LanceDB — Multimodal Vector Database for AI

What it is

LanceDB is an open-source multimodal vector database designed for AI and ML applications. It stores and indexes vectors, text, images, and structured data together, supporting fast similarity search across billions of vectors. LanceDB provides full-text search, SQL-style queries, and hybrid search that combines vector similarity with metadata filtering. Clients are available for Python, Node.js, and Rust.

LanceDB is built for developers creating RAG pipelines, recommendation systems, image search, and any application that needs to query across multiple data modalities. Its embedded mode runs in-process without a separate server, making it simple to integrate.

How it saves time or tokens

LanceDB runs in embedded mode by default -- no separate database server to configure, deploy, or maintain. You import the library and start storing vectors. The Lance columnar format provides fast reads and efficient storage, reducing infrastructure costs. Hybrid search (vector plus full-text plus metadata filtering) means a single query replaces multiple roundtrips. For RAG workflows, this reduces both latency and token consumption by returning more precise context.

How to use

1. Install the client: pip install lancedb (Python) or npm install lancedb (Node.js).
2. Create a database and table: db = lancedb.connect('my_db') then create a table with your data.
3. Query with vector search, full-text search, or hybrid queries.

Example

import lancedb
import numpy as np

# Connect (creates local database)
db = lancedb.connect('./my_lancedb')

# Create table with vectors and metadata
data = [
    {'text': 'AI agents automate tasks', 'vector': np.random.randn(128), 'category': 'ai'},
    {'text': 'Vector databases enable search', 'vector': np.random.randn(128), 'category': 'db'},
    {'text': 'RAG grounds LLM answers', 'vector': np.random.randn(128), 'category': 'ai'},
]
table = db.create_table('docs', data)

# Vector search
results = table.search(np.random.randn(128)).limit(2).to_pandas()

# Full-text search
results = table.search('vector database', query_type='fts').to_pandas()

# Filtered search
results = table.search(np.random.randn(128)).where("category = 'ai'").to_pandas()

Related on TokRepo

• RAG tools -- retrieval-augmented generation tools and frameworks
• Database AI tools -- AI-powered database management

Common pitfalls

• Not choosing the right index type for your scale. For small datasets (under 1M vectors), brute-force search is fast enough. For larger datasets, create an IVF_PQ index for approximate nearest neighbor search.
• Storing high-dimensional vectors without dimensionality reduction. LanceDB handles high dimensions, but reducing from 1536 to 256 dimensions often maintains accuracy while significantly improving speed and storage.
• Forgetting to create a full-text search index before querying. FTS requires a separate index creation step on the text column.