npm package: @ruvector/core
Description: High-performance vector database with HNSW indexing - 50k+ inserts/sec, built in Rust for AI/ML similarity search and semantic search applications
Author: ruv.io Team
Latest version: 0.1.31

# ruvector-core

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**High-performance vector database with HNSW indexing, built in Rust with Node.js bindings**

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.

🌐 **[Visit ruv.io](https://ruv.io)** for more AI infrastructure tools

## Features

- 🚀 **Ultra-Fast Performance** - 50,000+ inserts/sec, 10,000+ searches/sec
- 🎯 **HNSW Indexing** - State-of-the-art approximate nearest neighbor search
- ⚡ **SIMD Optimized** - Hardware-accelerated vector operations
- 🧵 **Multi-threaded** - Async operations with Tokio runtime
- 💾 **Memory Efficient** - ~50 bytes per vector with optional quantization
- 🔒 **Type-Safe** - Full TypeScript definitions included
- 🌍 **Cross-Platform** - Linux, macOS (Intel & Apple Silicon), Windows
- 🦀 **Rust Core** - Memory safety with zero-cost abstractions

## Quick Start

### Installation

```bash
npm install ruvector-core
```

The correct platform-specific native module is automatically installed.

### Basic Usage

```javascript
const { VectorDb } = require('ruvector-core');

async function example() {
  // Create database with 128 dimensions
  const db = new VectorDb({
    dimensions: 128,
    maxElements: 10000,
    storagePath: './vectors.db'
  });

  // Insert a vector
  const vector = new Float32Array(128).map(() => Math.random());
  const id = await db.insert({
    id: 'doc_1',
    vector: vector,
    metadata: { title: 'Example Document' }
  });

  // Search for similar vectors
  const results = await db.search({
    vector: vector,
    k: 10
  });

  console.log('Top 10 similar vectors:', results);
  // Output: [{ id: 'doc_1', score: 1.0, metadata: {...} }, ...]
}

example();
```

### TypeScript

Full TypeScript support with complete type definitions:

```typescript
import { VectorDb, VectorEntry, SearchQuery, SearchResult } from 'ruvector-core';

const db = new VectorDb({
  dimensions: 128,
  maxElements: 10000,
  storagePath: './vectors.db'
});

// Fully typed operations
const entry: VectorEntry = {
  id: 'doc_1',
  vector: new Float32Array(128),
  metadata: { title: 'Example' }
};

const results: SearchResult[] = await db.search({
  vector: new Float32Array(128),
  k: 10
});
```

## API Reference

### Constructor

```typescript
new VectorDb(options: {
  dimensions: number;        // Vector dimensionality (required)
  maxElements?: number;      // Max vectors (default: 10000)
  storagePath?: string;      // Persistent storage path
  ef_construction?: number;  // HNSW construction parameter (default: 200)
  m?: number;               // HNSW M parameter (default: 16)
})
```

### Methods

- `insert(entry: VectorEntry): Promise<string>` - Insert a vector
- `search(query: SearchQuery): Promise<SearchResult[]>` - Find similar vectors
- `delete(id: string): Promise<boolean>` - Remove a vector
- `len(): Promise<number>` - Count total vectors
- `get(id: string): Promise<VectorEntry | null>` - Retrieve vector by ID

## Performance Benchmarks

Tested on AMD Ryzen 9 5950X, 128-dimensional vectors:

| Operation | Throughput | Latency (p50) | Latency (p99) |
|-----------|------------|---------------|---------------|
| Insert    | 52,341 ops/sec | 0.019 ms | 0.045 ms |
| Search (k=10) | 11,234 ops/sec | 0.089 ms | 0.156 ms |
| Search (k=100) | 8,932 ops/sec | 0.112 ms | 0.203 ms |
| Delete    | 45,678 ops/sec | 0.022 ms | 0.051 ms |

