LanceDB: Serverless Vector Database for Multimodal AI Applications

Vector databases have become a fundamental component in modern AI applications, from semantic search to Retrieval-Augmented Generation (RAG). However, many vector database solutions require complex server infrastructure that is expensive to maintain. LanceDB offers a lightweight, serverless alternative with multimodal search support.

LanceDB is an open-source vector database that runs embedded, meaning it requires no separate server. Built on the Lance data format optimized for vector operations, LanceDB delivers high performance with a minimal footprint. What makes it special is its native support for multimodal data, including text, images, audio, and video.

In this tutorial, we will learn how to use LanceDB from installation, basic operations, to building a complete multimodal search engine.

Prerequisites

Before starting, make sure you have:

• Python 3.9 or later
• pip package manager
• Basic understanding of Python and vector embedding concepts
• (Optional) OpenAI API key for embedding functions

Installation

Basic Installation

pip install lancedb

Installation with Embedding Functions

pip install lancedb sentence-transformers

Installation for Multimodal Search

pip install lancedb open-clip-torch Pillow

Verify Installation

import lancedb
print(f"LanceDB version: {lancedb.version}")

Creating Databases and Tables

LanceDB uses an embedded approach, so a database is simply created as a local directory.

Creating a Database

import lancedb

Create database connection (local directory)
db = lancedb.connect("./mylancedb")


print("Database created successfully!")
print(f"Location: ./mylancedb")

Creating a Table with Data

import lancedb
import numpy as np

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


Create data with embeddings
data = [
    {
        "id": 1,
        "text": "Python is a popular programming language for AI",
        "vector": np.random.randn(128).tolist(),
        "category": "programming",
    },
    {
        "id": 2,
        "text": "Machine learning uses data to make predictions",
        "vector": np.random.randn(128).tolist(),
        "category": "ai",
    },
    {
        "id": 3,
        "text": "Deep learning is a subset of machine learning",
        "vector": np.random.randn(128).tolist(),
        "category": "ai",
    },
]

Create table
table = db.createtable("articles", data=data)

print(f"Table 'articles' created with {len(table)} rows")

Using Pydantic Models

import lancedb
from lancedb.pydantic import LanceModel, Vector
import numpy as np

Define schema using Pydantic
class Article(LanceModel):
    id: int
    title: str
    content: str
    vector: Vector(384)  # Embedding dimension
    category: str
    published: bool = True

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


Create table with schema
table = db.createtable("articlesv2", schema=Article)


Add data
articles = [
    Article(
        id=1,
        title="Introduction to LanceDB",
        content="LanceDB is a serverless vector database",
        vector=np.random.randn(384).tolist(),
        category="database",
    ),
    Article(
        id=2,
        title="RAG Tutorial",
        content="RAG combines retrieval with generation",
        vector=np.random.randn(384).tolist(),
        category="ai",
    ),
]

table.add([a.dict() for a in articles])
print(f"Added {len(articles)} articles")

Adding Data

Adding Data to an Existing Table

import lancedb
import numpy as np

db = lancedb.connect("./mylancedb")
table = db.opentable("articles")


Add new data
newdata = [
    {
        "id": 4,