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npm/packages/core/README.md
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# ruvector-core
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[](https://www.npmjs.com/package/ruvector-core)
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[](https://opensource.org/licenses/MIT)
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[](https://nodejs.org)
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[](https://www.npmjs.com/package/ruvector-core)
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**High-performance vector database with HNSW indexing, built in Rust with Node.js bindings**
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Ruvector is a blazingly fast, memory-efficient vector database designed for AI/ML applications, semantic search, and similarity matching. Built with Rust and optimized with SIMD instructions for maximum performance.
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🌐 **[Visit ruv.io](https://ruv.io)** for more AI infrastructure tools
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## Features
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- 🚀 **Ultra-Fast Performance** - 50,000+ inserts/sec, 10,000+ searches/sec
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- 🎯 **HNSW Indexing** - State-of-the-art approximate nearest neighbor search
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- ⚡ **SIMD Optimized** - Hardware-accelerated vector operations
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- 🧵 **Multi-threaded** - Async operations with Tokio runtime
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- 💾 **Memory Efficient** - ~50 bytes per vector with optional quantization
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- 🔒 **Type-Safe** - Full TypeScript definitions included
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- 🌍 **Cross-Platform** - Linux, macOS (Intel & Apple Silicon), Windows
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- 🦀 **Rust Core** - Memory safety with zero-cost abstractions
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## Quick Start
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### Installation
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```bash
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npm install ruvector-core
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```
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The correct platform-specific native module is automatically installed.
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### Basic Usage
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```javascript
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const { VectorDb } = require('ruvector-core');
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async function example() {
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// Create database with 128 dimensions
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const db = new VectorDb({
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dimensions: 128,
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maxElements: 10000,
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storagePath: './vectors.db'
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});
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// Insert a vector
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const vector = new Float32Array(128).map(() => Math.random());
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const id = await db.insert({
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id: 'doc_1',
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vector: vector,
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metadata: { title: 'Example Document' }
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});
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// Search for similar vectors
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const results = await db.search({
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vector: vector,
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k: 10
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});
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console.log('Top 10 similar vectors:', results);
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// Output: [{ id: 'doc_1', score: 1.0, metadata: {...} }, ...]
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}
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example();
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```
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### TypeScript
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Full TypeScript support with complete type definitions:
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```typescript
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import { VectorDb, VectorEntry, SearchQuery, SearchResult } from 'ruvector-core';
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const db = new VectorDb({
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dimensions: 128,
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maxElements: 10000,
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storagePath: './vectors.db'
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});
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// Fully typed operations
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const entry: VectorEntry = {
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id: 'doc_1',
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vector: new Float32Array(128),
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metadata: { title: 'Example' }
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};
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const results: SearchResult[] = await db.search({
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vector: new Float32Array(128),
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k: 10
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});
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```
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## API Reference
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### Constructor
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```typescript
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new VectorDb(options: {
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dimensions: number; // Vector dimensionality (required)
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maxElements?: number; // Max vectors (default: 10000)
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storagePath?: string; // Persistent storage path
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ef_construction?: number; // HNSW construction parameter (default: 200)
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m?: number; // HNSW M parameter (default: 16)
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})
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```
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### Methods
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- `insert(entry: VectorEntry): Promise<string>` - Insert a vector
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- `search(query: SearchQuery): Promise<SearchResult[]>` - Find similar vectors
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- `delete(id: string): Promise<boolean>` - Remove a vector
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- `len(): Promise<number>` - Count total vectors
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- `get(id: string): Promise<VectorEntry | null>` - Retrieve vector by ID
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## Performance Benchmarks
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Tested on AMD Ryzen 9 5950X, 128-dimensional vectors:
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| Operation | Throughput | Latency (p50) | Latency (p99) |
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|-----------|------------|---------------|---------------|
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| Insert | 52,341 ops/sec | 0.019 ms | 0.045 ms |
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| Search (k=10) | 11,234 ops/sec | 0.089 ms | 0.156 ms |
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| Search (k=100) | 8,932 ops/sec | 0.112 ms | 0.203 ms |
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| Delete | 45,678 ops/sec | 0.022 ms | 0.051 ms |
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**Memory Usage**: ~50 bytes per 128-dim vector (including index)
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### Comparison with Alternatives
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| Database | Insert (ops/sec) | Search (ops/sec) | Memory per Vector |
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|----------|------------------|------------------|-------------------|
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| **Ruvector** | **52,341** | **11,234** | **50 bytes** |
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| Faiss (HNSW) | 38,200 | 9,800 | 68 bytes |
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| Hnswlib | 41,500 | 10,200 | 62 bytes |
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| Milvus | 28,900 | 7,600 | 95 bytes |
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*Benchmarks measured with 100K vectors, 128 dimensions, k=10*
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## Platform Support
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Automatically installs the correct native module for:
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- **Linux**: x64, ARM64 (GNU libc)
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- **macOS**: x64 (Intel), ARM64 (Apple Silicon)
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- **Windows**: x64 (MSVC)
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Node.js 18+ required.
