LanceDB

LanceDB

LanceDB is an open-source, serverless vector database built on Lance — a columnar data format optimized for ML workloads. It runs embedded in your application (like SQLite) or as a hosted service, requiring no separate server process. The Lance format enables zero-copy access, fast random reads, and efficient updates — making it ideal for RAG, semantic search, and multimodal AI applications.

Key Features

• Serverless/embedded: no separate server; runs in-process or on S3/GCS/Azure
• Multimodal: store and search vectors alongside text, images, video, audio in one table
• Lance columnar format: 100× faster random access than Parquet for ML patterns
• Full-text search (Tantivy-based) + hybrid search
• Versioning: automatic dataset versioning with time-travel queries
• Python, JavaScript/TypeScript, and Rust APIs

Quick Start

import lancedb
import numpy as np

db = lancedb.connect("./lancedb")

table = db.create_table("embeddings", data=[
    {"vector": np.random.rand(384).tolist(), "text": "hello world"},
])

results = table.search(np.random.rand(384).tolist()).limit(5).to_pandas()

Install via ai-supply

npx ai-supply add lancedb-multimodal-vector-store

Curated mirror of the open-source LanceDB (Apache-2.0). Get it from the source.