Package Exports

vectlite
vectlite/index.js

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Readme

vectlite

Embedded vector store for local-first AI applications.

vectlite is a single-file, zero-dependency vector database written in Rust with Node.js bindings. It gives you dense + sparse hybrid search, HNSW indexing, metadata filtering, transactions, and crash-safe persistence in a single .vdb file -- no server, no Docker, no network calls.

Installation

npm install vectlite

Requires Node.js 18+. Pre-built binaries are available for macOS (x86_64, arm64), Linux (x86_64), and Windows (x86_64). Other platforms fall back to compiling from source (requires Rust/Cargo).

Quick Start

const vectlite = require('vectlite')

// Create or open a database
const db = vectlite.open('knowledge.vdb', { dimension: 384 })

// Insert records with vectors, metadata, and sparse terms
db.upsert('doc1', embedding, { source: 'blog', title: 'Auth Guide' })
db.upsert('doc2', embedding2, { source: 'notes', title: 'Billing' })

// Search with filters
const results = db.search(embeddingQuery, { k: 5, filter: { source: 'blog' } })

// Query-free inspection
console.log(db.count({ filter: { source: 'blog' } }))

// Clean up
db.close()

Features

Core

Single-file storage -- one .vdb file per database, portable and easy to back up
Distance metrics -- cosine (default), euclidean (L2), dot product, manhattan (L1) with SIMD acceleration
Dense vectors -- automatic HNSW indexing with metric-aware distance functions
Sparse vectors -- BM25-scored inverted index for keyword retrieval
Hybrid search -- dense + sparse fusion with linear or RRF strategies
Vector quantization -- scalar (int8, 4x), binary (32x), and product quantization (PQ) with 2-stage rescoring
Multi-vector / ColBERT -- late interaction search with per-token MaxSim scoring and 2-bit quantization (~16x compression)
Rich metadata -- string, number, boolean, null, array, and nested object values
Crash-safe WAL -- writes land in a write-ahead log first, then checkpoint with compact()
Transactions -- atomic batched writes with db.transaction()
File locking -- advisory locks prevent corruption from concurrent access

Search & Retrieval

Metadata filters -- MongoDB-style operators: $eq, $ne, $gt, $gte, $lt, $lte, $in, $nin, $contains, $exists, $and, $or, $not
Nested filters -- dot-path traversal (author.name), $elemMatch, $size on arrays and objects
Named vectors -- multiple vector spaces per record (vectors: { title: [...], body: [...] })
Multi-vector queries -- weighted search across vector spaces in a single call
MMR diversification -- mmrLambda controls relevance vs. diversity trade-off
Namespaces -- logical isolation with per-namespace or cross-namespace search
Observability -- searchWithStats() returns timings, BM25 term scores, ANN stats, and per-result explain payloads
Payload indexes -- keyword and numeric indexes on metadata fields accelerate filtered queries on large collections

Data Management

Physical collections -- vectlite.openStore() manages a directory of independent databases
Bulk ingestion -- bulkIngest() with deferred index rebuilds for fast imports
Listing & filtered counts -- list() and count({ namespace, filter }) without a vector query
Delete by filter -- deleteByFilter() for bulk deletion by metadata filter
Partial metadata updates -- updateMetadata() merges a patch without re-writing the vector or rebuilding indexes
Snapshots -- db.snapshot(path) creates a self-contained copy
Backup / Restore -- db.backup(dir) and vectlite.restore(dir, path) for full roundtrips
Read-only mode -- vectlite.open(path, { readOnly: true }) for safe concurrent readers
Explicit close -- db.close() to release locks deterministically
Lock timeouts -- lockTimeout for bounded lock acquisition waits
TTL / Expiry -- setTtl() / clearTtl() or ttl option on insert/upsert; expired records auto-filtered from reads and GC'd on compact
Cursor-based pagination -- listCursor() for efficient iteration over large collections
Async API -- searchAsync(), compactAsync(), flushAsync(), bulkIngestAsync() run on the libuv threadpool

