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Polars

Applies to: Embedded ✅, Enterprise ✅

LanceDB supports Polars, a blazingly fast DataFrame library for Python written in Rust. Under the hood, both Lance and Polars speak Arrow, so passing data back and forth stays zero-copy and ergonomic.

Create and Query a Table

Import the required libraries, including the optional Pydantic helpers if you plan to define schemas.

import tempfile
from pathlib import Path

import lancedb
import polars as pl
from lancedb.pydantic import LanceModel, Vector

Build a Polars DataFrame, convert it to Arrow, and use it directly when creating a LanceDB table.

import lancedb

birds = pl.DataFrame(
    {
        "text": ["phoenix", "sparrow"],
        "vector": [
            [0.1, 0.2, 0.3],
            [0.8, 0.6, 0.5],
        ],
    }
)
polars_db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "polars-demo"))
polars_table = polars_db.create_table(
    "birds", data=birds.to_arrow(), mode="overwrite"
)

Run vector search and keep the results as a Polars DataFrame for further processing or visualization.

polars_results = (
    polars_table.search([0.1, 0.2, 0.3])
    .select(["text", "_distance"])
    .limit(1)
    .to_polars()
)
print(polars_results)

Work with LazyFrames

When you want to operate on the entire table (potentially larger than RAM), convert to a Polars LazyFrame so you can chain transformations without loading everything at once. This works for LanceDB Embedded tables only; with LanceDB Enterprise, search(...).to_polars() returns an eager DataFrame of query results.

lazy_frame = polars_table.to_polars().lazy()
print(lazy_frame.select(["text"]).collect())

Define Schemas with Pydantic

You can also describe your table via LanceModel and continue ingesting data from Polars. This is useful when multiple teams share a schema or when you want validation.

class BirdModel(LanceModel):
    text: str
    vector: Vector(3)

schema_table = polars_db.create_table(
    "birds_schema", schema=BirdModel, mode="overwrite"
)
schema_table.add(birds.to_dicts())