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Author SHA1 Message Date
albertlockett
e926819e57 WIP hastily moved types around 2023-12-14 12:57:31 -05:00
17 changed files with 400 additions and 1349 deletions

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@@ -21,7 +21,15 @@ import type { EmbeddingFunction } from './embedding/embedding_function'
import { RemoteConnection } from './remote'
import { Query } from './query'
import { isEmbeddingFunction } from './embedding/embedding_function'
import { type Literal, toSQL } from './util'
import {
type Connection, type CreateTableOptions, type Table,
type VectorIndexParams, type UpdateArgs, type UpdateSqlArgs,
type VectorIndex, type IndexStats,
type ConnectionOptions, WriteMode, type WriteOptions
} from './types'
import { toSQL } from './util'
export { type WriteMode }
// eslint-disable-next-line @typescript-eslint/no-var-requires
const { databaseNew, databaseTableNames, databaseOpenTable, databaseDropTable, tableCreate, tableAdd, tableCreateVectorIndex, tableCountRows, tableDelete, tableUpdate, tableCleanupOldVersions, tableCompactFiles, tableListIndices, tableIndexStats } = require('../native.js')
@@ -30,30 +38,6 @@ export { Query }
export type { EmbeddingFunction }
export { OpenAIEmbeddingFunction } from './embedding/openai'
export interface AwsCredentials {
accessKeyId: string
secretKey: string
sessionToken?: string
}
export interface ConnectionOptions {
uri: string
awsCredentials?: AwsCredentials
awsRegion?: string
// API key for the remote connections
apiKey?: string
// Region to connect
region?: string
// override the host for the remote connections
hostOverride?: string
}
function getAwsArgs (opts: ConnectionOptions): any[] {
const callArgs = []
const awsCredentials = opts.awsCredentials
@@ -71,23 +55,6 @@ function getAwsArgs (opts: ConnectionOptions): any[] {
return callArgs
}
export interface CreateTableOptions<T> {
// Name of Table
name: string
// Data to insert into the Table
data?: Array<Record<string, unknown>> | ArrowTable | undefined
// Optional Arrow Schema for this table
schema?: Schema | undefined
// Optional embedding function used to create embeddings
embeddingFunction?: EmbeddingFunction<T> | undefined
// WriteOptions for this operation
writeOptions?: WriteOptions | undefined
}
/**
* Connect to a LanceDB instance at the given URI
* @param uri The uri of the database.
@@ -116,235 +83,6 @@ export async function connect (arg: string | Partial<ConnectionOptions>): Promis
return new LocalConnection(db, opts)
}
/**
* A LanceDB Connection that allows you to open tables and create new ones.
*
* Connection could be local against filesystem or remote against a server.
*/
export interface Connection {
uri: string
tableNames(): Promise<string[]>
/**
* Open a table in the database.
*
* @param name The name of the table.
* @param embeddings An embedding function to use on this table
*/
openTable<T>(name: string, embeddings?: EmbeddingFunction<T>): Promise<Table<T>>
/**
* Creates a new Table, optionally initializing it with new data.
*
* @param {string} name - The name of the table.
* @param data - Array of Records to be inserted into the table
* @param schema - An Arrow Schema that describe this table columns
* @param {EmbeddingFunction} embeddings - An embedding function to use on this table
* @param {WriteOptions} writeOptions - The write options to use when creating the table.
*/
createTable<T> ({ name, data, schema, embeddingFunction, writeOptions }: CreateTableOptions<T>): Promise<Table<T>>
/**
* Creates a new Table and initialize it with new data.
*
* @param {string} name - The name of the table.
* @param data - Non-empty Array of Records to be inserted into the table
*/
createTable (name: string, data: Array<Record<string, unknown>>): Promise<Table>
/**
* Creates a new Table and initialize it with new data.
*
* @param {string} name - The name of the table.
* @param data - Non-empty Array of Records to be inserted into the table
* @param {WriteOptions} options - The write options to use when creating the table.
*/
createTable (name: string, data: Array<Record<string, unknown>>, options: WriteOptions): Promise<Table>
/**
* Creates a new Table and initialize it with new data.
*
* @param {string} name - The name of the table.
* @param data - Non-empty Array of Records to be inserted into the table
* @param {EmbeddingFunction} embeddings - An embedding function to use on this table
*/
createTable<T> (name: string, data: Array<Record<string, unknown>>, embeddings: EmbeddingFunction<T>): Promise<Table<T>>
/**
* Creates a new Table and initialize it with new data.
*
* @param {string} name - The name of the table.
* @param data - Non-empty Array of Records to be inserted into the table
* @param {EmbeddingFunction} embeddings - An embedding function to use on this table
* @param {WriteOptions} options - The write options to use when creating the table.
*/
createTable<T> (name: string, data: Array<Record<string, unknown>>, embeddings: EmbeddingFunction<T>, options: WriteOptions): Promise<Table<T>>
/**
* Drop an existing table.
* @param name The name of the table to drop.
*/
dropTable(name: string): Promise<void>
}
/**
* A LanceDB Table is the collection of Records. Each Record has one or more vector fields.
*/
export interface Table<T = number[]> {
name: string
/**
* Creates a search query to find the nearest neighbors of the given search term
* @param query The query search term
*/
search: (query: T) => Query<T>
/**
* Insert records into this Table.
*
* @param data Records to be inserted into the Table
* @return The number of rows added to the table
*/
add: (data: Array<Record<string, unknown>>) => Promise<number>
/**
* Insert records into this Table, replacing its contents.
*
* @param data Records to be inserted into the Table
* @return The number of rows added to the table
*/
overwrite: (data: Array<Record<string, unknown>>) => Promise<number>
/**
* Create an ANN index on this Table vector index.
*
* @param indexParams The parameters of this Index, @see VectorIndexParams.
*/
createIndex: (indexParams: VectorIndexParams) => Promise<any>
/**
* Returns the number of rows in this table.
*/
countRows: () => Promise<number>
/**
* Delete rows from this table.
*
* This can be used to delete a single row, many rows, all rows, or
* sometimes no rows (if your predicate matches nothing).
*
* @param filter A filter in the same format used by a sql WHERE clause. The
* filter must not be empty.
*
* @examples
*
* ```ts
* const con = await lancedb.connect("./.lancedb")
* const data = [
* {id: 1, vector: [1, 2]},
* {id: 2, vector: [3, 4]},
* {id: 3, vector: [5, 6]},
* ];
* const tbl = await con.createTable("my_table", data)
* await tbl.delete("id = 2")
* await tbl.countRows() // Returns 2
* ```
*
* If you have a list of values to delete, you can combine them into a
* stringified list and use the `IN` operator:
*
* ```ts
* const to_remove = [1, 5];
* await tbl.delete(`id IN (${to_remove.join(",")})`)
* await tbl.countRows() // Returns 1
* ```
*/
delete: (filter: string) => Promise<void>
/**
* Update rows in this table.
