llamaindex/dist/type/ingestion/IngestionPipeline.d.ts

<p align="center"> <img height="100" width="100" alt="LlamaIndex logo" src="https://ts.llamaindex.ai/square.svg" /> </p> <h1 align="center">LlamaIndex.TS</h1> <h3 align="center"> Data framework for your LLM application. </h3>

import type { BaseReader, TransformComponent } from "@llamaindex/core/schema";
import { type BaseNode, type Document, type Metadata } from "@llamaindex/core/schema";
import type { BaseDocumentStore } from "@llamaindex/core/storage/doc-store";
import type { BaseVectorStore, VectorStoreByType } from "@llamaindex/core/vector-store";
import { IngestionCache } from "./IngestionCache.js";
import { DocStoreStrategy } from "./strategies/index.js";
type TransformRunArgs = {
    inPlace?: boolean;
    cache?: IngestionCache;
    docStoreStrategy?: TransformComponent;
};
export declare function runTransformations(nodesToRun: BaseNode[], transformations: TransformComponent[], transformOptions?: any, { inPlace, cache, docStoreStrategy }?: TransformRunArgs): Promise<BaseNode[]>;
export declare class IngestionPipeline {
    transformations: TransformComponent[];
    documents?: Document[] | undefined;
    reader?: BaseReader | undefined;
    vectorStore?: BaseVectorStore | undefined;
    vectorStores?: VectorStoreByType | undefined;
    docStore?: BaseDocumentStore;
    docStoreStrategy: DocStoreStrategy;
    cache?: IngestionCache | undefined;
    disableCache: boolean;
    private _docStoreStrategy?;
    constructor(init?: Partial<IngestionPipeline>);
    prepareInput(documents?: Document[], nodes?: BaseNode[]): Promise<BaseNode[]>;
    run(args?: any, transformOptions?: any): Promise<BaseNode[]>;
}
export declare function addNodesToVectorStores(nodes: BaseNode<Metadata>[], vectorStores: VectorStoreByType, nodesAdded?: (newIds: string[], nodes: BaseNode<Metadata>[], vectorStore: BaseVectorStore) => Promise<void>): Promise<void>;
export {};