| name | lancedb |
| description | LanceDB — serverless vector database for AI. Columnar storage on Lance format, zero-copy access, multimodal search (text + images + audio), and direct DataFrame integration. No separate server. |
| tags | ["lancedb","vector-database","embedded","multimodal","embeddings","python","zorai"] |
Overview
LanceDB is a developer-friendly, serverless vector database built on the Lance columnar format. It supports multimodal search (text, image, audio embeddings), hybrid search, and efficient streaming ingestion without a separate server process.
Installation
uv pip install lancedb
Create and Query
import lancedb
import numpy as np
db = lancedb.connect("./my_lancedb")
table = db.create_table("vectors", [
{"vector": np.random.rand(128), "text": "hello world"},
{"vector": np.random.rand(128), "text": "goodbye moon"},
])
results = table.search(np.random.rand(128)).limit(5).to_list()
print([r["text"] for r in results])
Open-Clip Embeddings
import lancedb
from lancedb.embeddings import with_open_clip
@with_open_clip
class Images:
image: str
vector: list
table = db.create_table("images", schema=Images)
table.add([{"image": "photo.jpg"}, {"image": "diagram.png"}])
results = table.search("sunset landscape").limit(3).to_pandas()
References