*
* This can be used to update a single row, many rows, all rows, or
* sometimes no rows (if your predicate matches nothing).
*
* @param args see {@link UpdateArgs} and {@link UpdateSqlArgs} for more details
*
* @examples
*
* ```ts
* const con = await lancedb.connect("./.lancedb")
* const data = [
* {id: 1, vector: [3, 3], name: 'Ye'},
* {id: 2, vector: [4, 4], name: 'Mike'},
* ];
* const tbl = await con.createTable("my_table", data)
*
* await tbl.update({
* filter: "id = 2",
* updates: { vector: [2, 2], name: "Michael" },
* })
*
* let results = await tbl.search([1, 1]).execute();
* // Returns [
* // {id: 2, vector: [2, 2], name: 'Michael'}
* // {id: 1, vector: [3, 3], name: 'Ye'}
* // ]
* ```
*
*/
update: (args: UpdateArgs | UpdateSqlArgs) => Promise<void>
/**
* List the indicies on this table.
*/
listIndices: () => Promise<VectorIndex[]>
/**
* Get statistics about an index.
*/
indexStats: (indexUuid: string) => Promise<IndexStats>
}
export interface UpdateArgs {
/**
* A filter in the same format used by a sql WHERE clause. The filter may be empty,
* in which case all rows will be updated.
*/
where?: string
/**
* A key-value map of updates. The keys are the column names, and the values are the
* new values to set
*/
values: Record<string, Literal>
}
export interface UpdateSqlArgs {
/**
* A filter in the same format used by a sql WHERE clause. The filter may be empty,
* in which case all rows will be updated.
*/
where?: string
/**
* A key-value map of updates. The keys are the column names, and the values are the
* new values to set as SQL expressions.
*/
valuesSql: Record<string, string>
}
export interface VectorIndex {
columns: string[]
name: string
uuid: string
}
export interface IndexStats {
numIndexedRows: number | null
numUnindexedRows: number | null
}
/**
* A connection to a LanceDB database.
*/
@@ -692,83 +430,6 @@ export interface CompactionMetrics {
filesAdded: number
}
/// Config to build IVF_PQ index.
///
export interface IvfPQIndexConfig {
/**
* The column to be indexed
*/
column?: string
/**
* A unique name for the index
*/
index_name?: string
/**
* Metric type, L2 or Cosine
*/
metric_type?: MetricType
/**
* The number of partitions this index
*/
num_partitions?: number
/**
* The max number of iterations for kmeans training.
*/
max_iters?: number
/**
* Train as optimized product quantization.
*/
use_opq?: boolean
/**
* Number of subvectors to build PQ code
*/
num_sub_vectors?: number
/**
* The number of bits to present one PQ centroid.
*/
num_bits?: number
/**
* Max number of iterations to train OPQ, if `use_opq` is true.
*/
max_opq_iters?: number
/**
* Replace an existing index with the same name if it exists.
*/
replace?: boolean
type: 'ivf_pq'
}
export type VectorIndexParams = IvfPQIndexConfig
/**
* Write mode for writing a table.
*/
export enum WriteMode {
/** Create a new {@link Table}. */
Create = 'create',
/** Overwrite the existing {@link Table} if presented. */
Overwrite = 'overwrite',
/** Append new data to the table. */
Append = 'append'
}
/**
* Write options when creating a Table.
*/
export interface WriteOptions {
/** A {@link WriteMode} to use on this operation */
writeMode?: WriteMode
}
export class DefaultWriteOptions implements WriteOptions {
writeMode = WriteMode.Create
}
@@ -777,23 +438,3 @@ export function isWriteOptions (value: any): value is WriteOptions {
return Object.keys(value).length === 1 &&
(value.writeMode === undefined || typeof value.writeMode === 'string')
}
/**
* Distance metrics type.
*/
export enum MetricType {
/**
* Euclidean distance
*/
L2 = 'l2',
/**
* Cosine distance
*/
Cosine = 'cosine',
/**
* Dot product
*/
Dot = 'dot'
}

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@@ -1,180 +0,0 @@
// Copyright 2023 LanceDB Developers.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import { describe } from 'mocha'
import * as chai from 'chai'
import * as chaiAsPromised from 'chai-as-promised'
import { v4 as uuidv4 } from 'uuid'
import * as lancedb from '../index'
import { tmpdir } from 'os'
import * as fs from 'fs'
import * as path from 'path'
const assert = chai.assert
chai.use(chaiAsPromised)
describe('LanceDB AWS Integration test', function () {
it('s3+ddb schema is processed correctly', async function () {
this.timeout(15000)
// WARNING: specifying engine is NOT a publicly supported feature in lancedb yet
// THE API WILL CHANGE
const conn = await lancedb.connect('s3://lancedb-integtest?engine=ddb&ddbTableName=lancedb-integtest')
const data = [{ vector: Array(128).fill(1.0) }]
const tableName = uuidv4()
let table = await conn.createTable(tableName, data, { writeMode: lancedb.WriteMode.Overwrite })
const futs = [table.add(data), table.add(data), table.add(data), table.add(data), table.add(data)]
await Promise.allSettled(futs)
table = await conn.openTable(tableName)
assert.equal(await table.countRows(), 6)
})
})
describe('LanceDB Mirrored Store Integration test', function () {
it('s3://...?mirroredStore=... param is processed correctly', async function () {
