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Quick start

LanceDB can be run in a number of ways:

  • Embedded within an existing backend (like your Django, Flask, Node.js or FastAPI application)
  • Directly from a client application like a Jupyter notebook for analytical workloads
  • Deployed as a remote serverless database


pip install lancedb
npm install vectordb
cargo add lancedb

To use the lancedb create, you first need to install protobuf.

brew install protobuf
sudo apt install -y protobuf-compiler libssl-dev

Please also make sure you're using the same version of Arrow as in the lancedb crate

Connect to a database

import lancedb
import pandas as pd
import pyarrow as pa

uri = "data/sample-lancedb"
db = lancedb.connect(uri)

# LanceDb offers both a synchronous and an asynchronous client.  There are still a
# few operations that are only supported by the synchronous client (e.g. embedding
# functions, full text search) but both APIs should soon be equivalent

# In this guide we will give examples of both clients.  In other guides we will
# typically only provide examples with one client or the other.
uri = "data/sample-lancedb"
async_db = await lancedb.connect_async(uri)

Asynchronous Python API

The asynchronous Python API is new and has some slight differences compared to the synchronous API. Feel free to start using the asynchronous version. Once all features have migrated we will start to move the synchronous API to use the same syntax as the asynchronous API. To help with this migration we have created a migration guide detailing the differences.

import * as lancedb from "vectordb";
import { Schema, Field, Float32, FixedSizeList, Int32, Float16 } from "apache-arrow";

const lancedb = require("vectordb");
const uri = "data/sample-lancedb";
const db = await lancedb.connect(uri);

@lancedb/lancedb vs. vectordb

The Javascript SDK was originally released as vectordb. In an effort to reduce maintenance we are aligning our SDKs. The new, aligned, Javascript API is being released as lancedb. If you are starting new work we encourage you to try out lancedb. Once the new API is feature complete we will begin slowly deprecating vectordb in favor of lancedb. There is a migration guide detailing the differences which will assist you in this process.

async fn main() -> Result<()> {
    let uri = "data/sample-lancedb";
    let db = connect(uri).execute().await?;

See examples/ for a full working example.

LanceDB will create the directory if it doesn't exist (including parent directories).

If you need a reminder of the uri, you can call db.uri().

Create a table

Create a table from initial data

If you have data to insert into the table at creation time, you can simultaneously create a table and insert the data into it. The schema of the data will be used as the schema of the table.

data = [
    {"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
    {"vector": [5.9, 26.5], "item": "bar", "price": 20.0},

# Synchronous client
tbl = db.create_table("my_table", data=data)
# Asynchronous client
async_tbl = await async_db.create_table("my_table2", data=data)

If the table already exists, LanceDB will raise an error by default. If you want to overwrite the table, you can pass in mode="overwrite" to the create_table method.

You can also pass in a pandas DataFrame directly:

df = pd.DataFrame(
        {"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
        {"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
# Synchronous client
tbl = db.create_table("table_from_df", data=df)
# Asynchronous client
async_tbl = await async_db.create_table("table_from_df2", df)
const tbl = await db.createTable(
    { vector: [3.1, 4.1], item: "foo", price: 10.0 },
    { vector: [5.9, 26.5], item: "bar", price: 20.0 },
  { writeMode: lancedb.WriteMode.Overwrite }

If the table already exists, LanceDB will raise an error by default. If you want to overwrite the table, you can pass in mode="overwrite" to the createTable function.

let initial_data = create_some_records()?;
let tbl = db
    .create_table("my_table", initial_data)

If the table already exists, LanceDB will raise an error by default. See the mode option for details on how to overwrite (or open) existing tables instead.


The Rust SDK currently expects data to be provided as an Arrow RecordBatchReader Support for additional formats (such as serde or polars) is on the roadmap.

Under the hood, LanceDB reads in the Apache Arrow data and persists it to disk using the Lance format.

