import { MilvusClient } from "@zilliz/milvus2-sdk-node"; import { Embeddings } from "../embeddings/base.js"; import { VectorStore } from "./base.js"; import { Document } from "../document.js"; export interface MilvusLibArgs { collectionName?: string; primaryField?: string; vectorField?: string; textField?: string; url?: string; ssl?: boolean; username?: string; password?: string; } type IndexType = "IVF_FLAT" | "IVF_SQ8" | "IVF_PQ" | "HNSW" | "RHNSW_FLAT" | "RHNSW_SQ" | "RHNSW_PQ" | "IVF_HNSW" | "ANNOY"; interface IndexParam { params: { nprobe?: number; ef?: number; search_k?: number; }; } export declare class Milvus extends VectorStore { get lc_secrets(): { [key: string]: string; }; collectionName: string; numDimensions?: number; autoId?: boolean; primaryField: string; vectorField: string; textField: string; fields: string[]; client: MilvusClient; indexParams: Record; indexCreateParams: { index_type: string; metric_type: string; params: string; }; indexSearchParams: string; _vectorstoreType(): string; constructor(embeddings: Embeddings, args: MilvusLibArgs); addDocuments(documents: Document[]): Promise; addVectors(vectors: number[][], documents: Document[]): Promise; similaritySearchVectorWithScore(query: number[], k: number): Promise<[Document, number][]>; ensureCollection(vectors?: number[][], documents?: Document[]): Promise; createCollection(vectors: number[][], documents: Document[]): Promise; grabCollectionFields(): Promise; static fromTexts(texts: string[], metadatas: object[] | object, embeddings: Embeddings, dbConfig?: MilvusLibArgs): Promise; static fromDocuments(docs: Document[], embeddings: Embeddings, dbConfig?: MilvusLibArgs): Promise; static fromExistingCollection(embeddings: Embeddings, dbConfig: MilvusLibArgs): Promise; } export {};