lancedb-typescript

name

lancedb-typescript

description

Embedded vector database using LanceDB with TypeScript/JavaScript SDK. Use when building vector search, semantic search, RAG pipelines, hybrid search, full-text search, or multimodal data storage with LanceDB. Trigger on mentions of LanceDB, Lance format, vector similarity search with embedded DB, or vectordb that runs in-process. Also trigger when user needs an embedded alternative to Pinecone/Weaviate/Qdrant.

LanceDB (TypeScript)

Multimodal lakehouse — embedded vector DB built on the Lance columnar format. Runs in-process (like SQLite), supports local disk, S3, GCS, Azure. Apache Arrow under the hood.

Quick start

import * as lancedb from "@lancedb/lancedb";

const db = await lancedb.connect("data/lancedb");

const table = await db.createTable("vectors", [
  { id: 1, text: "hello", vector: [0.1, 0.2, 0.3] },
  { id: 2, text: "world", vector: [0.4, 0.5, 0.6] },
]);

const results = await table.search([0.1, 0.2, 0.3]).limit(5).toArray();

Install: npm install @lancedb/lancedb (native module, uses napi-rs)

Reference routing

Read the relevant reference file before writing code:

Topic	File	When to read
Tables & ingestion	tables.md	Creating tables, adding data, schema, updates, deletes, merge-insert/upsert
Vector search	search.md	Vector similarity, distance metrics, ANN vs brute-force, pre/post filtering
Full-text & hybrid search	fts-hybrid.md	BM25 keyword search, hybrid vector+FTS, reranking
Indexing	indexing.md	IVF/HNSW index types, FTS indexes, scalar indexes, reindexing
Embeddings	embeddings.md	Embedding registry, auto-vectorization, supported providers
Versioning & consistency	versioning.md	Time travel, version restore, consistency intervals, optimize/compaction
Storage & config	storage.md	S3/GCS/Azure config, storage_options, DynamoDB commit store

Key concepts

• Lance format: open columnar format optimized for ML — random access, versioning, zero-copy
• No separate server: embedded library, connects via local path or object store URI
• Schema: Arrow-based. Vector columns are FixedSizeList<Float32>. Nested structs supported
• Versioning: every mutation creates a new version. checkout(version) for time travel
• Indexing: optional — brute-force kNN works well up to ~100k vectors. Create ANN indexes for larger datasets
• Enterprise: managed service with db:// URIs, auto-indexing, async background optimization

Connection patterns

// Local embedded
const db = await lancedb.connect("data/lancedb");

// S3
const db = await lancedb.connect("s3://bucket/path", {
  storageOptions: { region: "us-east-1" }
});

// Enterprise
const db = await lancedb.connect("db://your-project", {
  apiKey: "...", region: "us-east-1"
});

TypeScript SDK surface

Core operations map directly to methods on db and table objects:

• db.createTable(name, data, opts?) / db.openTable(name) / db.dropTable(name)
• table.add(data) / table.update(opts) / table.delete(filter) / table.mergeInsert(on)
• table.search(vector).limit(k).where(filter).toArray()
• table.createIndex(column, opts) / table.listIndices()
• table.schema / table.addColumns(transforms) / table.alterColumns(alts) / table.dropColumns(names)
• table.version / table.checkout(v) / table.checkoutLatest()