Create an empty table

Sometimes you may not have the data to insert into the table at creation time. In this case, you can create an empty table and specify the schema, so that you can add data to the table at a later time (as long as it conforms to the schema). This is similar to a CREATE TABLE statement in SQL.

schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), list_size=2))])
# Synchronous client
tbl = db.create_table("empty_table", schema=schema)
# Asynchronous client
async_tbl = await async_db.create_table("empty_table2", schema=schema)
const schema = new Schema([
  new Field("id", new Int32()),
  new Field("name", new Utf8()),
const empty_tbl = await db.createTable({ name: "empty_table", schema });
let schema = Arc::new(Schema::new(vec![
    Field::new("id", DataType::Int32, false),
    Field::new("item", DataType::Utf8, true),
db.create_empty_table("empty_table", schema).execute().await

Open an existing table

Once created, you can open a table as follows:

# Synchronous client
tbl = db.open_table("my_table")
# Asynchronous client
async_tbl = await async_db.open_table("my_table2")
const tbl = await db.openTable("myTable");
let table = db.open_table("my_table").execute().await.unwrap();

If you forget the name of your table, you can always get a listing of all table names:

# Synchronous client
# Asynchronous client
print(await async_db.table_names())
console.log(await db.tableNames());
println!("{:?}", db.table_names().execute().await?);

Add data to a table

After a table has been created, you can always add more data to it as follows:

# Option 1: Add a list of dicts to a table
data = [
    {"vector": [1.3, 1.4], "item": "fizz", "price": 100.0},
    {"vector": [9.5, 56.2], "item": "buzz", "price": 200.0},

# Option 2: Add a pandas DataFrame to a table
df = pd.DataFrame(data)
# Asynchronous client
await async_tbl.add(data)
const newData = Array.from({ length: 500 }, (_, i) => ({
  vector: [i, i + 1],
  item: "fizz",
  price: i * 0.1,
await tbl.add(newData);
let new_data = create_some_records()?;

Search for nearest neighbors

Once you've embedded the query, you can find its nearest neighbors as follows:

# Synchronous client[100, 100]).limit(2).to_pandas()
# Asynchronous client
await async_tbl.vector_search([100, 100]).limit(2).to_pandas()

This returns a pandas DataFrame with the results.

const query = await[100, 100]).limit(2).execute();
use futures::TryStreamExt;

    .nearest_to(&[1.0; 128])?


Rust does not yet support automatic execution of embedding functions. You will need to calculate embeddings yourself. Support for this is on the roadmap and can be tracked at

Query vectors can be provided as Arrow arrays or a Vec/slice of Rust floats. Support for additional formats (e.g. polars::series::Series) is on the roadmap.

By default, LanceDB runs a brute-force scan over dataset to find the K nearest neighbours (KNN). For tables with more than 50K vectors, creating an ANN index is recommended to speed up search performance. LanceDB allows you to create an ANN index on a table as follows:

# Synchronous client
# Asynchronous client (must specify column to index)
await async_tbl.create_index("vector")
await tbl.createIndex({
  type: "ivf_pq",
  num_partitions: 2,
  num_sub_vectors: 2,
table.create_index(&["vector"], Index::Auto).execute().await

Why do I need to create an index manually?

LanceDB does not automatically create the ANN index for two reasons. The first is that it's optimized for really fast retrievals via a disk-based index, and the second is that data and query workloads can be very diverse, so there's no one-size-fits-all index configuration. LanceDB provides many parameters to fine-tune index size, query latency and accuracy. See the section on ANN indexes for more details.

Delete rows from a table

Use the delete() method on tables to delete rows from a table. To choose which rows to delete, provide a filter that matches on the metadata columns. This can delete any number of rows that match the filter.

# Synchronous client
tbl.delete('item = "fizz"')
# Asynchronous client
await async_tbl.delete('item = "fizz"')
await tbl.delete('item = "fizz"');
tbl.delete("id > 24").await.unwrap();

The deletion predicate is a SQL expression that supports the same expressions as the where() clause (only_if() in Rust) on a search. They can be as simple or complex as needed. To see what expressions are supported, see the SQL filters section.

Drop a table

Use the drop_table() method on the database to remove a table.

# Synchronous client
# Asynchronous client
await async_db.drop_table("my_table2")

This permanently removes the table and is not recoverable, unlike deleting rows. By default, if the table does not exist an exception is raised. To suppress this, you can pass in ignore_missing=True.

await db.dropTable("myTable");

This permanently removes the table and is not recoverable, unlike deleting rows. If the table does not exist an exception is raised.


Bundling vectordb apps with Webpack

If you're using the vectordb module in JavaScript, since LanceDB contains a prebuilt Node binary, you must configure next.config.js to exclude it from webpack. This is required for both using Next.js and deploying a LanceDB app on Vercel.

/** @type {import('next').NextConfig} */
module.exports = ({
webpack(config) {
    config.externals.push({ vectordb: 'vectordb' })
    return config;

What's next

This section covered the very basics of using LanceDB. If you're learning about vector databases for the first time, you may want to read the page on indexing to get familiar with the concepts.

If you've already worked with other vector databases, you may want to read the guides to learn how to work with LanceDB in more detail.