import type { EmbeddingsInterface } from "@langchain/core/embeddings"; import { VectorStore } from "@langchain/core/vectorstores"; import { Document } from "@langchain/core/documents"; /** * Type definition for the arguments required to initialize a * TigrisVectorStore instance. */ export type TigrisLibArgs = { index: any; }; /** * Class for managing and operating vector search applications with * Tigris, an open-source Serverless NoSQL Database and Search Platform. */ export declare class TigrisVectorStore extends VectorStore { index?: any; _vectorstoreType(): string; constructor(embeddings: EmbeddingsInterface, args: TigrisLibArgs); /** * Method to add an array of documents to the Tigris database. * @param documents An array of Document instances to be added to the Tigris database. * @param options Optional parameter that can either be an array of string IDs or an object with a property 'ids' that is an array of string IDs. * @returns A Promise that resolves when the documents have been added to the Tigris database. */ addDocuments(documents: Document[], options?: { ids?: string[]; } | string[]): Promise; /** * Method to add vectors to the Tigris database. * @param vectors An array of vectors to be added to the Tigris database. * @param documents An array of Document instances corresponding to the vectors. * @param options Optional parameter that can either be an array of string IDs or an object with a property 'ids' that is an array of string IDs. * @returns A Promise that resolves when the vectors have been added to the Tigris database. */ addVectors(vectors: number[][], documents: Document[], options?: { ids?: string[]; } | string[]): Promise; /** * Method to perform a similarity search in the Tigris database and return * the k most similar vectors along with their similarity scores. * @param query The query vector. * @param k The number of most similar vectors to return. * @param filter Optional filter object to apply during the search. * @returns A Promise that resolves to an array of tuples, each containing a Document and its similarity score. */ similaritySearchVectorWithScore(query: number[], k: number, filter?: object): Promise<[Document>, number][]>; /** * Static method to create a new instance of TigrisVectorStore from an * array of texts. * @param texts An array of texts to be converted into Document instances and added to the Tigris database. * @param metadatas Either an array of metadata objects or a single metadata object to be associated with the texts. * @param embeddings An instance of Embeddings to be used for embedding the texts. * @param dbConfig An instance of TigrisLibArgs to be used for configuring the Tigris database. * @returns A Promise that resolves to a new instance of TigrisVectorStore. */ static fromTexts(texts: string[], metadatas: object[] | object, embeddings: EmbeddingsInterface, dbConfig: TigrisLibArgs): Promise; /** * Static method to create a new instance of TigrisVectorStore from an * array of Document instances. * @param docs An array of Document instances to be added to the Tigris database. * @param embeddings An instance of Embeddings to be used for embedding the documents. * @param dbConfig An instance of TigrisLibArgs to be used for configuring the Tigris database. * @returns A Promise that resolves to a new instance of TigrisVectorStore. */ static fromDocuments(docs: Document[], embeddings: EmbeddingsInterface, dbConfig: TigrisLibArgs): Promise; /** * Static method to create a new instance of TigrisVectorStore from an * existing index. * @param embeddings An instance of Embeddings to be used for embedding the documents. * @param dbConfig An instance of TigrisLibArgs to be used for configuring the Tigris database. * @returns A Promise that resolves to a new instance of TigrisVectorStore. */ static fromExistingIndex(embeddings: EmbeddingsInterface, dbConfig: TigrisLibArgs): Promise; }