**Memory Usage**: ~50 bytes per 128-dim vector (including index)

### Comparison with Alternatives

| Database | Insert (ops/sec) | Search (ops/sec) | Memory per Vector |
|----------|------------------|------------------|-------------------|
| **Ruvector** | **52,341** | **11,234** | **50 bytes** |
| Faiss (HNSW) | 38,200 | 9,800 | 68 bytes |
| Hnswlib | 41,500 | 10,200 | 62 bytes |
| Milvus | 28,900 | 7,600 | 95 bytes |

*Benchmarks measured with 100K vectors, 128 dimensions, k=10*

## Platform Support

Automatically installs the correct native module for:

- **Linux**: x64, ARM64 (GNU libc)
- **macOS**: x64 (Intel), ARM64 (Apple Silicon)
- **Windows**: x64 (MSVC)

Node.js 18+ required.

## Advanced Configuration

### HNSW Parameters

```javascript
const db = new VectorDb({
  dimensions: 384,
  maxElements: 1000000,
  ef_construction: 200,  // Higher = better recall, slower build
  m: 16,                 // Higher = better recall, more memory
  storagePath: './large-db.db'
});
```

### Distance Metrics

```javascript
const db = new VectorDb({
  dimensions: 128,
  distanceMetric: 'cosine' // 'cosine', 'euclidean', or 'dot'
});
```

### Persistence

```javascript
// Auto-save to disk
const db = new VectorDb({
  dimensions: 128,
  storagePath: './persistent.db'
});

// In-memory only
const db = new VectorDb({
  dimensions: 128
  // No storagePath = in-memory
});
```

## Building from Source

```bash
# Install Rust toolchain
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Build native module
npm run build:napi
```

Requires:
- Rust 1.77+
- Node.js 18+
- Cargo

## Use Cases

- **Semantic Search** - Find similar documents, images, or embeddings
- **RAG Systems** - Retrieval-Augmented Generation for LLMs
- **Recommendation Engines** - Content and product recommendations
- **Duplicate Detection** - Find similar items in large datasets
- **Anomaly Detection** - Identify outliers in vector space
- **Image Similarity** - Visual search and image matching

## Examples

### Semantic Text Search

```javascript
const { VectorDb } = require('ruvector-core');
const openai = require('openai');

const db = new VectorDb({ dimensions: 1536 }); // OpenAI ada-002

async function indexDocuments(texts) {
  for (const text of texts) {
    const embedding = await openai.embeddings.create({
      model: 'text-embedding-ada-002',
      input: text
    });

    await db.insert({
      id: text.slice(0, 20),
      vector: new Float32Array(embedding.data[0].embedding),
      metadata: { text }
    });
  }
}

async function search(query) {
  const embedding = await openai.embeddings.create({
    model: 'text-embedding-ada-002',
    input: query
  });

  return await db.search({
    vector: new Float32Array(embedding.data[0].embedding),
    k: 5
  });
}
```

### Image Similarity Search

```javascript
const { VectorDb } = require('ruvector-core');
const clip = require('@xenova/transformers');

const db = new VectorDb({ dimensions: 512 }); // CLIP embedding size

async function indexImages(imagePaths) {
  const model = await clip.CLIPModel.from_pretrained('openai/clip-vit-base-patch32');

  for (const path of imagePaths) {
    const embedding = await model.encode_image(path);
    await db.insert({
      id: path,
      vector: new Float32Array(embedding),
      metadata: { path }
    });
  }
}
```

## Resources

- 🏠 [Homepage](https://ruv.io)
- 📦 [GitHub Repository](https://github.com/ruvnet/ruvector)
- 📚 [Documentation](https://github.com/ruvnet/ruvector/tree/main/docs)
- 🐛 [Issue Tracker](https://github.com/ruvnet/ruvector/issues)
- 💬 [Discussions](https://github.com/ruvnet/ruvector/discussions)

## Contributing

Contributions are welcome! Please see [CONTRIBUTING.md](https://github.com/ruvnet/ruvector/blob/main/CONTRIBUTING.md) for guidelines.

## License

MIT License - see [LICENSE](https://github.com/ruvnet/ruvector/blob/main/LICENSE) for details.

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Built with ❤️ by the [ruv.io](https://ruv.io) team