Verso

High-performance vector search with HNSW indexing for Bun, Node.js, and Browser.

Performance

| Metric | Value | |--------|-------| | Recall@10 | 100% on 768D Wikipedia embeddings | | Query Performance | 95.5% improvement from baseline | | Memory Reduction | 4x with Int8 quantization |

Features

• HNSW Algorithm - Hierarchical Navigable Small World for fast approximate nearest neighbor search
• Multiple Distance Metrics - Cosine similarity, Euclidean, dot product
• Int8 Quantization - 4x memory reduction with minimal recall loss
• Multi-Platform - Bun/Node.js (file system) and Browser (OPFS)
• Parameter Presets - Pre-tuned configurations for different use cases
• Batch Queries - Efficient batch processing for throughput
• Metadata Filtering - MongoDB-style query operators

Installation

# Bun
bun add verso-db

# npm / Node.js
npm install verso-db

Quick Start

import { VectorDB } from 'verso-db';

const db = new VectorDB({ storagePath: './my_vectors' });
const collection = await db.createCollection('docs', { dimension: 3 });

await collection.add({
  ids: ['a', 'b', 'c'],
  vectors: [new Float32Array([1, 0, 0]), new Float32Array([0, 1, 0]), new Float32Array([0, 0, 1])]
});

const results = await collection.query({ queryVector: new Float32Array([1, 0.1, 0]), k: 2 });
console.log(results.ids);  // ['a', 'b']

await db.close();

Only dimension is required — defaults are metric: 'cosine', M: 16, efConstruction: 200. Use presets to tune for your use case.

With All Options

import { VectorDB, getRecommendedPreset } from 'verso-db';

const db = new VectorDB({ storagePath: './my_vectors' });
const preset = getRecommendedPreset(768);

const collection = await db.createCollection('documents', {
  dimension: 768,
  metric: 'cosine',              // 'cosine' | 'euclidean' | 'dot_product'
  M: preset.M,                   // max connections per node
  efConstruction: preset.efConstruction  // build-time search depth
});

await collection.add({
  ids: ['doc1', 'doc2', 'doc3'],
  vectors: [
    new Float32Array(768).fill(0.1),
    new Float32Array(768).fill(0.2),
    new Float32Array(768).fill(0.3)
  ],
  metadata: [
    { title: 'Document 1', category: 'tech' },
    { title: 'Document 2', category: 'science' },
    { title: 'Document 3', category: 'tech' }
  ]
});

const results = await collection.query({
  queryVector: new Float32Array(768).fill(0.15),
  k: 10,
  efSearch: preset.efSearch,             // query-time search depth
  filter: { category: 'tech' }           // MongoDB-style metadata filter
});

console.log(results.ids);        // ['doc1', 'doc3']
console.log(results.distances);  // [0.01, 0.12]
console.log(results.metadata);   // [{ title: 'Document 1', ... }, ...]

await db.close();

API Reference

VectorDB

Main database class for managing collections.

const db = new VectorDB({ storagePath: './vectors' });

// Create collection
const collection = await db.createCollection('name', {
  dimension: 768,
  metric: 'cosine',  // 'cosine' | 'euclidean' | 'dot_product'
  M: 16,             // Max connections per node
  efConstruction: 200 // Build-time search depth
});

// Get existing collection (returns undefined if not found)
const collection = await db.getCollection('name');

// List collections
const names = await db.listCollections();

// Delete collection
await db.deleteCollection('name');

// Close database
await db.close();

Collection

// Add vectors
await collection.add({
  ids: ['id1', 'id2'],
  vectors: [new Float32Array([...]), new Float32Array([...])],
  metadata: [{ key: 'value' }, { key: 'value' }]  // optional
});

// Upsert vectors (update existing, add new)
await collection.upsert({
  ids: ['id1', 'id3'],  // id1 updated, id3 added
  vectors: [new Float32Array([...]), new Float32Array([...])],
  metadata: [{ key: 'new_value' }, { key: 'value' }]
});

// Query — returns { ids: string[], distances: number[], metadata: object[] }
const results = await collection.query({
  queryVector: new Float32Array([...]),
  k: 10,
  efSearch: 200,           // optional
  filter: { category: 'tech' }  // optional
});

// Batch query
const batchResults = await collection.queryBatch([
  { queryVector: vec1, k: 10 },
  { queryVector: vec2, k: 5, filter: { type: 'article' } }
]);

// Delete a single vector
await collection.delete('id1');

// Delete multiple vectors
await collection.deleteBatch(['id1', 'id2']);

// Check existence
collection.has('id1');     // true if active (not deleted)
collection.count();        // number of active vectors

// Flush pending writes when using Collection directly
await collection.flush();

// Compact — permanently remove deleted vectors and reclaim space
await collection.compact();

Metadata Filtering

const results = await collection.query({
  queryVector: queryVec,
  k: 10,
  filter: {
    category: 'tech',           // Exact match
    score: { $gt: 0.5 },        // Greater than
    tags: { $in: ['ai', 'ml'] } // In array
  }
});

Supported operators: $eq, $ne, $gt, $gte, $lt, $lte, $in, $nin

Parameter Presets

import { getRecommendedPreset, getRAGPreset, PRESETS } from 'verso-db';

// Automatic preset based on dimensions
const preset = getRecommendedPreset(768);

// RAG-optimized preset (high recall) by embedding model name
const ragPreset = getRAGPreset('text-embedding-3-large');

// Named presets (accessed by string key)
PRESETS['low-dim']        // <= 128 dimensions
PRESETS['medium-dim']     // 256-512 dimensions
PRESETS['high-dim']       // 768+ dimensions
PRESETS['very-high-dim']  // 1536+ dimensions
PRESETS['small-dataset']  // < 10k vectors
PRESETS['large-dataset']  // 100k+ vectors
PRESETS['max-recall']     // Prioritize accuracy
PRESETS['low-latency']    // Prioritize speed

Int8 Quantization

Reduce memory usage by 4x with minimal recall loss:

import { ScalarQuantizer, QuantizedVectorStore } from 'verso-db';

// Create quantizer
const quantizer = new ScalarQuantizer(768);

// Train on sample vectors
quantizer.train(sampleVectors);

// Quantize vectors
const quantized = quantizer.quantize(vector);

// Use QuantizedVectorStore for compact in-memory storage
const store = new QuantizedVectorStore(768);
store.addVectors(sampleVectors);
console.log(store.size(), store.memoryUsage());

Storage Backends

Verso automatically selects the appropriate storage backend:

| Environment | Backend | Storage | |-------------|---------|---------| | Bun / Node.js | BunStorageBackend | File system | | Browser | OPFSBackend | Origin Private File System | | Fallback | MemoryBackend | In-memory (no persistence) |

import { createStorageBackend, getRecommendedStorageType } from 'verso-db';

// Automatic detection
const backend = await createStorageBackend({
  path: './vectors'
});

// Check available types
const type = getRecommendedStorageType(); // 'bun' | 'opfs' | 'memory'

Benchmarks

See docs/BENCHMARKS.md for detailed performance analysis.

Quick Summary (768D vectors, Cohere Wikipedia dataset):

• 100% Recall@10 with optimized parameters
• 95.5% query performance improvement through optimizations
• 4x memory reduction with Int8 quantization

Development

# Install dependencies
bun install

# Run tests
bun test

# Run browser tests
bun run test:browser

# Build
bun run build

# Run benchmarks
bun run bench

# Recall benchmark
bun run benchmark:recall

# Storage benchmark
bun run benchmark:storage

# Comprehensive benchmark suite
bun run benchmark:comprehensive

License

MIT