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vectordb

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Classes

Interfaces

Type Aliases

Functions

Type Aliases

VectorIndexParams

Ƭ VectorIndexParams: IvfPQIndexConfig

Defined in

index.ts:1290

Functions

connect

▸ connect(uri): Promise\<Connection>

Connect to a LanceDB instance at the given URI.

Accepted 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)

Parameters

Name Type Description
uri string The uri of the database. If the database uri starts with db:// then it connects to a remote database.

Returns

Promise\<Connection>

See

ConnectionOptions for more details on the URI format.

Defined in

index.ts:176

▸ connect(opts): Promise\<Connection>

Connect to a LanceDB instance with connection options.

Parameters

Name Type Description
opts Partial\<ConnectionOptions> The ConnectionOptions to use when connecting to the database.

Returns

Promise\<Connection>

Defined in

index.ts:182

convertToTable

▸ convertToTable\<T>(data, embeddings?, makeTableOptions?): Promise\<ArrowTable>

Type parameters

Name
T

Parameters

Name Type
data Record\<string, unknown>[]
embeddings? EmbeddingFunction\<T>
makeTableOptions? Partial\<MakeArrowTableOptions>

Returns

Promise\<ArrowTable>

Defined in

arrow.ts:420

isWriteOptions

▸ isWriteOptions(value): value is WriteOptions

Parameters

Name Type
value any

Returns

value is WriteOptions

Defined in

index.ts:1316

makeArrowTable

▸ makeArrowTable(data, options?): ArrowTable

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

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 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

Parameters

Name Type Description
data Record\<string, any>[] input data
options? Partial\<MakeArrowTableOptions> options to control the makeArrowTable call.

Returns

ArrowTable

Example

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(
     "vector",
     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

arrow.ts:196