agsamantha/node_modules/@langchain/community/dist/vectorstores/elasticsearch.d.ts

import { Client } from "@elastic/elasticsearch";
import type { EmbeddingsInterface } from "@langchain/core/embeddings";
import { VectorStore } from "@langchain/core/vectorstores";
import { Document } from "@langchain/core/documents";
/**
 * Type representing the k-nearest neighbors (k-NN) engine used in
 * Elasticsearch.
 */
type ElasticKnnEngine = "hnsw";
/**
 * Type representing the similarity measure used in Elasticsearch.
 */
type ElasticSimilarity = "l2_norm" | "dot_product" | "cosine";
/**
 * Interface defining the options for vector search in Elasticsearch.
 */
interface VectorSearchOptions {
    readonly engine?: ElasticKnnEngine;
    readonly similarity?: ElasticSimilarity;
    readonly m?: number;
    readonly efConstruction?: number;
    readonly candidates?: number;
}
/**
 * Interface defining the arguments required to create an Elasticsearch
 * client.
 */
export interface ElasticClientArgs {
    readonly client: Client;
    readonly indexName?: string;
    readonly vectorSearchOptions?: VectorSearchOptions;
}
/**
 * Type representing a filter object in Elasticsearch.
 */
type ElasticFilter = object | {
    field: string;
    operator: string;
    value: any;
}[];
/**
 * Class for interacting with an Elasticsearch database. It extends the
 * VectorStore base class and provides methods for adding documents and
 * vectors to the Elasticsearch database, performing similarity searches,
 * deleting documents, and more.
 */
export declare class ElasticVectorSearch extends VectorStore {
    FilterType: ElasticFilter;
    private readonly client;
    private readonly indexName;
    private readonly engine;
    private readonly similarity;
    private readonly efConstruction;
    private readonly m;
    private readonly candidates;
    _vectorstoreType(): string;
    constructor(embeddings: EmbeddingsInterface, args: ElasticClientArgs);
    /**
     * Method to add documents to the Elasticsearch database. It first
     * converts the documents to vectors using the embeddings, then adds the
     * vectors to the database.
     * @param documents The documents to add to the database.
     * @param options Optional parameter that can contain the IDs for the documents.
     * @returns A promise that resolves with the IDs of the added documents.
     */
    addDocuments(documents: Document[], options?: {
        ids?: string[];
    }): Promise<string[]>;
    /**
     * Method to add vectors to the Elasticsearch database. It ensures the
     * index exists, then adds the vectors and their corresponding documents
     * to the database.
     * @param vectors The vectors to add to the database.
     * @param documents The documents corresponding to the vectors.
     * @param options Optional parameter that can contain the IDs for the documents.
     * @returns A promise that resolves with the IDs of the added documents.
     */
    addVectors(vectors: number[][], documents: Document[], options?: {
        ids?: string[];
    }): Promise<string[]>;
    /**
     * Method to perform a similarity search in the Elasticsearch database
     * using a vector. It returns the k most similar documents along with
     * their similarity scores.
     * @param query The query vector.
     * @param k The number of most similar documents to return.
     * @param filter Optional filter to apply to the search.
     * @returns A promise that resolves with an array of tuples, where each tuple contains a Document and its similarity score.
     */
    similaritySearchVectorWithScore(query: number[], k: number, filter?: ElasticFilter): Promise<[Document, number][]>;
    /**
     * Method to delete documents from the Elasticsearch database.
     * @param params Object containing the IDs of the documents to delete.
     * @returns A promise that resolves when the deletion is complete.
     */
    delete(params: {
        ids: string[];
    }): Promise<void>;
    /**
     * Static method to create an ElasticVectorSearch instance from texts. It
     * creates Document instances from the texts and their corresponding
     * metadata, then calls the fromDocuments method to create the
     * ElasticVectorSearch instance.
     * @param texts The texts to create the ElasticVectorSearch instance from.
     * @param metadatas The metadata corresponding to the texts.
     * @param embeddings The embeddings to use for the documents.
     * @param args The arguments to create the Elasticsearch client.
     * @returns A promise that resolves with the created ElasticVectorSearch instance.
     */
    static fromTexts(texts: string[], metadatas: object[] | object, embeddings: EmbeddingsInterface, args: ElasticClientArgs): Promise<ElasticVectorSearch>;
    /**
     * Static method to create an ElasticVectorSearch instance from Document
     * instances. It adds the documents to the Elasticsearch database, then
     * returns the ElasticVectorSearch instance.
     * @param docs The Document instances to create the ElasticVectorSearch instance from.
     * @param embeddings The embeddings to use for the documents.
     * @param dbConfig The configuration for the Elasticsearch database.
     * @returns A promise that resolves with the created ElasticVectorSearch instance.
     */
    static fromDocuments(docs: Document[], embeddings: EmbeddingsInterface, dbConfig: ElasticClientArgs): Promise<ElasticVectorSearch>;
    /**
     * Static method to create an ElasticVectorSearch instance from an
     * existing index in the Elasticsearch database. It checks if the index
     * exists, then returns the ElasticVectorSearch instance if it does.
     * @param embeddings The embeddings to use for the documents.
     * @param dbConfig The configuration for the Elasticsearch database.
     * @returns A promise that resolves with the created ElasticVectorSearch instance if the index exists, otherwise it throws an error.
     */
    static fromExistingIndex(embeddings: EmbeddingsInterface, dbConfig: ElasticClientArgs): Promise<ElasticVectorSearch>;
    private ensureIndexExists;
    private buildMetadataTerms;
    /**
     * Method to check if an index exists in the Elasticsearch database.
     * @returns A promise that resolves with a boolean indicating whether the index exists.
     */
    doesIndexExist(): Promise<boolean>;
    /**
     * Method to delete an index from the Elasticsearch database if it exists.
     * @returns A promise that resolves when the deletion is complete.
     */
    deleteIfExists(): Promise<void>;
}
export {};