Usage

Distance Metrics

// Default is cosine similarity
const db = vectlite.open('knowledge.vdb', { dimension: 384 })

// Choose a different metric at creation time
const db2 = vectlite.open('knowledge.vdb', { dimension: 384, metric: 'euclidean' })
const db3 = vectlite.open('knowledge.vdb', { dimension: 384, metric: 'dotproduct' })
const db4 = vectlite.open('knowledge.vdb', { dimension: 384, metric: 'manhattan' })

// Aliases: 'l2', 'dot', 'ip', 'l1'
console.log(db2.metric) // "euclidean"

The metric is persisted in the database file. Scores are always oriented so that higher is better.

Hybrid Search

const vectlite = require('vectlite')

const db = vectlite.open('knowledge.vdb', { dimension: 384 })

// Upsert with dense + sparse vectors
db.upsert(
  'doc1',
  denseEmbedding,
  { source: 'docs', title: 'Auth Setup', text: 'How to configure SSO...' },
  { sparse: vectlite.sparseTerms('How to configure SSO authentication') },
)

// Hybrid search
const results = db.search(queryEmbedding, {
  k: 10,
  sparse: vectlite.sparseTerms('SSO authentication'),
  fusion: 'rrf',
  filter: { source: 'docs' },
  explain: true,
})

for (const result of results) {
  console.log(result.id, result.score)
}

Collections

const store = vectlite.openStore('./my_collections')
const products = store.createCollection('products', 384)
products.upsert('p1', embedding, { name: 'Widget', price: 9.99 })

const logs = store.openOrCreateCollection('logs', 128)
console.log(store.collections()) // ["logs", "products"]

products.close()
logs.close()
store.close()

Transactions

const tx = db.transaction()
try {
  tx.upsert('doc1', emb1, { source: 'a' })
  tx.upsert('doc2', emb2, { source: 'b' })
  tx.delete('old_doc')
  tx.commit() // All operations commit atomically
} catch (err) {
  tx.rollback() // Roll back on error
  throw err
}

Text Helpers

async function run() {
  // embedFn can be sync or async
  await vectlite.upsertText(db, 'doc1', 'Auth setup guide', embedFn, { source: 'docs' })
  const results = await vectlite.searchText(db, 'how to authenticate', embedFn, { k: 5 })
}

Snapshots & Backup

db.snapshot('/backups/knowledge_2024.vdb') // Self-contained copy
db.backup('/backups/full/')                // Full backup with ANN sidecars

const restored = vectlite.restore('/backups/full/', 'restored.vdb')

Read-Only Mode

const ro = vectlite.open('knowledge.vdb', { readOnly: true, lockTimeout: 5 })
const results = ro.search(query, { k: 5 }) // Reads work
ro.upsert(...)                              // Throws VectLiteError

Listing, Counting, and Lifecycle

const db = vectlite.open('knowledge.vdb', { dimension: 384, lockTimeout: 5 })

const records = db.list({ namespace: 'docs', filter: { stale: false }, limit: 20 })
const count = db.count({ namespace: 'docs', filter: { source: 'blog' } })
const deleted = db.deleteByFilter({ stale: true }, { namespace: 'docs' })

// Partial metadata update (merge patch -- only touches specified keys)
db.updateMetadata('doc1', { status: 'reviewed', score: 0.95 })

db.close()

Search Diagnostics

const outcome = db.searchWithStats(query, {
  k: 5,
  sparse: terms,
  explain: true,
})

console.log(outcome.stats.timings)      // { dense_us: 120, sparse_us: 45, ... }
console.log(outcome.stats.used_ann)     // true
console.log(outcome.results[0].explain) // Detailed scoring breakdown

Payload Indexes

Create keyword or numeric indexes on metadata fields to accelerate filtered queries on large collections. Indexes are automatically used by search(), count(), and list().