this.timeout(600000)
const dir = tmpdir()
console.log(dir)
const conn = await lancedb.connect(`s3://lancedb-integtest?mirroredStore=${dir}`)
const data = Array(200).fill({ vector: Array(128).fill(1.0), id: 0 })
data.push(...Array(200).fill({ vector: Array(128).fill(1.0), id: 1 }))
data.push(...Array(200).fill({ vector: Array(128).fill(1.0), id: 2 }))
data.push(...Array(200).fill({ vector: Array(128).fill(1.0), id: 3 }))
const tableName = uuidv4()
// try create table and check if it's mirrored
const t = await conn.createTable(tableName, data, { writeMode: lancedb.WriteMode.Overwrite })
const mirroredPath = path.join(dir, `${tableName}.lance`)
fs.readdir(mirroredPath, { withFileTypes: true }, (err, files) => {
if (err != null) throw err
// there should be three dirs
assert.equal(files.length, 3)
assert.isTrue(files[0].isDirectory())
assert.isTrue(files[1].isDirectory())
fs.readdir(path.join(mirroredPath, '_transactions'), { withFileTypes: true }, (err, files) => {
if (err != null) throw err
assert.equal(files.length, 1)
assert.isTrue(files[0].name.endsWith('.txn'))
})
fs.readdir(path.join(mirroredPath, '_versions'), { withFileTypes: true }, (err, files) => {
if (err != null) throw err
assert.equal(files.length, 1)
assert.isTrue(files[0].name.endsWith('.manifest'))
})
fs.readdir(path.join(mirroredPath, 'data'), { withFileTypes: true }, (err, files) => {
if (err != null) throw err
assert.equal(files.length, 1)
assert.isTrue(files[0].name.endsWith('.lance'))
})
})
// try create index and check if it's mirrored
await t.createIndex({ column: 'vector', type: 'ivf_pq' })
fs.readdir(mirroredPath, { withFileTypes: true }, (err, files) => {
if (err != null) throw err
// there should be four dirs
assert.equal(files.length, 4)
assert.isTrue(files[0].isDirectory())
assert.isTrue(files[1].isDirectory())
assert.isTrue(files[2].isDirectory())
// Two TXs now
fs.readdir(path.join(mirroredPath, '_transactions'), { withFileTypes: true }, (err, files) => {
if (err != null) throw err
assert.equal(files.length, 2)
assert.isTrue(files[0].name.endsWith('.txn'))
assert.isTrue(files[1].name.endsWith('.txn'))
})
fs.readdir(path.join(mirroredPath, 'data'), { withFileTypes: true }, (err, files) => {
if (err != null) throw err
assert.equal(files.length, 1)
assert.isTrue(files[0].name.endsWith('.lance'))
})
fs.readdir(path.join(mirroredPath, '_indices'), { withFileTypes: true }, (err, files) => {
if (err != null) throw err
assert.equal(files.length, 1)
assert.isTrue(files[0].isDirectory())
fs.readdir(path.join(mirroredPath, '_indices', files[0].name), { withFileTypes: true }, (err, files) => {
if (err != null) throw err
assert.equal(files.length, 1)
assert.isTrue(files[0].isFile())
assert.isTrue(files[0].name.endsWith('.idx'))
})
})
})
// try delete and check if it's mirrored
await t.delete('id = 0')
fs.readdir(mirroredPath, { withFileTypes: true }, (err, files) => {
if (err != null) throw err
// there should be five dirs
assert.equal(files.length, 5)
assert.isTrue(files[0].isDirectory())
assert.isTrue(files[1].isDirectory())
assert.isTrue(files[2].isDirectory())
assert.isTrue(files[3].isDirectory())
assert.isTrue(files[4].isDirectory())
// Three TXs now
fs.readdir(path.join(mirroredPath, '_transactions'), { withFileTypes: true }, (err, files) => {
if (err != null) throw err
assert.equal(files.length, 3)
assert.isTrue(files[0].name.endsWith('.txn'))
assert.isTrue(files[1].name.endsWith('.txn'))
})
fs.readdir(path.join(mirroredPath, 'data'), { withFileTypes: true }, (err, files) => {
if (err != null) throw err
assert.equal(files.length, 1)
assert.isTrue(files[0].name.endsWith('.lance'))
})
fs.readdir(path.join(mirroredPath, '_indices'), { withFileTypes: true }, (err, files) => {
if (err != null) throw err
assert.equal(files.length, 1)
assert.isTrue(files[0].isDirectory())
fs.readdir(path.join(mirroredPath, '_indices', files[0].name), { withFileTypes: true }, (err, files) => {
if (err != null) throw err
assert.equal(files.length, 1)
assert.isTrue(files[0].isFile())
assert.isTrue(files[0].name.endsWith('.idx'))
})
})
fs.readdir(path.join(mirroredPath, '_deletions'), { withFileTypes: true }, (err, files) => {
if (err != null) throw err
assert.equal(files.length, 1)
assert.isTrue(files[0].name.endsWith('.arrow'))
})
})
})
})

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@@ -14,10 +14,16 @@
import { Vector, tableFromIPC } from 'apache-arrow'
import { type EmbeddingFunction } from './embedding/embedding_function'
import { type MetricType } from '.'
import { type MetricType } from './types'
// eslint-disable-next-line @typescript-eslint/no-var-requires
const { tableSearch } = require('../native.js')
// const { tableSearch } = require('../native.js')
const tableSearch = async function (args: any, arg2: any): Promise<any> {
return await new Promise((resolve, reject) => {
resolve('')
})
}
/**
* A builder for nearest neighbor queries for LanceDB.

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@@ -13,12 +13,15 @@
// limitations under the License.
