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LanceDB Tutorial
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LanceDB Tutorial: Serverless Vector Database for AI

Master LanceDB, the open-source serverless vector database designed for AI applications, RAG systems, and semantic search.

Stars License: Apache 2.0 Rust

Build AI-Native Applications with Vector Search

What is LanceDB?

LanceDB is an open-source, serverless vector database built on the Lance data format. It's designed from the ground up for AI applications, offering fast vector similarity search, filtering, and full-text search without the operational overhead of traditional databases.

Key Features

Feature Description
Serverless No server management, embedded or cloud deployment
Lance Format Columnar format optimized for ML workloads
Multi-Modal Store vectors, text, images, and metadata together
Hybrid Search Combine vector search with SQL filters
Full-Text Search BM25-based full-text search built-in
Python/JS Native First-class Python and JavaScript SDKs
Zero-Copy Memory-mapped access for efficiency 
flowchart TD
    A[Your Application] --> B[LanceDB]

    B --> C[Vector Index]
    B --> D[Full-Text Index]
    B --> E[Scalar Index]

    C --> F[ANN Search]
    D --> G[BM25 Search]
    E --> H[Filtered Queries]

    F --> I[Results]
    G --> I
    H --> I

    B --> J[(Lance Files)]
    J --> K[Local Storage]
    J --> L[S3/GCS/Azure]

    classDef app fill:#e1f5fe,stroke:#01579b
    classDef db fill:#d4a574,stroke:#8b5a2b
    classDef index fill:#f3e5f5,stroke:#4a148c
    classDef search fill:#fff3e0,stroke:#ef6c00
    classDef storage fill:#e8f5e8,stroke:#1b5e20

    class A app
    class B db
    class C,D,E index
    class F,G,H search
    class J,K,L storage
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Current Snapshot (auto-updated)

Tutorial Chapters

  1. Chapter 1: Getting Started - Installation, setup, and first database
  2. Chapter 2: Data Modeling - Schemas, tables, and data types
  3. Chapter 3: Vector Operations - Embeddings, indexing, and similarity search
  4. Chapter 4: Hybrid Search - Combining vector, full-text, and filtered search
  5. Chapter 5: Integrations - LangChain, LlamaIndex, and embedding providers
  6. Chapter 6: Performance - Indexing strategies and query optimization
  7. Chapter 7: Production Deployment - Cloud storage, scaling, and monitoring
  8. Chapter 8: Advanced Patterns - Multi-tenancy, versioning, and RAG systems

What You'll Learn

  • Store Vectors efficiently with the Lance format
  • Search Semantically using approximate nearest neighbors
  • Filter Results with SQL-like predicates
  • Build RAG Systems with vector + full-text search
  • Integrate with LangChain, LlamaIndex, and more
  • Optimize Performance with proper indexing
  • Deploy to Production with cloud storage backends
  • Scale Applications for real-world workloads

Prerequisites

  • Python 3.8+ or Node.js 18+
  • Basic understanding of vectors/embeddings
  • Familiarity with SQL concepts
  • An embedding model (OpenAI, Sentence Transformers, etc.)

Quick Start

# Install LanceDB
pip install lancedb

# Basic usage
import lancedb

# Connect to database
db = lancedb.connect("./my_database")

# Create a table with data
data = [
    {"text": "Hello world", "vector": [0.1, 0.2, 0.3, 0.4]},
    {"text": "Goodbye world", "vector": [0.5, 0.6, 0.7, 0.8]},
]
table = db.create_table("my_table", data)

# Search by vector
results = table.search([0.1, 0.2, 0.3, 0.4]).limit(10).to_pandas()
print(results)
// JavaScript/TypeScript
import * as lancedb from '@lancedb/lancedb';

const db = await lancedb.connect('./my_database');

const data = [
    { text: "Hello world", vector: [0.1, 0.2, 0.3, 0.4] },
    { text: "Goodbye world", vector: [0.5, 0.6, 0.7, 0.8] },
];

const table = await db.createTable('my_table', data);
const results = await table.search([0.1, 0.2, 0.3, 0.4]).limit(10).toArray();

Use Cases

Semantic Search

# Find similar documents
results = table.search(query_embedding).limit(10).to_list()

RAG (Retrieval-Augmented Generation)

# Retrieve context for LLM
context = table.search(question_embedding).limit(5).to_list()
response = llm.generate(question, context=context)

Recommendation Systems

# Find similar items
similar_items = items_table.search(item_embedding).limit(20).to_list()

Image Search

# Find similar images
similar_images = images_table.search(image_embedding).limit(10).to_list()

Why LanceDB?

vs. Pinecone/Weaviate/Milvus

  • Serverless: No infrastructure to manage
  • Embedded: Run locally or in your application
  • Open Source: Full control, no vendor lock-in
  • Cost Effective: No per-vector pricing

vs. pgvector/SQLite-VSS

  • Purpose Built: Optimized for vector workloads
  • Faster: Better performance on large datasets
  • Multi-Modal: Native support for mixed data types
  • Cloud Ready: Built-in S3/GCS support

Learning Path

Beginner Track

  1. Chapters 1-2: Setup and data modeling
  2. Store and query your first vectors

Intermediate Track

  1. Chapters 3-5: Vector operations and integrations
  2. Build a complete RAG pipeline

Advanced Track

  1. Chapters 6-8: Performance, production, and advanced patterns
  2. Deploy scalable AI applications

Ready to build with LanceDB? Let's begin with Chapter 1: Getting Started!

Generated for Awesome Code Docs

Navigation & Backlinks

Full Chapter Map

  1. Chapter 1: Getting Started with LanceDB
  2. Chapter 2: Data Modeling
  3. Chapter 3: Vector Operations
  4. Chapter 4: Hybrid Search
  5. Chapter 5: Integrations
  6. Chapter 6: Performance
  7. Chapter 7: Production Deployment
  8. Chapter 8: Advanced Patterns

Source References

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