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LanceDB

Developer-friendly embedded vector database for multimodal AI. Search More; Manage Less.

★ 11,008 GitHub Apache-2.0 vector-databaseembeddingsmultimodalretrievalragdatabaselancedb RAG & Knowledge

Overview

LanceDB is an embedded vector database built for multimodal AI applications. It stores, indexes, and retrieves embeddings from text, images, audio, and video in a single unified database. Designed for developers, it offers a simple API, zero-copy Arrow integration, and runs embedded inside your application — no separate server needed. With native support for Lance columnar format, it handles petabyte-scale datasets while keeping queries fast with disk-based IVF-PQ indexing.

Requirements

Min vCPU
2
Min RAM
4096 MB
Min Disk
10 GB
Rec vCPU
4
Rec RAM
8192 MB
Rec Disk
20 GB

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Docker Compose

# Generated by Run This Ai — docker-compose.yml
services:
  lancedb:
    image: openeuler/lancedb:latest
    restart: unless-stopped
    ports:
      - 8080:8080
    volumes:
      - ./data/lancedb:/data

Best VPS for LanceDB →

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Guides & articles

Getting Started with LanceDB: A Practical Tutorial for RAG Applications

A step-by-step tutorial on using LanceDB for RAG applications. Install, create embeddings, run semantic search, and deploy with Docker in minutes.

Jul 27, 2026

LanceDB: The Embedded Vector Database Powering Multimodal AI

Discover LanceDB, the developer-friendly embedded vector database for multimodal AI. Store, index, and search embeddings from text, images, and audio — no separate server required.

Jul 27, 2026