// Create indexes on frequently-filtered fields
db.createIndex('source', 'keyword')   // string equality, $in
db.createIndex('score', 'numeric')    // range queries: $gt, $gte, $lt, $lte

// Filtered queries now use indexes automatically
const count = db.count({ filter: { source: 'blog' } })
const results = db.search(query, { k: 10, filter: { score: { $gte: 0.8 } } })

// Inspect and manage indexes
console.log(db.listIndexes())  // [{ field: 'source', type: 'keyword' }, ...]
db.dropIndex('score')

Vector Quantization

Reduce in-memory candidate-index usage and accelerate search with quantized vectors. All methods use a 2-stage pipeline: fast quantized candidate selection followed by exact float32 rescoring.

// Scalar quantization (int8) -- smaller in-memory candidate index, minimal recall loss
db.enableQuantization('scalar')

// Binary quantization -- smallest in-memory candidate index, best for normalized embeddings
db.enableQuantization('binary', { rescoreMultiplier: 10 })

// Product quantization -- "pq" and "product" are accepted case-insensitively
console.log(db.validNumSubVectors()) // valid PQ partitions for this dimension
db.enableQuantization('pq', { numSubVectors: 16, numCentroids: 256 })

// Search works exactly the same -- quantization accelerates it transparently
const results = db.search(queryEmbedding, { k: 10 })
const sameResults = db.search({ query: queryEmbedding, k: 10 })

// Check quantization status
console.log(db.isQuantized)         // true
console.log(db.quantizationMethod)  // "scalar", "binary", or "product"

// Disable quantization
db.disableQuantization()

rescoreMultiplier controls the number of quantized candidates rescored with exact float32 scoring: k * rescoreMultiplier, capped at the collection size. Increase it to trade latency for recall.

For PQ, numSubVectors must divide the database dimension. If omitted, Vectlite chooses a compatible default; use db.validNumSubVectors() to inspect all valid values.

Quantization does not shrink the .vdb file on disk. Vectlite keeps the original float32 vectors for exact rescoring and stores quantization parameters in a .vdb.quant sidecar file, so total disk footprint can increase slightly. The quantized index auto-rebuilds on inserts and upserts.

Multi-Vector / ColBERT Search

Store token-level embeddings (ColBERT, ColPali) and search with MaxSim late interaction scoring.

// Upsert with per-token ColBERT embeddings
db.upsertMultiVectors('doc1', denseVector,
  { colbert: [tokenVec1, tokenVec2] },
  { metadata: { source: 'paper' } }
)

// MaxSim search
const results = db.searchMultiVector('colbert', queryTokenVectors)

// Enable 2-bit quantization (~16x compression)
db.enableMultiVectorQuantization('colbert')

// Check and disable
console.log(db.isMultiVectorQuantized('colbert'))  // true
db.disableMultiVectorQuantization('colbert')

TTL / Expiry

Records can automatically expire after a time-to-live. Expired records are transparently filtered from all reads and permanently removed on compact().

// Set TTL on insert/upsert (seconds)
db.upsert('session1', embedding, { user: 'alice' }, { ttl: 3600 }) // expires in 1 hour

// Set/clear TTL on existing records
db.setTtl('doc1', 86400)    // expire in 24 hours
db.clearTtl('doc1')          // remove expiry

// Expired records are invisible to get/list/count/search
const record = db.get('session1') // null after TTL elapses

// compact() garbage-collects expired records from disk
db.compact()

Cursor-Based Pagination

Efficiently iterate over large collections without offset overhead.

// Paginate 100 records at a time
let cursor = null
do {
  const page = db.listCursor({ limit: 100, cursor })
  for (const record of page.records) {
    process(record)
  }
  cursor = page.cursor
} while (cursor !== null)

// Works with namespace and filter
const page = db.listCursor({ namespace: 'docs', filter: { source: 'blog' }, limit: 50 })

Async API

Non-blocking versions of heavy operations that run on the libuv threadpool.