import {
type EmbeddingFunction, type Table, type VectorIndexParams, type Connection,
type ConnectionOptions, type CreateTableOptions, type VectorIndex,
type WriteOptions,
type Table, type VectorIndexParams,
type VectorIndex,
type IndexStats,
type UpdateArgs, type UpdateSqlArgs
} from '../index'
type UpdateArgs, type UpdateSqlArgs,
type Connection,
type ConnectionOptions, type CreateTableOptions,
type WriteOptions
} from '../types'
import { type EmbeddingFunction } from '../embedding/embedding_function'
import { Query } from '../query'
import { Vector, Table as ArrowTable } from 'apache-arrow'

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@@ -1,57 +0,0 @@
// Copyright 2023 Lance Developers.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import { describe } from 'mocha'
import { assert } from 'chai'
import { OpenAIEmbeddingFunction } from '../../embedding/openai'
import { isEmbeddingFunction } from '../../embedding/embedding_function'
// eslint-disable-next-line @typescript-eslint/no-var-requires
const { OpenAIApi } = require('openai')
// eslint-disable-next-line @typescript-eslint/no-var-requires
const { stub } = require('sinon')
describe('OpenAPIEmbeddings', function () {
const stubValue = {
data: {
data: [
{
embedding: Array(1536).fill(1.0)
},
{
embedding: Array(1536).fill(2.0)
}
]
}
}
describe('#embed', function () {
it('should create vector embeddings', async function () {
const openAIStub = stub(OpenAIApi.prototype, 'createEmbedding').returns(stubValue)
const f = new OpenAIEmbeddingFunction('text', 'sk-key')
const vectors = await f.embed(['abc', 'def'])
assert.isTrue(openAIStub.calledOnce)
assert.equal(vectors.length, 2)
assert.deepEqual(vectors[0], stubValue.data.data[0].embedding)
assert.deepEqual(vectors[1], stubValue.data.data[1].embedding)
})
})
describe('isEmbeddingFunction', function () {
it('should match the isEmbeddingFunction guard', function () {
assert.isTrue(isEmbeddingFunction(new OpenAIEmbeddingFunction('text', 'sk-key')))
})
})
})

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@@ -1,76 +0,0 @@
// Copyright 2023 Lance Developers.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// IO tests
import { describe } from 'mocha'
import { assert } from 'chai'
import * as lancedb from '../index'
import { type ConnectionOptions } from '../index'
describe('LanceDB S3 client', function () {
if (process.env.TEST_S3_BASE_URL != null) {
const baseUri = process.env.TEST_S3_BASE_URL
it('should have a valid url', async function () {
const opts = { uri: `${baseUri}/valid_url` }
const table = await createTestDB(opts, 2, 20)
const con = await lancedb.connect(opts)
assert.equal(con.uri, opts.uri)
const results = await table.search([0.1, 0.3]).limit(5).execute()
assert.equal(results.length, 5)
}).timeout(10_000)
} else {
describe.skip('Skip S3 test', function () {})
}
if (process.env.TEST_S3_BASE_URL != null && process.env.TEST_AWS_ACCESS_KEY_ID != null && process.env.TEST_AWS_SECRET_ACCESS_KEY != null) {
const baseUri = process.env.TEST_S3_BASE_URL
it('use custom credentials', async function () {
const opts: ConnectionOptions = {
uri: `${baseUri}/custom_credentials`,
awsCredentials: {
accessKeyId: process.env.TEST_AWS_ACCESS_KEY_ID as string,
secretKey: process.env.TEST_AWS_SECRET_ACCESS_KEY as string
}
}
const table = await createTestDB(opts, 2, 20)
console.log(table)
const con = await lancedb.connect(opts)
console.log(con)
assert.equal(con.uri, opts.uri)
const results = await table.search([0.1, 0.3]).limit(5).execute()
assert.equal(results.length, 5)
}).timeout(10_000)
} else {
describe.skip('Skip S3 test', function () {})
}
})
async function createTestDB (opts: ConnectionOptions, numDimensions: number = 2, numRows: number = 2): Promise<lancedb.Table> {
const con = await lancedb.connect(opts)
const data = []
for (let i = 0; i < numRows; i++) {
const vector = []
for (let j = 0; j < numDimensions; j++) {
vector.push(i + (j * 0.1))
}
data.push({ id: i + 1, name: `name_${i}`, price: i + 10, is_active: (i % 2 === 0), vector })
}
return await con.createTable('vectors_2', data)
}

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@@ -1,616 +0,0 @@
// Copyright 2023 LanceDB Developers.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import { describe } from 'mocha'
import { track } from 'temp'
import * as chai from 'chai'
import * as chaiAsPromised from 'chai-as-promised'
import * as lancedb from '../index'
import { type AwsCredentials, type EmbeddingFunction, MetricType, Query, WriteMode, DefaultWriteOptions, isWriteOptions, type LocalTable } from '../index'
import { FixedSizeList, Field, Int32, makeVector, Schema, Utf8, Table as ArrowTable, vectorFromArray, Float32 } from 'apache-arrow'
const expect = chai.expect
const assert = chai.assert
chai.use(chaiAsPromised)
describe('LanceDB client', function () {
describe('when creating a connection to lancedb', function () {
it('should have a valid url', async function () {
const uri = await createTestDB()
const con = await lancedb.connect(uri)
assert.equal(con.uri, uri)
})
it('should accept an options object', async function () {
const uri = await createTestDB()
const con = await lancedb.connect({ uri })
assert.equal(con.uri, uri)
})
it('should accept custom aws credentials', async function () {
const uri = await createTestDB()
const awsCredentials: AwsCredentials = {
accessKeyId: '',
secretKey: ''
}
const con = await lancedb.connect({ uri, awsCredentials })
assert.equal(con.uri, uri)
})
it('should return the existing table names', async function () {
const uri = await createTestDB()
const con = await lancedb.connect(uri)
assert.deepEqual(await con.tableNames(), ['vectors'])
})
})
describe('when querying an existing dataset', function () {
it('should open a table', async function () {
const uri = await createTestDB()
const con = await lancedb.connect(uri)
const table = await con.openTable('vectors')
assert.equal(table.name, 'vectors')
})
it('execute a query', async function () {
const uri = await createTestDB()
const con = await lancedb.connect(uri)
const table = await con.openTable('vectors')
const results = await table.search([0.1, 0.3]).execute()
assert.equal(results.length, 2)
assert.equal(results[0].price, 10)
const vector = results[0].vector as Float32Array
assert.approximately(vector[0], 0.0, 0.2)
assert.approximately(vector[0], 0.1, 0.3)
})
it('limits # of results', async function () {
const uri = await createTestDB(2, 100)
const con = await lancedb.connect(uri)
const table = await con.openTable('vectors')
let results = await table.search([0.1, 0.3]).limit(1).execute()
assert.equal(results.length, 1)
assert.equal(results[0].id, 1)
// there is a default limit if unspecified
results = await table.search([0.1, 0.3]).execute()
assert.equal(results.length, 10)
})
