@pinecone-database/pinecone/dist/data/vectors/searchRecords.d.ts

This is the official Node.js SDK for [Pinecone](https://www.pinecone.io), written in TypeScript.

import { VectorOperationsProvider } from './vectorOperationsProvider';
import { SearchRecordsResponse } from '../../pinecone-generated-ts-fetch/db_data';
/**
 * Options for searching records within a specific namespace.
 * @see [Upsert and search with integrated inference](https://docs.pinecone.io/guides/inference/integrated-inference).
 */
export type SearchRecordsOptions = {
    /**
     * The query to use for searching.
     */
    query: SearchRecordsQuery;
    /**
     * The fields to return in the search results. If not specified, the response will include all fields.
     */
    fields?: Array<string>;
    /**
     * Parameters to rerank the initial search results.
     */
    rerank?: SearchRecordsRerank;
};
/**
 * The query object used with a {@link SearchRecordsOptions } request.
 */
export type SearchRecordsQuery = {
    /**
     * The number of similar records to return.
     */
    topK: number;
    /**
     * The filter to apply. You can use vector metadata to limit your search.
     * @see [Understanding metadata](https://docs.pinecone.io/guides/data/understanding-metadata).
     */
    filter?: object;
    /**
     * The query text to search with. Searching with text is supported only for indexes with [integrated embedding](https://docs.pinecone.io/guides/indexes/create-an-index#integrated-embedding).
     */
    inputs?: object;
    /**
     * The {@link SearchRecordsVector} to search with, if provided.
     */
    vector?: SearchRecordsVector;
};
/**
 * A vector object used with a {@link SearchRecordsQuery } request.
 */
export type SearchRecordsVector = {
    /**
     * The dense embedding values to search with.
     */
    values?: Array<number>;
    /**
     * The sparse embedding values to search with.
     */
    sparseValues?: Array<number>;
    /**
     * The sparse embedding indices to search with.
     */
    sparseIndices?: Array<number>;
};
/**
 * Parameters used for reranking the initial search results.
 */
export type SearchRecordsRerank = {
    /**
     * The name of the [reranking model](https://docs.pinecone.io/guides/inference/understanding-inference#reranking-models) to use.
     */
    model: string;
    /**
     * The field(s) to consider for reranking. If not provided, the default is `["text"]`.
     * The number of fields supported is [model-specific](https://docs.pinecone.io/guides/inference/understanding-inference#reranking-models)
     */
    rankFields: Array<string>;
    /**
     * The number of top results to return after reranking. Defaults to the `topK` in {@link SearchRecordsQuery}.
     */
    topN?: number;
    /**
     * Additional model-specific parameters. Refer to the [model guide](https://docs.pinecone.io/guides/inference/understanding-inference#reranking-models)
     * for available model parameters.
     */
    parameters?: {
        [key: string]: any;
    };
    /**
     * The query to rerank documents against. If a specific rerank query is specified,
     * it overwrites the query input that was provided at the top level.
     */
    query?: string;
};
export declare class SearchRecordsCommand {
    apiProvider: VectorOperationsProvider;
    namespace: string;
    constructor(apiProvider: any, namespace: any);
    validator: (options: SearchRecordsOptions) => void;
    run(searchOptions: SearchRecordsOptions, maxRetries?: number): Promise<SearchRecordsResponse>;
}