import { VectorStore } from '@langchain/core/vectorstores';
import { Embeddings } from '@langchain/core/embeddings';
import { Document } from '@langchain/core/documents';
interface DatabricksVectorStoreConfig {
    workspaceUrl: string;
    token: string;
    indexName: string;
    textColumn: string;
    metadataColumns: string[];
    scoreThreshold?: number;
}
export declare class DatabricksVectorStoreLangChain extends VectorStore {
    private config;
    _vectorstoreType(): string;
    constructor(embeddings: Embeddings, config: DatabricksVectorStoreConfig);
    private makeRequest;
    static fromDocuments(docs: Document[], embeddings: Embeddings, config: DatabricksVectorStoreConfig): Promise<DatabricksVectorStoreLangChain>;
    static fromExistingIndex(embeddings: Embeddings, config: DatabricksVectorStoreConfig): Promise<DatabricksVectorStoreLangChain>;
    addDocuments(documents: Document[]): Promise<void>;
    addVectors(vectors: number[][], documents: Document[]): Promise<void>;
    delete(params: {
        ids: string[];
    }): Promise<void>;
    similaritySearchVectorWithScore(query: number[], k: number, filterJson?: string, queryType?: 'ANN' | 'HYBRID', extraColumns?: string[], scoreThreshold?: number): Promise<[Document, number][]>;
}
export {};