it('uses a filter / where clause without vector search', async function () {
// eslint-disable-next-line @typescript-eslint/explicit-function-return-type
const assertResults = (results: Array<Record<string, unknown>>) => {
assert.equal(results.length, 50)
}
const uri = await createTestDB(2, 100)
const con = await lancedb.connect(uri)
const table = (await con.openTable('vectors')) as LocalTable
let results = await table.filter('id % 2 = 0').execute()
assertResults(results)
results = await table.where('id % 2 = 0').execute()
assertResults(results)
})
it('uses a filter / where clause', async function () {
// eslint-disable-next-line @typescript-eslint/explicit-function-return-type
const assertResults = (results: Array<Record<string, unknown>>) => {
assert.equal(results.length, 1)
assert.equal(results[0].id, 2)
}
const uri = await createTestDB()
const con = await lancedb.connect(uri)
const table = await con.openTable('vectors')
let results = await table.search([0.1, 0.1]).filter('id == 2').execute()
assertResults(results)
results = await table.search([0.1, 0.1]).where('id == 2').execute()
assertResults(results)
})
it('should correctly process prefilter/postfilter', async function () {
const uri = await createTestDB(16, 300)
const con = await lancedb.connect(uri)
const table = await con.openTable('vectors')
await table.createIndex({ type: 'ivf_pq', column: 'vector', num_partitions: 2, max_iters: 2, num_sub_vectors: 2 })
// post filter should return less than the limit
let results = await table.search(new Array(16).fill(0.1)).limit(10).filter('id >= 10').prefilter(false).execute()
assert.isTrue(results.length < 10)
// pre filter should return exactly the limit
results = await table.search(new Array(16).fill(0.1)).limit(10).filter('id >= 10').prefilter(true).execute()
assert.isTrue(results.length === 10)
})
it('select only a subset of columns', async function () {
const uri = await createTestDB()
const con = await lancedb.connect(uri)
const table = await con.openTable('vectors')
const results = await table.search([0.1, 0.1]).select(['is_active']).execute()
assert.equal(results.length, 2)
// vector and _distance are always returned
assert.isDefined(results[0].vector)
assert.isDefined(results[0]._distance)
assert.isDefined(results[0].is_active)
assert.isUndefined(results[0].id)
assert.isUndefined(results[0].name)
assert.isUndefined(results[0].price)
})
})
describe('when creating a new dataset', function () {
it('create an empty table', async function () {
const dir = await track().mkdir('lancejs')
const con = await lancedb.connect(dir)
const schema = new Schema(
[new Field('id', new Int32()), new Field('name', new Utf8())]
)
const table = await con.createTable({ name: 'vectors', schema })
assert.equal(table.name, 'vectors')
assert.deepEqual(await con.tableNames(), ['vectors'])
})
it('create a table with a empty data array', async function () {
const dir = await track().mkdir('lancejs')
const con = await lancedb.connect(dir)
const schema = new Schema(
[new Field('id', new Int32()), new Field('name', new Utf8())]
)
const table = await con.createTable({ name: 'vectors', schema, data: [] })
assert.equal(table.name, 'vectors')
assert.deepEqual(await con.tableNames(), ['vectors'])
})
it('create a table from an Arrow Table', async function () {
const dir = await track().mkdir('lancejs')
const con = await lancedb.connect(dir)
const i32s = new Int32Array(new Array<number>(10))
const i32 = makeVector(i32s)
const data = new ArrowTable({ vector: i32 })
const table = await con.createTable({ name: 'vectors', data })
assert.equal(table.name, 'vectors')
assert.equal(await table.countRows(), 10)
assert.deepEqual(await con.tableNames(), ['vectors'])
})
it('creates a new table from javascript objects', async function () {
const dir = await track().mkdir('lancejs')
const con = await lancedb.connect(dir)
const data = [
{ id: 1, vector: [0.1, 0.2], price: 10 },
{ id: 2, vector: [1.1, 1.2], price: 50 }
]
const tableName = `vectors_${Math.floor(Math.random() * 100)}`
const table = await con.createTable(tableName, data)
assert.equal(table.name, tableName)
assert.equal(await table.countRows(), 2)
})
it('fails to create a new table when the vector column is missing', async function () {
const dir = await track().mkdir('lancejs')
const con = await lancedb.connect(dir)
const data = [
{ id: 1, price: 10 }
]
const create = con.createTable('missing_vector', data)
await expect(create).to.be.rejectedWith(Error, 'column \'vector\' is missing')
})
it('use overwrite flag to overwrite existing table', async function () {
const dir = await track().mkdir('lancejs')
const con = await lancedb.connect(dir)
const data = [
{ id: 1, vector: [0.1, 0.2], price: 10 },
{ id: 2, vector: [1.1, 1.2], price: 50 }
]
const tableName = 'overwrite'
await con.createTable(tableName, data, { writeMode: WriteMode.Create })
const newData = [
{ id: 1, vector: [0.1, 0.2], price: 10 },
{ id: 2, vector: [1.1, 1.2], price: 50 },
{ id: 3, vector: [1.1, 1.2], price: 50 }
]
await expect(con.createTable(tableName, newData)).to.be.rejectedWith(Error, 'already exists')
const table = await con.createTable(tableName, newData, { writeMode: WriteMode.Overwrite })
assert.equal(table.name, tableName)
assert.equal(await table.countRows(), 3)
})
it('appends records to an existing table ', async function () {
const dir = await track().mkdir('lancejs')
const con = await lancedb.connect(dir)
const data = [
{ id: 1, vector: [0.1, 0.2], price: 10, name: 'a' },
{ id: 2, vector: [1.1, 1.2], price: 50, name: 'b' }
]
const table = await con.createTable('vectors', data)
assert.equal(await table.countRows(), 2)
const dataAdd = [
{ id: 3, vector: [2.1, 2.2], price: 10, name: 'c' },
{ id: 4, vector: [3.1, 3.2], price: 50, name: 'd' }
]
await table.add(dataAdd)
assert.equal(await table.countRows(), 4)
})
it('overwrite all records in a table', async function () {
const uri = await createTestDB()
const con = await lancedb.connect(uri)
const table = await con.openTable('vectors')
assert.equal(await table.countRows(), 2)
const dataOver = [
{ vector: [2.1, 2.2], price: 10, name: 'foo' },
{ vector: [3.1, 3.2], price: 50, name: 'bar' }
]
await table.overwrite(dataOver)
assert.equal(await table.countRows(), 2)
})
it('can update records in the table', async function () {
const uri = await createTestDB()
const con = await lancedb.connect(uri)
const table = await con.openTable('vectors')
assert.equal(await table.countRows(), 2)
await table.update({ where: 'price = 10', valuesSql: { price: '100' } })
const results = await table.search([0.1, 0.2]).execute()
assert.equal(results[0].price, 100)
assert.equal(results[1].price, 11)
})
it('can update the records using a literal value', async function () {
const uri = await createTestDB()
const con = await lancedb.connect(uri)
