import type { RagStore, RagStoreConfig } from "./types.js";
/**
 * Creates a persistent RAG store with lazy embedding and similarity search.
 *
 * Combines document ingestion, chunking, embedding, and vector search into
 * a single factory. Documents are chunked on ingest; embeddings are generated
 * lazily on the first search call to avoid blocking uploads on AI API calls.
 *
 * By default, this uses the local JSON store. When Veryfront Cloud bootstrap
 * is present, it automatically upgrades to the cloud-backed store unless
 * explicitly overridden.
 *
 * @example
 * ```ts
 * import { ragStore } from "veryfront/embedding";
 *
 * const store = ragStore({
 *   storagePath: "data/index.json",
 *   contentDir: "content",
 * });
 *
 * await store.ingest("My Doc", text, { source: "upload:file.pdf", type: "pdf" });
 * const results = await store.search("query", { topK: 5, threshold: 0.7 });
 * ```
 */
export declare function ragStore(config: RagStoreConfig): RagStore;
//# sourceMappingURL=rag-store.d.ts.map