/**
 * Discover facets — curated feature→capability fusion layer (#1623).
 *
 * The capability index (hybrid BM25 + dense) is near-optimal for a ~400-item
 * corpus, but bare domain words ("persona", "expansion") and feature-intent
 * phrases ("author an expansion", "scaffold a project") have weak lexical
 * scent: generic artifacts that merely contain the word out-rank the artifacts
 * that actually OWN the feature. This module adds a small, curated set of
 * **facets** that map feature intents to their owning capabilities and fuses
 * that signal into the single-pass `aiwg discover` ranking (reciprocal-rank
 * spirit: a second ranker combined by max-floor + injection).
 *
 * This is the *single-pass fused* form the ADR ships for #1623. The parallel
 * multi-vector fan-out over these same facets is the deferred #1626 work — the
 * facet table here is the shared contract both consume, so #1626 reuses
 * `DISCOVER_FACETS` rather than redefining domains.
 *
 * Design notes:
 *  - Facets are *curated*, not learned — appropriate at this corpus size and
 *    portable across all 10 providers (no embedding store, no fine-tuned
 *    router). See `.aiwg/architecture/adr-steward-feature-discoverability.md`.
 *  - Mapped capabilities are matched against an entry's canonical `name`
 *    (falling back to the path basename / parent-dir slug), so the table lists
 *    names — not paths — and survives bundle moves.
 *  - Activation strength tiers the score floor so an exact domain-word query
 *    ("persona") sorts the owning domain to the very top (above generic
 *    capped-at-1.0 substring matches, mirroring the exact-name 1.001 floor in
 *    query-engine), while looser token-overlap activations apply a gentler
 *    floor that lifts the domain into the top-K without dominating.
 *
 * @implements #1623 (U3)
 */
import type { MetadataEntry } from './types.js';
export type FacetKind = 'feature-domain' | 'persona-identity' | 'authoring-surface' | 'provider-capability';
export interface FacetEntry {
    /** Which facet this entry belongs to (for fan-out weighting in #1626). */
    facet: FacetKind;
    /** Human label for diagnostics / `aiwg index status` surfacing. */
    label: string;
    /**
     * Lowercase intent phrases. A query activates this entry when it exactly
     * equals an intent, contains one (or is contained by one), or — for
     * multi-word intents — overlaps a majority of the intent's tokens.
     */
    intents: string[];
    /**
     * Canonical capability names this intent maps to (matched against
     * `entry.name`, then path basename / parent-dir slug).
     */
    capabilities: string[];
}
/**
 * The curated facet table. Kept deliberately small and legible — it is a
 * routing index, not a knowledge base. Guarded by the discover acceptance
 * test (canonical phrases must rank their targets top-3) and the
 * metadata-completeness lint (empty triggers cannot recur).
 */
export declare const DISCOVER_FACETS: FacetEntry[];
/**
 * Per-facet (and base-ranker) weights for the RRF fusion (#1626). Tunable so
 * operators can bias the fan-out — e.g. dial down provider-capability when a
 * project never asks provider questions. Defaults preserve the #1623 ranking.
 */
export interface FacetWeights {
    /** Weight of the base lexical ranker in the fusion. */
    base: number;
    /** Per-facet-kind weights (missing kinds default to 1.0). */
    facets: Partial<Record<FacetKind, number>>;
}
export declare const DEFAULT_FACET_WEIGHTS: FacetWeights;
/**
 * Multi-vector facet fan-out + reciprocal-rank fusion (#1623 single-pass →
 * #1626 parallel fan-out). Fans out across the curated facet vectors in
 * parallel, fuses them with the base lexical ranker via RRF, and lifts each
 * facet-matched capability to its activation floor so it out-ranks generic
 * artifacts that merely mention the domain word.
 *
 * The fused result is a SUPERSET of the #1623 single-pass behavior (ADR
 * contract): facet-matched entries are guaranteed a place at no less than the
 * activation floor (injected if the lexical pass missed them), base-only
 * entries keep their lexical score, and RRF consensus orders entries within a
 * floor tier (so multi-facet / more-relevant capabilities sort first) without
 * changing the emitted 2-decimal score. Per-facet weighting is configurable.
 *
 * The returned list is re-sorted but NOT truncated — the caller applies limit.
 * Pure/deterministic: no IO, no mutation of inputs.
 */
export declare function applyFacetFusion(scored: Array<{
    entry: MetadataEntry;
    score: number;
}>, candidates: MetadataEntry[], phrase: string, weights?: FacetWeights): Promise<Array<{
    entry: MetadataEntry;
    score: number;
}>>;
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