const table = await con.openTable('vectors')
assert.equal(await table.countRows(), 2)
await table.update({ where: 'price = 10', values: { price: 100 } })
const results = await table.search([0.1, 0.2]).execute()
assert.equal(results[0].price, 100)
assert.equal(results[1].price, 11)
})
it('can update every record in the table', async function () {
const uri = await createTestDB()
const con = await lancedb.connect(uri)
const table = await con.openTable('vectors')
assert.equal(await table.countRows(), 2)
await table.update({ valuesSql: { price: '100' } })
const results = await table.search([0.1, 0.2]).execute()
assert.equal(results[0].price, 100)
assert.equal(results[1].price, 100)
})
it('can delete records from a table', async function () {
const uri = await createTestDB()
const con = await lancedb.connect(uri)
const table = await con.openTable('vectors')
assert.equal(await table.countRows(), 2)
await table.delete('price = 10')
assert.equal(await table.countRows(), 1)
})
})
describe('when searching an empty dataset', function () {
it('should not fail', async function () {
const dir = await track().mkdir('lancejs')
const con = await lancedb.connect(dir)
const schema = new Schema(
[new Field('vector', new FixedSizeList(128, new Field('float32', new Float32())))]
)
const table = await con.createTable({ name: 'vectors', schema })
const result = await table.search(Array(128).fill(0.1)).execute()
assert.isEmpty(result)
})
})
describe('when searching an empty-after-delete dataset', function () {
it('should not fail', async function () {
const dir = await track().mkdir('lancejs')
const con = await lancedb.connect(dir)
const schema = new Schema(
[new Field('vector', new FixedSizeList(128, new Field('float32', new Float32())))]
)
const table = await con.createTable({ name: 'vectors', schema })
await table.add([{ vector: Array(128).fill(0.1) }])
// https://github.com/lancedb/lance/issues/1635
await table.delete('true')
const result = await table.search(Array(128).fill(0.1)).execute()
assert.isEmpty(result)
})
})
describe('when creating a vector index', function () {
it('overwrite all records in a table', async function () {
const uri = await createTestDB(32, 300)
const con = await lancedb.connect(uri)
const table = await con.openTable('vectors')
await table.createIndex({ type: 'ivf_pq', column: 'vector', num_partitions: 2, max_iters: 2, num_sub_vectors: 2 })
}).timeout(10_000) // Timeout is high partially because GH macos runner is pretty slow
it('replace an existing index', async function () {
const uri = await createTestDB(16, 300)
const con = await lancedb.connect(uri)
const table = await con.openTable('vectors')
await table.createIndex({ type: 'ivf_pq', column: 'vector', num_partitions: 2, max_iters: 2, num_sub_vectors: 2 })
// Replace should fail if the index already exists
await expect(table.createIndex({
type: 'ivf_pq', column: 'vector', num_partitions: 2, max_iters: 2, num_sub_vectors: 2, replace: false
})
).to.be.rejectedWith('LanceError(Index)')
// Default replace = true
await table.createIndex({ type: 'ivf_pq', column: 'vector', num_partitions: 2, max_iters: 2, num_sub_vectors: 2 })
}).timeout(50_000)
it('it should fail when the column is not a vector', async function () {
const uri = await createTestDB(32, 300)
const con = await lancedb.connect(uri)
const table = await con.openTable('vectors')
const createIndex = table.createIndex({ type: 'ivf_pq', column: 'name', num_partitions: 2, max_iters: 2, num_sub_vectors: 2 })
await expect(createIndex).to.be.rejectedWith(/VectorIndex requires the column data type to be fixed size list of float32s/)
})
it('it should fail when the column is not a vector', async function () {
const uri = await createTestDB(32, 300)
const con = await lancedb.connect(uri)
const table = await con.openTable('vectors')
const createIndex = table.createIndex({ type: 'ivf_pq', column: 'name', num_partitions: -1, max_iters: 2, num_sub_vectors: 2 })
await expect(createIndex).to.be.rejectedWith('num_partitions: must be > 0')
})
it('should be able to list index and stats', async function () {
const uri = await createTestDB(32, 300)
const con = await lancedb.connect(uri)
const table = await con.openTable('vectors')
await table.createIndex({ type: 'ivf_pq', column: 'vector', num_partitions: 2, max_iters: 2, num_sub_vectors: 2 })
const indices = await table.listIndices()
expect(indices).to.have.lengthOf(1)
expect(indices[0].name).to.equal('vector_idx')
expect(indices[0].uuid).to.not.be.equal(undefined)
expect(indices[0].columns).to.have.lengthOf(1)
expect(indices[0].columns[0]).to.equal('vector')
const stats = await table.indexStats(indices[0].uuid)
expect(stats.numIndexedRows).to.equal(300)
expect(stats.numUnindexedRows).to.equal(0)
}).timeout(50_000)
})
describe('when using a custom embedding function', function () {
class TextEmbedding implements EmbeddingFunction<string> {
sourceColumn: string
constructor (targetColumn: string) {
this.sourceColumn = targetColumn
}
_embedding_map = new Map<string, number[]>([
['foo', [2.1, 2.2]],
['bar', [3.1, 3.2]]
])
async embed (data: string[]): Promise<number[][]> {
return data.map(datum => this._embedding_map.get(datum) ?? [0.0, 0.0])
}
}
it('should encode the original data into embeddings', async function () {
const dir = await track().mkdir('lancejs')
const con = await lancedb.connect(dir)
const embeddings = new TextEmbedding('name')
const data = [
{ price: 10, name: 'foo' },
{ price: 50, name: 'bar' }
]
const table = await con.createTable('vectors', data, embeddings, { writeMode: WriteMode.Create })
const results = await table.search('foo').execute()
assert.equal(results.length, 2)
})
it('should create embeddings for Arrow Table', async function () {
const dir = await track().mkdir('lancejs')
const con = await lancedb.connect(dir)
const embeddingFunction = new TextEmbedding('name')
const names = vectorFromArray(['foo', 'bar'], new Utf8())
const data = new ArrowTable({ name: names })
const table = await con.createTable({ name: 'vectors', data, embeddingFunction })
assert.equal(table.name, 'vectors')
const results = await table.search('foo').execute()
assert.equal(results.length, 2)
})
})
})
describe('Remote LanceDB client', function () {
describe('when the server is not reachable', function () {
it('produces a network error', async function () {
const con = await lancedb.connect({
uri: 'db://test-1234',
region: 'asdfasfasfdf',
apiKey: 'some-api-key'
})
// GET
try {
await con.tableNames()
} catch (err) {
expect(err).to.have.property('message', 'Network Error: getaddrinfo ENOTFOUND test-1234.asdfasfasfdf.api.lancedb.com')
}
// POST
try {
await con.createTable({ name: 'vectors', schema: new Schema([]) })
} catch (err) {
expect(err).to.have.property('message', 'Network Error: getaddrinfo ENOTFOUND test-1234.asdfasfasfdf.api.lancedb.com')
