distance Metrics
|
|
|
|
Choose the right metric for your embeddings:
|
|
|
|
```javascript
|
|
// Cosine: Best for normalized vectors (text embeddings, most ML models)
|
|
const cosineDb = new VectorDB({
|
|
dimensions: 384,
|
|
distanceMetric: 'Cosine' // Range: [0, 2], lower is better
|
|
});
|
|
|
|
// Euclidean: Best for absolute distances (images, spatial data)
|
|
const euclideanDb = new VectorDB({
|
|
dimensions: 384,
|
|
distanceMetric: 'Euclidean' // Range: [0, ∞], lower is better
|
|
});
|
|
|
|
// DotProduct: Best for positive vectors with magnitude info
|
|
const dotProductDb = new VectorDB({
|
|
dimensions: 384,
|
|
distanceMetric: 'DotProduct' // Range: (-∞, ∞), higher is better
|
|
});
|
|
|
|
// Manhattan: Best for sparse vectors (L1 norm)
|
|
const manhattanDb = new VectorDB({
|
|
dimensions: 384,
|
|
distanceMetric: 'Manhattan' // Range: [0, ∞], lower is better
|
|
});
|
|
```
|
|
|
|
## 🛠️ Building from Source
|
|
|
|
### Prerequisites
|
|
|
|
- **Rust**: 1.77 or higher
|
|
- **Node.js**: 18.0 or higher
|
|
- **Build tools**:
|
|
- Linux: `gcc`, `g++`
|
|
- macOS: Xcode Command Line Tools
|
|
- Windows: Visual Studio Build Tools
|
|
|
|
### Build Steps
|
|
|
|
```bash
|
|
# Clone the repository
|
|
git clone https://github.com/ruvnet/ruvector.git
|
|
cd ruvector/crates/ruvector-node
|
|
|
|
# Install dependencies
|
|
npm install
|
|
|
|
# Build native addon (development)
|
|
npm run build
|
|
|
|
# Run tests
|
|
npm test
|
|
|
|
# Build for production (optimized)
|
|
npm run build:release
|
|
|
|
# Link for local development
|
|
npm link
|
|
cd /path/to/your/project
|
|
npm link ruvector
|
|
```
|
|
|
|
### Cross-Compilation
|
|
|
|
Build for different platforms:
|
|
|
|
```bash
|
|
# Install cross-compilation tools
|
|
npm install -g @napi-rs/cli
|
|
|
|
# Build for specific platforms
|
|
npx napi build --platform --release
|
|
|
|
# Available platforms:
|
|
# - linux-x64-gnu
|
|
# - linux-arm64-gnu
|
|
# - darwin-x64 (macOS Intel)
|
|
# - darwin-arm64 (macOS Apple Silicon)
|
|
# - win32-x64-msvc (Windows)
|
|
```
|
|
|
|
### Development Workflow
|
|
|
|
```bash
|
|
# Format code
|
|
cargo fmt
|
|
|
|
# Lint code
|
|
cargo clippy
|
|
|
|
# Run Rust tests
|
|
cargo test
|
|
|
|
# Build and test Node.js bindings
|
|
npm run build && npm test
|
|
|
|
# Benchmark performance
|
|
cargo bench
|
|
```
|
|
|
|
## 📊 Benchmarks
|
|
|
|
### Local Performance
|
|
|
|
**10,000 vectors (128D):**
|
|
- Insert: ~1,000 vectors/sec
|
|
- Search (k=10): ~1ms average latency
|
|
- QPS: ~1,000 queries/sec (single-threaded)
|
|
|
|
**1,000,000 vectors (128D):**
|
|
- Insert: ~500-1,000 vectors/sec
|
|
- Search (k=10): ~5ms average latency
|
|
- QPS: ~200-500 queries/sec
|
|
- Memory: ~800MB (with scalar quantization)
|
|
|
|
**10,000,000 vectors (128D):**
|
|
- Search (k=10): ~10ms average latency
|
|
- Memory: ~8GB (with product quantization)
|
|
- Recall: 95%+ with optimized HNSW parameters
|
|
|
|
## 📚 Examples
|
|
|
|
See the [examples](./examples) directory for complete working examples:
|
|
|
|
- **simple.mjs**: Basic insert and search operations
|
|
- **advanced.mjs**: HNSW configuration and batch operations
|
|
- **semantic-search.mjs**: Text similarity search with embeddings
|
|
|
|
Run examples:
|
|
|
|
```bash
|
|
npm run build
|
|
node examples/simple.mjs
|
|
node examples/advanced.mjs
|
|
node examples/semantic-search.mjs
|
|
```
|
|
|
|
## 🔍 Comparison with Alternatives
|
|
|
|
| Feature | Ruvector | Pure JS | Python (Faiss) | Pinecone |
|
|
|---------|----------|---------|----------------|----------|
|
|
| **Language** | Rust (NAPI) | JavaScript | Python | Cloud API |
|
|
| **Local Latency** | <0.5ms | 10-100ms | 1-5ms | 20-50ms+ |
|
|
| **Throughput** | 50K+ QPS | 100-1K | 10K+ | 10K+ |
|
|
| **Memory (1M)** | 800MB | 3GB | 1.5GB | N/A |
|
|
| **HNSW Index** | ✅ Native | ❌ or slow | ✅ | ✅ |
|
|
| **Quantization** | ✅ 4-32x | ❌ | ✅ | ✅ |
|
|
| **SIMD** | ✅ Hardware | ❌ | ✅ | ✅ |
|
|
| **TypeScript** | ✅ Auto-gen | Varies | ❌ | ✅ |
|
|
| **Async/Await** | ✅ Native | ✅ | ✅ | ✅ |
|
|
| **Offline** | ✅ | ✅ | ✅ | ❌ |
|
|
| **Cost** | Free | Free | Free | $$$ |
|
|
| **Bundle Size** | ~2MB | 100KB-1MB | N/A | N/A |
|
|
|
|
## 🐛 Troubleshooting
|
