@astermind/astermind-pro/dist/reranking/OmegaRR.d.ts

Astermind Pro - Premium ML Toolkit with Advanced RAG, Reranking, Summarization, and Information Flow Analysis

export type Chunk = {
    heading: string;
    content: string;
    rich?: string;
    level?: number;
    secId?: number;
    score_base?: number;
};
export type ScoredChunk = Chunk & {
    score_rr: number;
    p_relevant: number;
    /** Engineered feature vector used by the ridge reranker (if exposeFeatures=true) */
    _features?: number[];
    /** Names for _features; same array for all rows (if attachFeatureNames=true) */
    _feature_names?: string[];
};
export type RerankOptions = {
    lambdaRidge?: number;
    useMMR?: boolean;
    mmrLambda?: number;
    probThresh?: number;
    epsilonTop?: number;
    budgetChars?: number;
    randomProjDim?: number;
    /** NEW: attach _features to outputs (default true) */
    exposeFeatures?: boolean;
    /** NEW: also attach _feature_names (default false) */
    attachFeatureNames?: boolean;
};
/** Train per-query ridge model and score chunks. */
export declare function rerank(query: string, chunks: Chunk[], opts?: RerankOptions): ScoredChunk[];
/** Filter scored chunks using probability/near-top thresholds and MMR coverage. */
export declare function filterMMR(scored: ScoredChunk[], opts?: RerankOptions): ScoredChunk[];
/** Convenience: run rerank then filter. */
export declare function rerankAndFilter(query: string, chunks: Chunk[], opts?: RerankOptions): ScoredChunk[];
export declare function explainFeatures(query: string, chunks: Chunk[], opts?: {
    randomProjDim?: number;
}): {
    names: string[];
    rows: {
        heading: string;
        features: number[];
    }[];
};
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