}
// Search
const table = await con.openTable('vectors')
try {
await table.search([0.1, 0.3]).execute()
} catch (err) {
expect(err).to.have.property('message', 'Network Error: getaddrinfo ENOTFOUND test-1234.asdfasfasfdf.api.lancedb.com')
}
})
})
})
describe('Query object', function () {
it('sets custom parameters', async function () {
const query = new Query([0.1, 0.3])
.limit(1)
.metricType(MetricType.Cosine)
.refineFactor(100)
.select(['a', 'b'])
.nprobes(20) as Record<string, any>
assert.equal(query._limit, 1)
assert.equal(query._metricType, MetricType.Cosine)
assert.equal(query._refineFactor, 100)
assert.equal(query._nprobes, 20)
assert.deepEqual(query._select, ['a', 'b'])
})
})
async function createTestDB (numDimensions: number = 2, numRows: number = 2): Promise<string> {
const dir = await track().mkdir('lancejs')
const con = await lancedb.connect(dir)
const data = []
for (let i = 0; i < numRows; i++) {
const vector = []
for (let j = 0; j < numDimensions; j++) {
vector.push(i + (j * 0.1))
}
data.push({ id: i + 1, name: `name_${i}`, price: i + 10, is_active: (i % 2 === 0), vector })
}
await con.createTable('vectors', data)
return dir
}
describe('Drop table', function () {
it('drop a table', async function () {
const dir = await track().mkdir('lancejs')
const con = await lancedb.connect(dir)
const data = [
{ price: 10, name: 'foo', vector: [1, 2, 3] },
{ price: 50, name: 'bar', vector: [4, 5, 6] }
]
await con.createTable('t1', data)
await con.createTable('t2', data)
assert.deepEqual(await con.tableNames(), ['t1', 't2'])
await con.dropTable('t1')
assert.deepEqual(await con.tableNames(), ['t2'])
})
})
describe('WriteOptions', function () {
context('#isWriteOptions', function () {
it('should not match empty object', function () {
assert.equal(isWriteOptions({}), false)
})
it('should match write options', function () {
assert.equal(isWriteOptions({ writeMode: WriteMode.Create }), true)
})
it('should match undefined write mode', function () {
assert.equal(isWriteOptions({ writeMode: undefined }), true)
})
it('should match default write options', function () {
assert.equal(isWriteOptions(new DefaultWriteOptions()), true)
})
})
})
describe('Compact and cleanup', function () {
it('can cleanup after compaction', async function () {
const dir = await track().mkdir('lancejs')
const con = await lancedb.connect(dir)
const data = [
{ price: 10, name: 'foo', vector: [1, 2, 3] },
{ price: 50, name: 'bar', vector: [4, 5, 6] }
]
const table = await con.createTable('t1', data) as LocalTable
const newData = [
{ price: 30, name: 'baz', vector: [7, 8, 9] }
]
await table.add(newData)
const compactionMetrics = await table.compactFiles({
numThreads: 2
})
assert.equal(compactionMetrics.fragmentsRemoved, 2)
assert.equal(compactionMetrics.fragmentsAdded, 1)
assert.equal(await table.countRows(), 3)
await table.cleanupOldVersions()
assert.equal(await table.countRows(), 3)
// should have no effect, but this validates the arguments are parsed.
await table.compactFiles({
targetRowsPerFragment: 102410,
maxRowsPerGroup: 1024,
materializeDeletions: true,
materializeDeletionsThreshold: 0.5,
numThreads: 2
})
const cleanupMetrics = await table.cleanupOldVersions(0, true)
assert.isAtLeast(cleanupMetrics.bytesRemoved, 1)
assert.isAtLeast(cleanupMetrics.oldVersions, 1)
assert.equal(await table.countRows(), 3)
})
})

View File

@@ -1,45 +0,0 @@
// Copyright 2023 LanceDB Developers.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
import { toSQL } from '../util'
import * as chai from 'chai'
const expect = chai.expect
describe('toSQL', function () {
it('should turn string to SQL expression', function () {
expect(toSQL('foo')).to.equal("'foo'")
})
it('should turn number to SQL expression', function () {
expect(toSQL(123)).to.equal('123')
})
it('should turn boolean to SQL expression', function () {
expect(toSQL(true)).to.equal('TRUE')
})
it('should turn null to SQL expression', function () {
expect(toSQL(null)).to.equal('NULL')
})
it('should turn Date to SQL expression', function () {
const date = new Date('05 October 2011 14:48 UTC')
expect(toSQL(date)).to.equal("'2011-10-05T14:48:00.000Z'")
})
it('should turn array to SQL expression', function () {
expect(toSQL(['foo', 'bar', true, 1])).to.equal("['foo', 'bar', TRUE, 1]")
})
})

375
node/src/types.ts Normal file
View File

@@ -0,0 +1,375 @@
import {
type Schema,
type Table as ArrowTable
} from 'apache-arrow'
import { type Literal } from './util'
import type { EmbeddingFunction } from './embedding/embedding_function'
import { type Query } from './query'
export interface AwsCredentials {
accessKeyId: string
secretKey: string
sessionToken?: string
}
/**
* Write options when creating a Table.
*/
export interface WriteOptions {
/** A {@link WriteMode} to use on this operation */
writeMode?: WriteMode
}
/**
* Write mode for writing a table.
*/
export enum WriteMode {
/** Create a new {@link Table}. */
Create = 'create',
/** Overwrite the existing {@link Table} if presented. */
Overwrite = 'overwrite',
/** Append new data to the table. */
Append = 'append'
}
/**
* A LanceDB Connection that allows you to open tables and create new ones.
*
* Connection could be local against filesystem or remote against a server.
*/
export interface Connection {
uri: string
tableNames(): Promise<string[]>
/**
* Open a table in the database.
*
* @param name The name of the table.
* @param embeddings An embedding function to use on this table
*/
openTable<T>(name: string, embeddings?: EmbeddingFunction<T>): Promise<Table<T>>
/**
* Creates a new Table, optionally initializing it with new data.
*
* @param {string} name - The name of the table.
* @param data - Array of Records to be inserted into the table
* @param schema - An Arrow Schema that describe this table columns
* @param {EmbeddingFunction} embeddings - An embedding function to use on this table
* @param {WriteOptions} writeOptions - The write options to use when creating the table.
*/
createTable<T> ({ name, data, schema, embeddingFunction, writeOptions }: CreateTableOptions<T>): Promise<Table<T>>
/**
* Creates a new Table and initialize it with new data.
*
* @param {string} name - The name of the table.
* @param data - Non-empty Array of Records to be inserted into the table
*/
createTable (name: string, data: Array<Record<string, unknown>>): Promise<Table>
/**
* Creates a new Table and initialize it with new data.
*
* @param {string} name - The name of the table.
* @param data - Non-empty Array of Records to be inserted into the table
* @param {WriteOptions} options - The write options to use when creating the table.
*/
createTable (name: string, data: Array<Record<string, unknown>>, options: WriteOptions): Promise<Table>
/**
* Creates a new Table and initialize it with new data.
