Getting Started with LanceDB: A Practical Tutorial for RAG Applications

Getting Started with LanceDB

In this tutorial, you'll learn how to set up LanceDB for a real-world RAG (Retrieval-Augmented Generation) application. LanceDB's embedded architecture means you can go from pip install to vector search in under 5 minutes — no Docker, no separate server, no cloud dependencies required.

Step 1: Installation

LanceDB is a Python package installable via pip. It has minimal dependencies — just NumPy and PyArrow:

pip install lancedb

That's it. No database server, no Docker container, no cloud service to provision. LanceDB creates and manages its database files locally on your filesystem.

Step 2: Create a Database and Insert Embeddings

import lancedb
import numpy as np

# Create or open a database
db = lancedb.connect("./my_vectors")

# Create a table with embeddings
data = [
    {"vector": np.random.randn(128), "text": "LanceDB is an embedded vector database"},
    {"vector": np.random.randn(128), "text": "It supports multimodal AI applications"},
    {"vector": np.random.randn(128), "text": "Built on the Lance columnar format"},
]
table = db.create_table("my_docs", data=data)
print(f"Created table with {len(table)} rows")

Step 3: Semantic Search

# Search for similar vectors
query_vector = np.random.randn(128)
results = table.search(query_vector).limit(3).to_pandas()
print(results)

With real embedding models (e.g., OpenAI's text-embedding-3-small, sentence-transformers, or CLIP for images), you can replace the random vectors with actual embeddings and perform meaningful semantic search across your documents.

Step 4: Production Deployment with Docker

For production deployments or when you need a network-accessible endpoint, LanceDB can run via Docker:

docker run -d -p 8080:8080 -v ./data:/data openeuler/lancedb:latest

This launches LanceDB with persistent storage mounted at ./data. Visit http://localhost:8080 to start interacting with your vector database over HTTP.

Performance Tips

• Index your data — For tables larger than 10,000 rows, create an IVF-PQ index using table.create_index() for sub-second query times
• Use the right embedding dimension — 384 or 768 dimensions (from sentence-transformers models) work well for most text applications; 512 is common for image embeddings
• Batch inserts — Insert vectors in batches of 1,000-10,000 for optimal write throughput
• Disk space matters — Lance's columnar format compresses well, but plan for ~2x the raw embedding size for index overhead