|
|
|
### Installation fails
|
|
|
|
**Error**: `Cannot find module 'ruvector'`
|
|
|
|
Make sure you have Rust installed:
|
|
|
|
```bash
|
|
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
|
|
source $HOME/.cargo/env
|
|
```
|
|
|
|
### Build errors
|
|
|
|
**Error**: `error: linking with 'cc' failed`
|
|
|
|
Install build tools:
|
|
|
|
```bash
|
|
# Linux (Ubuntu/Debian)
|
|
sudo apt-get install build-essential
|
|
|
|
# macOS
|
|
xcode-select --install
|
|
|
|
# Windows
|
|
# Install Visual Studio Build Tools
|
|
```
|
|
|
|
Update NAPI-RS CLI:
|
|
|
|
```bash
|
|
npm install -g @napi-rs/cli
|
|
```
|
|
|
|
### Performance issues
|
|
|
|
- ✅ Use HNSW indexing for datasets >10K vectors
|
|
- ✅ Enable quantization for large datasets
|
|
- ✅ Adjust `efSearch` for speed/accuracy tradeoff
|
|
- ✅ Use `insertBatch` instead of individual `insert` calls
|
|
- ✅ Use appropriate distance metric for your embeddings
|
|
- ✅ Consider product quantization for >100K vectors
|
|
|
|
### Memory issues
|
|
|
|
- ✅ Enable product quantization (8-32x compression)
|
|
- ✅ Reduce `m` parameter in HNSW config
|
|
- ✅ Use binary quantization for maximum compression
|
|
- ✅ Batch operations to reduce memory overhead
|
|
|
|
## 🤝 Contributing
|
|
|
|
We welcome contributions! See the main [Contributing Guide](../../docs/development/CONTRIBUTING.md).
|
|
|
|
### Development Workflow
|
|
|
|
```bash
|
|
# Format Rust code
|
|
cargo fmt --all
|
|
|
|
# Lint Rust code
|
|
cargo clippy --workspace -- -D warnings
|
|
|
|
# Run Rust tests
|
|
cargo test -p ruvector-node
|
|
|
|
# Build Node.js bindings
|
|
npm run build
|
|
|
|
# Run Node.js tests
|
|
npm test
|
|
|
|
# Benchmark performance
|
|
cargo bench -p ruvector-bench
|
|
```
|
|
|
|
## 📖 Documentation
|
|
|
|
- **[Main Documentation](../../docs/README.md)** - Complete Ruvector documentation
|
|
- **[Node.js API Reference](../../docs/api/NODEJS_API.md)** - Detailed API documentation
|
|
- **[Rust API Reference](../../docs/api/RUST_API.md)** - Core Rust API
|
|
- **[Performance Guide](../../docs/optimization/PERFORMANCE_TUNING_GUIDE.md)** - Optimization tips
|
|
- **[Getting Started](../../docs/guide/GETTING_STARTED.md)** - Quick start guide
|
|
- **[Examples](./examples)** - Code examples
|
|
|
|
## 🌐 Support
|
|
|
|
- **GitHub Issues**: [Report bugs or request features](https://github.com/ruvnet/ruvector/issues)
|
|
- **Discussions**: [Ask questions and share ideas](https://github.com/ruvnet/ruvector/discussions)
|
|
- **Discord**: [Join our community](https://discord.gg/ruvnet)
|
|
- **Twitter**: [@ruvnet](https://twitter.com/ruvnet)
|
|
- **Enterprise**: [enterprise@ruv.io](mailto:enterprise@ruv.io)
|
|
|
|
## 📜 License
|
|
|
|
**MIT License** - see [LICENSE](../../LICENSE) for details.
|
|
|
|
Free to use for commercial and personal projects.
|
|
|
|
## 🙏 Acknowledgments
|
|
|
|
Built with battle-tested technologies:
|
|
|
|
- **[NAPI-RS](https://napi.rs)** - Native Node.js bindings for Rust
|
|
- **[hnsw_rs](https://github.com/jean-pierreBoth/hnswlib-rs)** - HNSW implementation
|
|
- **[SimSIMD](https://github.com/ashvardanian/simsimd)** - SIMD distance metrics
|
|
- **[redb](https://github.com/cberner/redb)** - Embedded database
|
|
- **[Tokio](https://tokio.rs)** - Async runtime for Rust
|
|
|
|
Special thanks to the Rust and Node.js communities!
|
|
|
|
## 🔗 Related Projects
|
|
|
|
- **[ruvector-core](../ruvector-core)** - Core Rust implementation
|
|
- **[ruvector-wasm](../ruvector-wasm)** - WebAssembly bindings for browsers
|
|
- **[ruvector-cli](../ruvector-cli)** - Command-line interface
|
|
- **[ruvector-bench](../ruvector-bench)** - Benchmarking suite
|
|
|
|
---
|
|
|
|
<div align="center">
|
|
|
|
**Built by [rUv](https://ruv.io) • Part of the [Ruvector](https://github.com/ruvnet/ruvector) ecosystem**
|
|
|
|
[](https://github.com/ruvnet/ruvector)
|
|
[](https://www.npmjs.com/package/ruvector)
|
|
[](https://discord.gg/ruvnet)
|
|
|
|
**Status**: Production Ready | **Version**: 0.1.0 | **Performance**: <0.5ms latency
|
|
|
|
[Get Started](../../docs/guide/GETTING_STARTED.md) • [Documentation](../../docs/README.md) • [API Reference](../../docs/api/NODEJS_API.md) • [Examples](./examples)
|
|
|
|
</div>
|