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

import { CreateCollectionOptions } from "@datastax/astra-db-ts";
import { AsyncCaller, AsyncCallerParams } from "@langchain/core/utils/async_caller";
import { Document } from "@langchain/core/documents";
import type { EmbeddingsInterface } from "@langchain/core/embeddings";
import { MaxMarginalRelevanceSearchOptions, VectorStore } from "@langchain/core/vectorstores";
export type CollectionFilter = Record<string, unknown>;
export interface AstraLibArgs extends AsyncCallerParams {
    token: string;
    endpoint: string;
    collection: string;
    namespace?: string;
    idKey?: string;
    contentKey?: string;
    skipCollectionProvisioning?: boolean;
    collectionOptions?: CreateCollectionOptions<any>;
    batchSize?: number;
}
export type AstraDeleteParams = {
    ids: string[];
};
export declare class AstraDBVectorStore extends VectorStore {
    FilterType: CollectionFilter;
    private astraDBClient;
    private collectionName;
    private collection;
    private collectionOptions;
    private readonly idKey;
    private readonly contentKey;
    caller: AsyncCaller;
    private readonly skipCollectionProvisioning;
    _vectorstoreType(): string;
    constructor(embeddings: EmbeddingsInterface, args: AstraLibArgs);
    private static applyCollectionOptionsDefaults;
    /**
     * Create a new collection in your Astra DB vector database and then connects to it.
     * If the collection already exists, it will connect to it as well.
     *
     * @returns Promise that resolves if connected to the collection.
     */
    initialize(): Promise<void>;
    /**
     * Method to save vectors to AstraDB.
     *
     * @param vectors Vectors to save.
     * @param documents The documents associated with the vectors.
     * @returns Promise that resolves when the vectors have been added.
     */
    addVectors(vectors: number[][], documents: Document[], options?: string[]): Promise<void>;
    /**
     * Method that adds documents to AstraDB.
     *
     * @param documents Array of documents to add to AstraDB.
     * @param options Optional ids for the documents.
     * @returns Promise that resolves the documents have been added.
     */
    addDocuments(documents: Document[], options?: string[]): Promise<void>;
    /**
     * Method that deletes documents from AstraDB.
     *
     * @param params AstraDeleteParameters for the delete.
     * @returns Promise that resolves when the documents have been deleted.
     */
    delete(params: AstraDeleteParams): Promise<void>;
    /**
     * Method that performs a similarity search in AstraDB and returns and similarity scores.
     *
     * @param query Query vector for the similarity search.
     * @param k Number of top results to return.
     * @param filter Optional filter to apply to the search.
     * @returns Promise that resolves with an array of documents and their scores.
     */
    similaritySearchVectorWithScore(query: number[], k: number, filter?: CollectionFilter): Promise<[Document, number][]>;
    /**
     * Return documents selected using the maximal marginal relevance.
     * Maximal marginal relevance optimizes for similarity to the query AND diversity
     * among selected documents.
     *
     * @param {string} query - Text to look up documents similar to.
     * @param {number} options.k - Number of documents to return.
     * @param {number} options.fetchK - Number of documents to fetch before passing to the MMR algorithm.
     * @param {number} options.lambda - Number between 0 and 1 that determines the degree of diversity among the results,
     *                 where 0 corresponds to maximum diversity and 1 to minimum diversity.
     * @param {CollectionFilter} options.filter - Optional filter
     *
     * @returns {Promise<Document[]>} - List of documents selected by maximal marginal relevance.
     */
    maxMarginalRelevanceSearch(query: string, options: MaxMarginalRelevanceSearchOptions<this["FilterType"]>): Promise<Document[]>;
    /**
     * Static method to create an instance of AstraDBVectorStore from texts.
     *
     * @param texts The texts to use.
     * @param metadatas The metadata associated with the texts.
     * @param embeddings The embeddings to use.
     * @param dbConfig The arguments for the AstraDBVectorStore.
     * @returns Promise that resolves with a new instance of AstraDBVectorStore.
     */
    static fromTexts(texts: string[], metadatas: object[] | object, embeddings: EmbeddingsInterface, dbConfig: AstraLibArgs): Promise<AstraDBVectorStore>;
    /**
     * Static method to create an instance of AstraDBVectorStore from documents.
     *
     * @param docs The Documents to use.
     * @param embeddings The embeddings to use.
     * @param dbConfig The arguments for the AstraDBVectorStore.
     * @returns Promise that resolves with a new instance of AstraDBVectorStore.
     */
    static fromDocuments(docs: Document[], embeddings: EmbeddingsInterface, dbConfig: AstraLibArgs): Promise<AstraDBVectorStore>;
    /**
     * Static method to create an instance of AstraDBVectorStore from an existing index.
     *
     * @param embeddings The embeddings to use.
     * @param dbConfig The arguments for the AstraDBVectorStore.
     * @returns Promise that resolves with a new instance of AstraDBVectorStore.
     */
    static fromExistingIndex(embeddings: EmbeddingsInterface, dbConfig: AstraLibArgs): Promise<AstraDBVectorStore>;
}