import type { VectorStore, VectorStoreConfig } from "./types.js";
/**
 * Creates an in-memory vector store with integrated embedding and similarity search.
 *
 * Supports three search strategies:
 * - `"dense"` (default) — cosine similarity ranking
 * - `"mmr"` — Maximum Marginal Relevance for diverse results
 * - `"hybrid"` — BM25 + dense fusion via Reciprocal Rank Fusion
 *
 * @example
 * ```ts
 * const store = vectorStore({ embedder });
 * await store.add(["chunk 1", "chunk 2"]);
 *
 * // Basic search
 * const results = await store.search("query", { topK: 5 });
 *
 * // With threshold and metadata filtering
 * const filtered = await store.search("query", {
 *   topK: 10,
 *   threshold: 0.7,
 *   filter: { source: "upload" },
 * });
 *
 * // MMR for diverse results
 * const diverse = await store.search("query", { strategy: "mmr", lambda: 0.5 });
 *
 * // Hybrid search (BM25 + dense, fused with RRF)
 * const hybrid = await store.search("query", { strategy: "hybrid" });
 * ```
 */
export declare function vectorStore(config: VectorStoreConfig): VectorStore;
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