// Async search (returns a Promise)
const results = await db.searchAsync(queryEmbedding, { k: 10, filter: { source: 'blog' } })

// Async search with stats
const outcome = await db.searchWithStatsAsync(queryEmbedding, { k: 10 })

// Async maintenance
await db.flushAsync()
await db.compactAsync()

// Async bulk ingestion
const count = await db.bulkIngestAsync(records, { batchSize: 5000 })

OpenTelemetry Integration

vectlite ships with optional OpenTelemetry tracing. When enabled, every search call is wrapped in a span carrying semantic DB attributes and search-specific metrics. @opentelemetry/api is loaded lazily -- it is not a runtime dependency.

const vectlite = require('vectlite')

// Auto-detect: resolves a tracer from @opentelemetry/api if installed
const tracer = vectlite.configureOpenTelemetry()

// Or supply your own tracer
vectlite.configureOpenTelemetry({ tracer: myTracer })

// Custom tracer name (default: 'vectlite')
vectlite.configureOpenTelemetry({ tracerName: 'my-app' })

// Disable
vectlite.configureOpenTelemetry(false)

When a tracer is active, each search / searchWithStats / searchAsync / searchWithStatsAsync call creates a vectlite.search span with these attributes:

Attribute	Description
`db.system`	Always `"vectlite"`
`db.operation.name`	Always `"search"`
`vectlite.search.k`	Requested result count
`vectlite.search.namespace`	Target namespace
`vectlite.search.has_dense`	Whether a dense query vector was provided
`vectlite.search.has_sparse`	Whether sparse terms were provided
`vectlite.search.fusion`	Fusion strategy (`"linear"` or `"rrf"`)
`vectlite.search.used_ann`	Whether HNSW was used (set after completion)
`vectlite.search.result_count`	Number of results returned (set after completion)
`vectlite.search.total_us`	Total search time in microseconds (set after completion)

If a search throws, the span records the exception and sets an error status before re-throwing.

Database Methods Reference

Write Methods

Method	Description
`db.upsert(id, vector, metadata, options)`	Insert or update a single record
`db.insert(id, vector, metadata, options)`	Insert a record (throws on duplicate id)
`db.upsertMany(records, { namespace })`	Upsert a batch of records
`db.insertMany(records, { namespace })`	Insert a batch
`db.bulkIngest(records, { namespace, batchSize })`	Fastest bulk import with batched WAL writes
`db.delete(id, { namespace })`	Delete a single record
`db.deleteMany(ids, { namespace })`	Delete multiple records by id
`db.deleteByFilter(filter, { namespace })`	Delete all records matching a filter
`db.updateMetadata(id, metadata, { namespace })`	Merge a metadata patch into an existing record (no vector rewrite)
`db.setTtl(id, seconds, { namespace })`	Set time-to-live on a record (seconds from now)
`db.clearTtl(id, { namespace })`	Remove TTL from a record

Read Methods

Method	Description
`db.get(id, { namespace })`	Get a single record by id
`db.search(query, options)` or `db.search({ query, ...options })`	Search and return a list of results
`db.searchWithStats(query, options)`	Search with detailed performance stats
`db.count({ namespace, filter })`	Count records, optionally scoped by namespace/filter
`db.list({ namespace, filter, limit, offset })`	List records without issuing a vector query
`db.listCursor({ namespace, filter, limit, cursor })`	Cursor-based pagination for large collections
`db.namespaces()`	List all namespaces
`db.dimension`	Vector dimension (property)
`db.path`	Database file path (property)
`db.metric`	Distance metric name: `"cosine"`, `"euclidean"`, `"dotproduct"`, or `"manhattan"` (property)
`db.readOnly`	Whether the database is read-only (property)

Index Methods

Method	Description
`db.createIndex(field, indexType)`	Create a payload index (`'keyword'` or `'numeric'`) on a metadata field
`db.dropIndex(field)`	Remove an index
`db.listIndexes()`	List all active indexes as `[{ field, type }, ...]`