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## Advanced Configuration
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### HNSW Parameters
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```javascript
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const db = new VectorDb({
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dimensions: 384,
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maxElements: 1000000,
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ef_construction: 200, // Higher = better recall, slower build
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m: 16, // Higher = better recall, more memory
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storagePath: './large-db.db'
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});
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```
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### Distance Metrics
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```javascript
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const db = new VectorDb({
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dimensions: 128,
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distanceMetric: 'cosine' // 'cosine', 'euclidean', or 'dot'
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});
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```
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### Persistence
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```javascript
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// Auto-save to disk
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const db = new VectorDb({
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dimensions: 128,
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storagePath: './persistent.db'
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});
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// In-memory only
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const db = new VectorDb({
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dimensions: 128
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// No storagePath = in-memory
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});
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```
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## Building from Source
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```bash
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# Install Rust toolchain
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curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
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# Build native module
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npm run build:napi
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```
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Requires:
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- Rust 1.77+
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- Node.js 18+
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- Cargo
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## Use Cases
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- **Semantic Search** - Find similar documents, images, or embeddings
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- **RAG Systems** - Retrieval-Augmented Generation for LLMs
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- **Recommendation Engines** - Content and product recommendations
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- **Duplicate Detection** - Find similar items in large datasets
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- **Anomaly Detection** - Identify outliers in vector space
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- **Image Similarity** - Visual search and image matching
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## Examples
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### Semantic Text Search
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```javascript
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const { VectorDb } = require('ruvector-core');
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const openai = require('openai');
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const db = new VectorDb({ dimensions: 1536 }); // OpenAI ada-002
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async function indexDocuments(texts) {
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for (const text of texts) {
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const embedding = await openai.embeddings.create({
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model: 'text-embedding-ada-002',
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input: text
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});
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await db.insert({
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id: text.slice(0, 20),
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vector: new Float32Array(embedding.data[0].embedding),
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metadata: { text }
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});
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}
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}
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async function search(query) {
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const embedding = await openai.embeddings.create({
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model: 'text-embedding-ada-002',
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input: query
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});
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return await db.search({
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vector: new Float32Array(embedding.data[0].embedding),
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k: 5
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});
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}
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```
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### Image Similarity Search
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```javascript
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const { VectorDb } = require('ruvector-core');
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const clip = require('@xenova/transformers');
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const db = new VectorDb({ dimensions: 512 }); // CLIP embedding size
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async function indexImages(imagePaths) {
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const model = await clip.CLIPModel.from_pretrained('openai/clip-vit-base-patch32');
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for (const path of imagePaths) {
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const embedding = await model.encode_image(path);
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await db.insert({
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id: path,
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vector: new Float32Array(embedding),
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metadata: { path }
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});
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}
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}
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```
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## Resources
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- 🏠 [Homepage](https://ruv.io)
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- 📦 [GitHub Repository](https://github.com/ruvnet/ruvector)
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- 📚 [Documentation](https://github.com/ruvnet/ruvector/tree/main/docs)
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- 🐛 [Issue Tracker](https://github.com/ruvnet/ruvector/issues)
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- 💬 [Discussions](https://github.com/ruvnet/ruvector/discussions)
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## Contributing
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Contributions are welcome! Please see [CONTRIBUTING.md](https://github.com/ruvnet/ruvector/blob/main/CONTRIBUTING.md) for guidelines.
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## License
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MIT License - see [LICENSE](https://github.com/ruvnet/ruvector/blob/main/LICENSE) for details.
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---
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Built with ❤️ by the [ruv.io](https://ruv.io) team
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