/**
 * ADR-121 Phase 10 — Maximal Marginal Relevance (MMR) diversity rerank.
 *
 * Plain top-k vector retrieval optimizes for relevance only — when a
 * corpus contains near-duplicate chunks (e.g. multiple versions of the
 * same doc, several paragraphs that paraphrase the same fact), the
 * top-k typically returns redundant variants of one good answer.
 *
 * MMR (Carbonell & Goldstein, 1998) addresses this by iteratively
 * picking the next-best item as a tradeoff between:
 *   - relevance to the query                  (sim(item, query))
 *   - dissimilarity from already-picked items (max sim(item, picked))
 *
 *   score(item) = λ · sim(item, query) − (1 − λ) · max sim(item, picked)
 *
 * λ = 1 → pure relevance (same as plain top-k)
 * λ = 0 → pure diversity (ignores query relevance)
 * Typical production value: λ ≈ 0.5–0.7.
 *
 * This module ships the algorithm as a pure function so callers can
 * compose it however they want — apply to AnnRouter results, to raw
 * search hits, to memory recall, etc. The CLI's
 * `embeddings_search_text_diverse` tool wraps search_text + mmrRerank
 * for one-call diverse RAG.
 */

export interface MmrCandidate {
  /** Caller-supplied identifier. */
  readonly id: string;
  /** Embedding vector. Required — MMR needs it for the diversity term. */
  readonly vector: Float32Array | number[];
  /** Optional relevance score from the prior search step. */
  readonly score?: number;
  /** Free-form payload — preserved through the rerank. */
  readonly payload?: unknown;
}

export interface MmrOptions {
  /** Number of items to return. Clamped to candidates.length. */
  readonly k: number;
  /**
   * Relevance/diversity tradeoff in [0, 1].
   * 1 = pure relevance (same as plain top-k by query-similarity).
   * 0 = pure diversity (ignores query relevance entirely).
   * Default 0.5 — balanced.
   */
  readonly lambda?: number;
}

export interface MmrPickedHit {
  readonly id: string;
  readonly vector: Float32Array;
  readonly payload?: unknown;
  /** Final MMR score at the iteration this item was picked. */
  readonly mmrScore: number;
  /** Original relevance score (query similarity). */
  readonly relevance: number;
  /** Max similarity to already-picked items at pick time (0 for the first pick). */
  readonly redundancy: number;
  /** Order this item was selected — 0 is the highest-relevance pick. */
  readonly pickOrder: number;
}

/**
 * Cosine similarity over two Float32Array views. Defined here rather
 * than imported so this module's dep graph stays at zero.
 */
function cosineSimilarity(a: Float32Array, b: Float32Array): number {
  let dot = 0, na = 0, nb = 0;
  const len = Math.min(a.length, b.length);
  for (let i = 0; i < len; i++) {
    dot += a[i]! * b[i]!;
    na += a[i]! * a[i]!;
    nb += b[i]! * b[i]!;
  }
  if (na === 0 || nb === 0) return 0;
  return dot / (Math.sqrt(na) * Math.sqrt(nb));
}

function toFloat32(v: Float32Array | number[]): Float32Array {
  return v instanceof Float32Array ? v : new Float32Array(v);
}

/**
 * Iterative MMR over a candidate set. Time complexity:
 *   O(k · N · dim) where N = candidates.length, dim = vector length
 *
 * For typical RAG (k=10, N=50, dim=384) that's <200k ops — trivial.
 */
export function mmrRerank(
  candidates: ReadonlyArray<MmrCandidate>,
  queryVector: Float32Array | number[],
  options: MmrOptions,
): MmrPickedHit[] {
  const lambda = Math.max(0, Math.min(1, options.lambda ?? 0.5));
  const k = Math.max(0, Math.min(options.k, candidates.length));
  if (k === 0 || candidates.length === 0) return [];

  const query = toFloat32(queryVector);

  // Pre-compute query similarity for each candidate. The MMR loop
  // only needs candidate-to-picked sim recomputed on each pick.
  const items = candidates.map((c, idx) => {
    const vec = toFloat32(c.vector);
    const relevance = cosineSimilarity(query, vec);
    return { idx, id: c.id, vector: vec, payload: c.payload, relevance };
  });

  const picked: MmrPickedHit[] = [];
  const pickedVectors: Float32Array[] = [];
  const remaining = new Set<number>(items.map((_, i) => i));

  while (picked.length < k && remaining.size > 0) {
    let bestIdx = -1;
    let bestScore = -Infinity;
    let bestRedundancy = 0;

    for (const i of remaining) {
      const item = items[i]!;
      // Max similarity to anything already picked (0 if nothing picked yet).
      let maxSim = 0;
      for (const pv of pickedVectors) {
        const s = cosineSimilarity(item.vector, pv);
        if (s > maxSim) maxSim = s;
      }
      const score = lambda * item.relevance - (1 - lambda) * maxSim;
      if (score > bestScore) {
        bestScore = score;
        bestIdx = i;
        bestRedundancy = maxSim;
      }
    }

    if (bestIdx < 0) break;
    const winner = items[bestIdx]!;
    remaining.delete(bestIdx);
    picked.push({
      id: winner.id,
      vector: winner.vector,
      payload: winner.payload,
      mmrScore: bestScore,
      relevance: winner.relevance,
      redundancy: bestRedundancy,
      pickOrder: picked.length,
    });
    pickedVectors.push(winner.vector);
  }

  return picked;
}

/**
 * Convenience: same as mmrRerank but returns just ids in pick order.
 * Useful when the caller already has the full candidate metadata
 * elsewhere and just needs the diversified ordering.
 */
export function mmrIds(
  candidates: ReadonlyArray<MmrCandidate>,
  queryVector: Float32Array | number[],
  options: MmrOptions,
): string[] {
  return mmrRerank(candidates, queryVector, options).map(p => p.id);
}

/**
 * Diagnostic — measure how much MMR diversified the result vs plain
 * top-k. Returns the **average pairwise similarity** of the picked set;
 * lower = more diverse. Useful for the CI smoke + cost trackers.
 */
export function averagePairwiseSimilarity(picked: ReadonlyArray<MmrPickedHit>): number {
  if (picked.length < 2) return 0;
  let total = 0;
  let pairs = 0;
  for (let i = 0; i < picked.length; i++) {
    for (let j = i + 1; j < picked.length; j++) {
      total += cosineSimilarity(picked[i]!.vector, picked[j]!.vector);
      pairs++;
    }
  }
  return total / pairs;
}