import { type EmbeddingFunction } from './embedding/embedding_function'; import { type MetricType } from '.'; /** * A builder for nearest neighbor queries for LanceDB. */ export declare class Query { private readonly _query?; private readonly _tbl?; private _queryVector?; private _limit?; private _refineFactor?; private _nprobes; private _select?; private _filter?; private _metricType?; private _prefilter; private _fastSearch; protected readonly _embeddings?: EmbeddingFunction; constructor(query?: T, tbl?: any, embeddings?: EmbeddingFunction); /*** * Sets the number of results that will be returned * default value is 10 * @param value number of results */ limit(value: number): Query; /** * Refine the results by reading extra elements and re-ranking them in memory. * @param value refine factor to use in this query. */ refineFactor(value: number): Query; /** * The number of probes used. A higher number makes search more accurate but also slower. * @param value The number of probes used. */ nprobes(value: number): Query; /** * A filter statement to be applied to this query. * @param value A filter in the same format used by a sql WHERE clause. */ filter(value: string): Query; where: (value: string) => Query; /** Return only the specified columns. * * @param value Only select the specified columns. If not specified, all columns will be returned. */ select(value: string[]): Query; /** * The MetricType used for this Query. * @param value The metric to the. @see MetricType for the different options */ metricType(value: MetricType): Query; prefilter(value: boolean): Query; /** * Skip searching un-indexed data. This can make search faster, but will miss * any data that is not yet indexed. */ fastSearch(value: boolean): Query; /** * Execute the query and return the results as an Array of Objects */ execute>(): Promise; private isElectron; }