//#region ../ai/src/rag/store/vector-store.contract.d.ts
/**
 * The three vector operations the RAG pipeline needs, expressed as a thin
 * structural narrowing of the `@warlock.js/cache` `CacheDriver` surface
 * (`set({ vector })` + `similar()` + `removeNamespace()`).
 *
 * This is NOT a new storage engine — v1 has exactly one implementation,
 * {@link cacheVectorStore}, which adapts any `CacheDriver`. The contract
 * exists so a future non-cache backend can be swapped in without touching
 * the pipeline. Cache stays embedding-agnostic; the RAG vocabulary
 * (`upsert` / `query`) lives here, in the rag feature.
 */
interface VectorStore {
  /**
   * Index a value under `key` with its embedding vector. Optional `tags`
   * are stored alongside the entry so `query({ tags })` can restrict the
   * candidate set to a subset of sources.
   */
  upsert(key: string, value: unknown, vector: number[], tags?: string[]): Promise<void>;
  /**
   * Cosine-nearest entries to `vector` clearing `threshold`, capped at
   * `topK`, optionally restricted to entries carrying one of `tags`.
   */
  query<T>(vector: number[], options: {
    topK: number;
    threshold?: number;
    tags?: string[];
  }): Promise<{
    key: string;
    value: T;
    score: number;
  }[]>;
  /** Drop every entry written under `namespace`. */
  removeNamespace(namespace: string): Promise<void>;
}
//#endregion
export { VectorStore };
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