*
* @param {string} name - The name of the table.
* @param data - Non-empty Array of Records to be inserted into the table
* @param {EmbeddingFunction} embeddings - An embedding function to use on this table
*/
createTable<T> (name: string, data: Array<Record<string, unknown>>, embeddings: EmbeddingFunction<T>): Promise<Table<T>>
/**
* Creates a new Table and initialize it with new data.
*
* @param {string} name - The name of the table.
* @param data - Non-empty Array of Records to be inserted into the table
* @param {EmbeddingFunction} embeddings - An embedding function to use on this table
* @param {WriteOptions} options - The write options to use when creating the table.
*/
createTable<T> (name: string, data: Array<Record<string, unknown>>, embeddings: EmbeddingFunction<T>, options: WriteOptions): Promise<Table<T>>
/**
* Drop an existing table.
* @param name The name of the table to drop.
*/
dropTable(name: string): Promise<void>
}
export interface CreateTableOptions<T> {
// Name of Table
name: string
// Data to insert into the Table
data?: Array<Record<string, unknown>> | ArrowTable | undefined
// Optional Arrow Schema for this table
schema?: Schema | undefined
// Optional embedding function used to create embeddings
embeddingFunction?: EmbeddingFunction<T> | undefined
// WriteOptions for this operation
writeOptions?: WriteOptions | undefined
}
export interface ConnectionOptions {
uri: string
awsCredentials?: AwsCredentials
awsRegion?: string
// API key for the remote connections
apiKey?: string
// Region to connect
region?: string
// override the host for the remote connections
hostOverride?: string
}
/**
* Distance metrics type.
*/
export enum MetricType {
/**
* Euclidean distance
*/
L2 = 'l2',
/**
* Cosine distance
*/
Cosine = 'cosine',
/**
* Dot product
*/
Dot = 'dot'
}
/// Config to build IVF_PQ index.
///
export interface IvfPQIndexConfig {
/**
* The column to be indexed
*/
column?: string
/**
* A unique name for the index
*/
index_name?: string
/**
* Metric type, L2 or Cosine
*/
metric_type?: MetricType
/**
* The number of partitions this index
*/
num_partitions?: number
/**
* The max number of iterations for kmeans training.
*/
max_iters?: number
/**
* Train as optimized product quantization.
*/
use_opq?: boolean
/**
* Number of subvectors to build PQ code
*/
num_sub_vectors?: number
/**
* The number of bits to present one PQ centroid.
*/
num_bits?: number
/**
* Max number of iterations to train OPQ, if `use_opq` is true.
*/
max_opq_iters?: number
/**
* Replace an existing index with the same name if it exists.
*/
replace?: boolean
type: 'ivf_pq'
}
export type VectorIndexParams = IvfPQIndexConfig
/**
* A LanceDB Table is the collection of Records. Each Record has one or more vector fields.
*/
export interface Table<T = number[]> {
name: string
/**
* Creates a search query to find the nearest neighbors of the given search term
* @param query The query search term
*/
search: (query: T) => Query<T>
/**
* Insert records into this Table.
*
* @param data Records to be inserted into the Table
* @return The number of rows added to the table
*/
add: (data: Array<Record<string, unknown>>) => Promise<number>
/**
* Insert records into this Table, replacing its contents.
*
* @param data Records to be inserted into the Table
* @return The number of rows added to the table
*/
overwrite: (data: Array<Record<string, unknown>>) => Promise<number>
/**
* Create an ANN index on this Table vector index.
*
* @param indexParams The parameters of this Index, @see VectorIndexParams.
*/
createIndex: (indexParams: VectorIndexParams) => Promise<any>
/**
* Returns the number of rows in this table.
*/
countRows: () => Promise<number>
/**
* Delete rows from this table.
*
* This can be used to delete a single row, many rows, all rows, or
* sometimes no rows (if your predicate matches nothing).
*
* @param filter A filter in the same format used by a sql WHERE clause. The
* filter must not be empty.
*
* @examples
*
* ```ts
* const con = await lancedb.connect("./.lancedb")
* const data = [
* {id: 1, vector: [1, 2]},
* {id: 2, vector: [3, 4]},
* {id: 3, vector: [5, 6]},
* ];
* const tbl = await con.createTable("my_table", data)
* await tbl.delete("id = 2")
* await tbl.countRows() // Returns 2
* ```
*
* If you have a list of values to delete, you can combine them into a
* stringified list and use the `IN` operator:
*
* ```ts
* const to_remove = [1, 5];
* await tbl.delete(`id IN (${to_remove.join(",")})`)
* await tbl.countRows() // Returns 1
* ```
*/
delete: (filter: string) => Promise<void>
/**
* Update rows in this table.
*
* This can be used to update a single row, many rows, all rows, or
* sometimes no rows (if your predicate matches nothing).
*
* @param args see {@link UpdateArgs} and {@link UpdateSqlArgs} for more details
*
* @examples
*
* ```ts
* const con = await lancedb.connect("./.lancedb")
* const data = [
* {id: 1, vector: [3, 3], name: 'Ye'},
* {id: 2, vector: [4, 4], name: 'Mike'},
* ];
* const tbl = await con.createTable("my_table", data)
*
* await tbl.update({
* filter: "id = 2",
* updates: { vector: [2, 2], name: "Michael" },
* })
*
* let results = await tbl.search([1, 1]).execute();
* // Returns [
* // {id: 2, vector: [2, 2], name: 'Michael'}
* // {id: 1, vector: [3, 3], name: 'Ye'}
* // ]
* ```
*
*/
update: (args: UpdateArgs | UpdateSqlArgs) => Promise<void>
/**
* List the indicies on this table.
*/
listIndices: () => Promise<VectorIndex[]>
/**
* Get statistics about an index.
*/
indexStats: (indexUuid: string) => Promise<IndexStats>
}
export interface UpdateArgs {
/**
* A filter in the same format used by a sql WHERE clause. The filter may be empty,
* in which case all rows will be updated.
*/
where?: string
/**
* A key-value map of updates. The keys are the column names, and the values are the
* new values to set
*/
values: Record<string, Literal>
}
export interface UpdateSqlArgs {
/**
* A filter in the same format used by a sql WHERE clause. The filter may be empty,
* in which case all rows will be updated.
*/
where?: string
/**
* A key-value map of updates. The keys are the column names, and the values are the
* new values to set as SQL expressions.
*/
valuesSql: Record<string, string>
}
export interface VectorIndex {
columns: string[]
name: string
uuid: string
}
export interface IndexStats {
numIndexedRows: number | null
numUnindexedRows: number | null
}