import { ModelContract } from "../../contracts/model.contract.mjs";
import { RagReranker } from "./reranker.contract.mjs";

//#region ../ai/src/rag/rerank/llm-reranker.d.ts
/** Options for the {@link llmReranker}. */
type LlmRerankerOptions = {
  /** The model used to score candidate relevance. Required. */model: ModelContract;
  /**
   * How many candidates to score per model call. Larger batches mean
   * fewer round-trips but a longer prompt. Default `10`.
   */
  batchSize?: number;
};
/**
 * Optional model-backed reranker.
 *
 * Asks an LLM to grade each over-fetched candidate's relevance to the
 * query on a `0..1` scale, then sorts descending by the model's score.
 * Candidates the model does not score keep their original cosine score, so
 * a partial/garbled reply degrades gracefully rather than dropping hits.
 * Scoring is batched (`batchSize`) to bound prompt length.
 *
 * Unlike {@link keywordReranker}, this costs one or more model calls per
 * retrieval — opt in only when precision matters more than latency/cost.
 *
 * @example
 * const kb = ai.rag({
 *   embedder,
 *   store,
 *   reranker: ai.rag.llmReranker({ model: openai.model({ name: "gpt-4o-mini" }) }),
 * });
 */
declare function llmReranker(options: LlmRerankerOptions): RagReranker;
//#endregion
export { LlmRerankerOptions, llmReranker };
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