import type { IndexFlatL2 } from "faiss-node";
import type { NameRegistry, Parser } from "pickleparser";
import type { EmbeddingsInterface } from "@langchain/core/embeddings";
import { SaveableVectorStore } from "@langchain/core/vectorstores";
import { Document } from "@langchain/core/documents";
import { SynchronousInMemoryDocstore } from "../stores/doc/in_memory.js";
/**
 * Interface for the arguments required to initialize a FaissStore
 * instance.
 */
export interface FaissLibArgs {
    docstore?: SynchronousInMemoryDocstore;
    index?: IndexFlatL2;
    mapping?: Record<number, string>;
}
/**
 * A class that wraps the FAISS (Facebook AI Similarity Search) vector
 * database for efficient similarity search and clustering of dense
 * vectors.
 */
export declare class FaissStore extends SaveableVectorStore {
    _index?: IndexFlatL2;
    _mapping: Record<number, string>;
    docstore: SynchronousInMemoryDocstore;
    args: FaissLibArgs;
    _vectorstoreType(): string;
    getMapping(): Record<number, string>;
    getDocstore(): SynchronousInMemoryDocstore;
    constructor(embeddings: EmbeddingsInterface, args: FaissLibArgs);
    /**
     * Adds an array of Document objects to the store.
     * @param documents An array of Document objects.
     * @returns A Promise that resolves when the documents have been added.
     */
    addDocuments(documents: Document[], options?: {
        ids?: string[];
    }): Promise<string[]>;
    get index(): IndexFlatL2;
    private set index(value);
    /**
     * Adds an array of vectors and their corresponding Document objects to
     * the store.
     * @param vectors An array of vectors.
     * @param documents An array of Document objects corresponding to the vectors.
     * @returns A Promise that resolves with an array of document IDs when the vectors and documents have been added.
     */
    addVectors(vectors: number[][], documents: Document[], options?: {
        ids?: string[];
    }): Promise<string[]>;
    /**
     * Performs a similarity search in the vector store using a query vector
     * and returns the top k results along with their scores.
     * @param query A query vector.
     * @param k The number of top results to return.
     * @returns A Promise that resolves with an array of tuples, each containing a Document and its corresponding score.
     */
    similaritySearchVectorWithScore(query: number[], k: number): Promise<any>;
    /**
     * Saves the current state of the FaissStore to a specified directory.
     * @param directory The directory to save the state to.
     * @returns A Promise that resolves when the state has been saved.
     */
    save(directory: string): Promise<void>;
    /**
     * Method to delete documents.
     * @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>;
    /**
     * Merges the current FaissStore with another FaissStore.
     * @param targetIndex The FaissStore to merge with.
     * @returns A Promise that resolves with an array of document IDs when the merge is complete.
     */
    mergeFrom(targetIndex: FaissStore): Promise<string[]>;
    /**
     * Loads a FaissStore from a specified directory.
     * @param directory The directory to load the FaissStore from.
     * @param embeddings An Embeddings object.
     * @returns A Promise that resolves with a new FaissStore instance.
     */
    static load(directory: string, embeddings: EmbeddingsInterface): Promise<FaissStore>;
    static loadFromPython(directory: string, embeddings: EmbeddingsInterface): Promise<FaissStore>;
    /**
     * Creates a new FaissStore from an array of texts, their corresponding
     * metadata, and an Embeddings object.
     * @param texts An array of texts.
     * @param metadatas An array of metadata corresponding to the texts, or a single metadata object to be used for all texts.
     * @param embeddings An Embeddings object.
     * @param dbConfig An optional configuration object for the document store.
     * @returns A Promise that resolves with a new FaissStore instance.
     */
    static fromTexts(texts: string[], metadatas: object[] | object, embeddings: EmbeddingsInterface, dbConfig?: {
        docstore?: SynchronousInMemoryDocstore;
    }): Promise<FaissStore>;
    /**
     * Creates a new FaissStore from an array of Document objects and an
     * Embeddings object.
     * @param docs An array of Document objects.
     * @param embeddings An Embeddings object.
     * @param dbConfig An optional configuration object for the document store.
     * @returns A Promise that resolves with a new FaissStore instance.
     */
    static fromDocuments(docs: Document[], embeddings: EmbeddingsInterface, dbConfig?: {
        docstore?: SynchronousInMemoryDocstore;
    }): Promise<FaissStore>;
    /**
     * Creates a new FaissStore from an existing FaissStore and an Embeddings
     * object.
     * @param targetIndex An existing FaissStore.
     * @param embeddings An Embeddings object.
     * @param dbConfig An optional configuration object for the document store.
     * @returns A Promise that resolves with a new FaissStore instance.
     */
    static fromIndex(targetIndex: FaissStore, embeddings: EmbeddingsInterface, dbConfig?: {
        docstore?: SynchronousInMemoryDocstore;
    }): Promise<FaissStore>;
    static importFaiss(): Promise<{
        IndexFlatL2: typeof IndexFlatL2;
    }>;
    static importPickleparser(): Promise<{
        Parser: typeof Parser;
        NameRegistry: typeof NameRegistry;
    }>;
}