Quantization Methods

Method	Description
`db.enableQuantization(method, options)`	Enable quantization (`'scalar'`, `'binary'`, or `'pq'` / `'product'`)
`db.disableQuantization()`	Disable quantization and remove persisted parameters
`db.isQuantized`	Whether quantization is enabled (property)
`db.quantizationMethod`	Active method name or `null` (property)
`db.validNumSubVectors()`	Valid PQ `numSubVectors` values for this database dimension
`db.enableMultiVectorQuantization(space, options)`	Enable 2-bit quantization for a multi-vector space
`db.disableMultiVectorQuantization(space)`	Disable multi-vector quantization for a space
`db.isMultiVectorQuantized(space)`	Whether multi-vector quantization is enabled for a space

Maintenance Methods

Method	Description
`db.compact()`	Fold WAL into snapshot and persist ANN indexes
`db.flush()`	Alias for `compact()`
`db.snapshot(dest)`	Create a self-contained `.vdb` copy
`db.backup(destDir)`	Full backup including ANN sidecar files
`db.transaction()`	Begin an atomic transaction
`db.close()`	Flush pending state, release the file lock, and invalidate the handle

Async Methods

Method	Description
`db.searchAsync(query, options)`	Non-blocking search (returns Promise)
`db.searchWithStatsAsync(query, options)`	Non-blocking search with stats (returns Promise)
`db.flushAsync()`	Non-blocking flush/compact (returns Promise)
`db.compactAsync()`	Non-blocking compact (returns Promise)
`db.bulkIngestAsync(records, options)`	Non-blocking bulk import (returns Promise)

Filter Operators

Operator	Example	Description
`$eq`	`{ field: { $eq: 'value' } }`	Equal (also `{ field: 'value' }`)
`$ne`	`{ field: { $ne: 'value' } }`	Not equal
`$gt` / `$gte`	`{ field: { $gt: 5 } }`	Greater than (or equal)
`$lt` / `$lte`	`{ field: { $lt: 20 } }`	Less than (or equal)
`$in` / `$nin`	`{ field: { $in: ['a', 'b'] } }`	In / not in set
`$contains`	`{ field: { $contains: 'auth' } }`	Substring match
`$exists`	`{ field: { $exists: true } }`	Field presence
`$and` / `$or`	`{ $and: [{...}, {...}] }`	Logical combinators
`$not`	`{ $not: {...} }`	Logical negation
`$elemMatch`	`{ tags: { $elemMatch: { $eq: 'rust' } } }`	Match array elements
`$size`	`{ tags: { $size: 3 } }`	Array length
dot-path	`{ 'author.name': 'Alice' }`	Nested field access

How It Works

Records are stored in a compact binary .vdb snapshot file
Writes go through a crash-safe WAL (.wal) before being applied in memory
compact() folds the WAL into the snapshot and persists HNSW sidecar files
Dense search uses HNSW indexes (auto-built for collections above ~128 records)
Sparse search uses an inverted index with BM25 scoring
Hybrid fusion combines dense + sparse via linear combination or reciprocal rank fusion
Advisory file locks (flock) prevent concurrent write corruption

License

MIT

vectlite

Package Exports

Readme

vectlite

Installation

Quick Start

Features

Core

Search & Retrieval

Data Management

Usage

Distance Metrics

Hybrid Search

Collections

Transactions

Text Helpers

Snapshots & Backup

Read-Only Mode

Listing, Counting, and Lifecycle

Search Diagnostics

Payload Indexes

Vector Quantization

Multi-Vector / ColBERT Search

TTL / Expiry

Cursor-Based Pagination

Async API

OpenTelemetry Integration

Database Methods Reference

Write Methods

Read Methods

Index Methods

Quantization Methods

Maintenance Methods

Async Methods

Filter Operators

How It Works

Links

License