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https://github.com/lancedb/lancedb.git
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[Node] initial support of nodejs remote sdk (#333)
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@@ -14,15 +14,15 @@
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import {
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RecordBatchFileWriter,
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type Table as ArrowTable,
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tableFromIPC,
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Vector
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type Table as ArrowTable
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} from 'apache-arrow'
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import { fromRecordsToBuffer } from './arrow'
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import type { EmbeddingFunction } from './embedding/embedding_function'
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import { RemoteConnection } from './remote'
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import { Query } from './query'
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// eslint-disable-next-line @typescript-eslint/no-var-requires
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const { databaseNew, databaseTableNames, databaseOpenTable, databaseDropTable, tableCreate, tableSearch, tableAdd, tableCreateVectorIndex, tableCountRows, tableDelete } = require('../native.js')
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const { databaseNew, databaseTableNames, databaseOpenTable, databaseDropTable, tableCreate, tableAdd, tableCreateVectorIndex, tableCountRows, tableDelete } = require('../native.js')
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export type { EmbeddingFunction }
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export { OpenAIEmbeddingFunction } from './embedding/openai'
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@@ -37,7 +37,13 @@ export interface AwsCredentials {
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export interface ConnectionOptions {
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uri: string
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awsCredentials?: AwsCredentials
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// API key for the remote connections
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apiKey?: string
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// Region to connect
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region?: string
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}
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/**
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@@ -54,9 +60,16 @@ export async function connect (arg: string | Partial<ConnectionOptions>): Promis
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// opts = { uri: arg.uri, awsCredentials = arg.awsCredentials }
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opts = Object.assign({
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uri: '',
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awsCredentials: undefined
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awsCredentials: undefined,
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apiKey: undefined,
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region: 'us-west-2'
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}, arg)
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}
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if (opts.uri.startsWith('db://')) {
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// Remote connection
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return new RemoteConnection(opts)
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}
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const db = await databaseNew(opts.uri)
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return new LocalConnection(db, opts)
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}
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@@ -191,8 +204,8 @@ export class LocalConnection implements Connection {
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}
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/**
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* Get the names of all tables in the database.
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*/
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* Get the names of all tables in the database.
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*/
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async tableNames (): Promise<string[]> {
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return databaseTableNames.call(this._db)
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}
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@@ -203,6 +216,7 @@ export class LocalConnection implements Connection {
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* @param name The name of the table.
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*/
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async openTable (name: string): Promise<Table>
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/**
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* Open a table in the database.
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*
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@@ -308,7 +322,7 @@ export class LocalTable<T = number[]> implements Table<T> {
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* @param query The query search term
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*/
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search (query: T): Query<T> {
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return new Query(this._tbl, query, this._embeddings)
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return new Query(query, this._tbl, this._embeddings)
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}
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/**
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@@ -430,116 +444,6 @@ export interface IvfPQIndexConfig {
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export type VectorIndexParams = IvfPQIndexConfig
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/**
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* A builder for nearest neighbor queries for LanceDB.
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*/
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export class Query<T = number[]> {
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private readonly _tbl: any
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private readonly _query: T
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private _queryVector?: number[]
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private _limit: number
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private _refineFactor?: number
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private _nprobes: number
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private _select?: string[]
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private _filter?: string
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private _metricType?: MetricType
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private readonly _embeddings?: EmbeddingFunction<T>
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constructor (tbl: any, query: T, embeddings?: EmbeddingFunction<T>) {
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this._tbl = tbl
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this._query = query
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this._limit = 10
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this._nprobes = 20
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this._refineFactor = undefined
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this._select = undefined
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this._filter = undefined
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this._metricType = undefined
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this._embeddings = embeddings
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}
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/***
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* Sets the number of results that will be returned
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* @param value number of results
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*/
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limit (value: number): Query<T> {
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this._limit = value
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return this
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}
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/**
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* Refine the results by reading extra elements and re-ranking them in memory.
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* @param value refine factor to use in this query.
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*/
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refineFactor (value: number): Query<T> {
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this._refineFactor = value
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return this
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}
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/**
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* The number of probes used. A higher number makes search more accurate but also slower.
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* @param value The number of probes used.
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*/
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nprobes (value: number): Query<T> {
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this._nprobes = value
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return this
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}
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/**
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* A filter statement to be applied to this query.
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* @param value A filter in the same format used by a sql WHERE clause.
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*/
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filter (value: string): Query<T> {
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this._filter = value
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return this
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}
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where = this.filter
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/** Return only the specified columns.
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*
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* @param value Only select the specified columns. If not specified, all columns will be returned.
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*/
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select (value: string[]): Query<T> {
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this._select = value
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return this
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}
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/**
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* The MetricType used for this Query.
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* @param value The metric to the. @see MetricType for the different options
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*/
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metricType (value: MetricType): Query<T> {
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this._metricType = value
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return this
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}
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/**
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* Execute the query and return the results as an Array of Objects
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*/
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async execute<T = Record<string, unknown>> (): Promise<T[]> {
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if (this._embeddings !== undefined) {
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this._queryVector = (await this._embeddings.embed([this._query]))[0]
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} else {
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this._queryVector = this._query as number[]
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}
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const buffer = await tableSearch.call(this._tbl, this)
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const data = tableFromIPC(buffer)
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return data.toArray().map((entry: Record<string, unknown>) => {
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const newObject: Record<string, unknown> = {}
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Object.keys(entry).forEach((key: string) => {
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if (entry[key] instanceof Vector) {
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newObject[key] = (entry[key] as Vector).toArray()
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} else {
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newObject[key] = entry[key]
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}
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})
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return newObject as unknown as T
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})
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}
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}
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/**
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* Write mode for writing a table.
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*/
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