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Type Aliases


Type Aliases


Ƭ Data: Record\<string, unknown>[] | ArrowTable

Data type accepted by NodeJS SDK

Defined in




connect(uri, opts?): Promise\<Connection>

Connect to a LanceDB instance at the given URI.

Accpeted formats:

  • /path/to/database - local database
  • s3://bucket/path/to/database or gs://bucket/path/to/database - database on cloud storage
  • db://host:port - remote database (LanceDB cloud)


Name Type Description
uri string The uri of the database. If the database uri starts with db:// then it connects to a remote database.
opts? Partial\<ConnectionOptions> -




ConnectionOptions for more details on the URI format.

Defined in



makeArrowTable(data, options?): ArrowTable

An enhanced version of the makeTable function from Apache Arrow that supports nested fields and embeddings columns.

(typically you do not need to call this function. It will be called automatically when creating a table or adding data to it)

This function converts an array of Record (row-major JS objects) to an Arrow Table (a columnar structure)

Note that it currently does not support nulls.

If a schema is provided then it will be used to determine the resulting array types. Fields will also be reordered to fit the order defined by the schema.

If a schema is not provided then the types will be inferred and the field order will be controlled by the order of properties in the first record. If a type is inferred it will always be nullable.

If the input is empty then a schema must be provided to create an empty table.

When a schema is not specified then data types will be inferred. The inference rules are as follows:

  • boolean => Bool
  • number => Float64
  • String => Utf8
  • Buffer => Binary
  • Record => Struct
  • Array => List


Name Type
data Record\<string, unknown>[]
options? Partial\<MakeArrowTableOptions>




import { fromTableToBuffer, makeArrowTable } from "../arrow";
import { Field, FixedSizeList, Float16, Float32, Int32, Schema } from "apache-arrow";

const schema = new Schema([
  new Field("a", new Int32()),
  new Field("b", new Float32()),
  new Field("c", new FixedSizeList(3, new Field("item", new Float16()))),
 const table = makeArrowTable([
   { a: 1, b: 2, c: [1, 2, 3] },
   { a: 4, b: 5, c: [4, 5, 6] },
   { a: 7, b: 8, c: [7, 8, 9] },
 ], { schema });

By default it assumes that the column named vector is a vector column and it will be converted into a fixed size list array of type float32. The vectorColumns option can be used to support other vector column names and data types.

const schema = new Schema([
   new Field("a", new Float64()),
   new Field("b", new Float64()),
   new Field(
     new FixedSizeList(3, new Field("item", new Float32()))
 const table = makeArrowTable([
   { a: 1, b: 2, vector: [1, 2, 3] },
   { a: 4, b: 5, vector: [4, 5, 6] },
   { a: 7, b: 8, vector: [7, 8, 9] },
 assert.deepEqual(table.schema, schema);

You can specify the vector column types and names using the options as well

const schema = new Schema([
   new Field('a', new Float64()),
   new Field('b', new Float64()),
   new Field('vec1', new FixedSizeList(3, new Field('item', new Float16()))),
   new Field('vec2', new FixedSizeList(3, new Field('item', new Float16())))
const table = makeArrowTable([
   { a: 1, b: 2, vec1: [1, 2, 3], vec2: [2, 4, 6] },
   { a: 4, b: 5, vec1: [4, 5, 6], vec2: [8, 10, 12] },
   { a: 7, b: 8, vec1: [7, 8, 9], vec2: [14, 16, 18] }
 ], {
   vectorColumns: {
     vec1: { type: new Float16() },
     vec2: { type: new Float16() }
assert.deepEqual(table.schema, schema)

Defined in