{"version":3,"file":"mmr.cjs","sources":["../../../src/memory/mmr.ts"],"sourcesContent":["/**\n * Maximal Marginal Relevance (MMR) re-ranking — Phase 2.\n *\n * Ported from upstream `extensions/memory-core/src/memory/mmr.ts` with\n * minor adaptation for our `MemoryEntry` shape (content vs snippet, id vs\n * path+startLine). Behavior is identical: normalize scores, iteratively\n * pick the item that maximizes `λ * relevance - (1-λ) * max_similarity`\n * using Jaccard on tokenized content.\n *\n * @see Carbonell & Goldstein, \"The Use of MMR, Diversity-Based Reranking\" (1998)\n */\n\nexport interface MMRConfig {\n /** Opt-in. Upstream default is false. */\n enabled: boolean;\n /** 0 = max diversity, 1 = max relevance. Upstream default 0.7. */\n lambda: number;\n}\n\nexport const DEFAULT_MMR_CONFIG: MMRConfig = {\n enabled: false,\n lambda: 0.7,\n};\n\n/**\n * CJK Unified Ideographs + Extension A, Hiragana/Katakana, Hangul.\n * These lack whitespace boundaries so we must tokenize them differently.\n */\nconst CJK_RE =\n /[\\u3040-\\u309f\\u30a0-\\u30ff\\u3400-\\u4dbf\\u4e00-\\u9fff\\uac00-\\ud7af\\u1100-\\u11ff]/;\n\n/**\n * Tokenize content into a set for Jaccard similarity.\n *\n * ASCII: alphanumeric + underscore runs, lowercased.\n * CJK: each char becomes a unigram; consecutive pairs become a bigram.\n * Non-adjacent CJK chars (e.g. `我a好`) do NOT form a bigram.\n */\nexport function tokenize(text: string): Set {\n const lower = (text ?? '').toLowerCase();\n const ascii = lower.match(/[a-z0-9_]+/g) ?? [];\n\n const chars = Array.from(lower);\n const cjkData: Array<{ char: string; index: number }> = [];\n for (let i = 0; i < chars.length; i++) {\n if (CJK_RE.test(chars[i])) cjkData.push({ char: chars[i], index: i });\n }\n\n const bigrams: string[] = [];\n for (let i = 0; i < cjkData.length - 1; i++) {\n if (cjkData[i + 1].index === cjkData[i].index + 1) {\n bigrams.push(cjkData[i].char + cjkData[i + 1].char);\n }\n }\n\n return new Set([...ascii, ...bigrams, ...cjkData.map((d) => d.char)]);\n}\n\nexport function jaccardSimilarity(a: Set, b: Set): number {\n if (a.size === 0 && b.size === 0) return 1;\n if (a.size === 0 || b.size === 0) return 0;\n\n const [smaller, larger] = a.size <= b.size ? [a, b] : [b, a];\n let intersection = 0;\n for (const t of smaller) if (larger.has(t)) intersection++;\n const union = a.size + b.size - intersection;\n return union === 0 ? 0 : intersection / union;\n}\n\nexport function textSimilarity(a: string, b: string): number {\n return jaccardSimilarity(tokenize(a), tokenize(b));\n}\n\nexport function computeMMRScore(\n relevance: number,\n maxSimilarity: number,\n lambda: number\n): number {\n return lambda * relevance - (1 - lambda) * maxSimilarity;\n}\n\nexport interface MMRItem {\n id: string;\n score: number;\n content: string;\n}\n\n/**\n * Re-rank items using MMR. Returns a new array in MMR order.\n */\nexport function mmrRerank(\n items: T[],\n config: Partial = {}\n): T[] {\n const enabled = config.enabled ?? DEFAULT_MMR_CONFIG.enabled;\n const rawLambda = config.lambda ?? DEFAULT_MMR_CONFIG.lambda;\n\n if (!enabled || items.length <= 1) return [...items];\n\n const lambda = Math.max(0, Math.min(1, rawLambda));\n if (lambda === 1) return [...items].sort((a, b) => b.score - a.score);\n\n const tokenCache = new Map>();\n for (const item of items) tokenCache.set(item.id, tokenize(item.content));\n\n const scores = items.map((i) => i.score);\n const maxScore = Math.max(...scores);\n const minScore = Math.min(...scores);\n const range = maxScore - minScore;\n const normalize = (s: number): number =>\n range === 0 ? 1 : (s - minScore) / range;\n\n const selected: T[] = [];\n const remaining = new Set(items);\n\n while (remaining.size > 0) {\n let best: T | null = null;\n let bestMMR = -Infinity;\n for (const cand of remaining) {\n const rel = normalize(cand.score);\n const candTokens = tokenCache.get(cand.id)!;\n let maxSim = 0;\n for (const sel of selected) {\n const sim = jaccardSimilarity(candTokens, tokenCache.get(sel.id)!);\n if (sim > maxSim) maxSim = sim;\n }\n const mmr = computeMMRScore(rel, maxSim, lambda);\n if (\n mmr > bestMMR ||\n (mmr === bestMMR && cand.score > (best?.score ?? -Infinity))\n ) {\n bestMMR = mmr;\n best = cand;\n }\n }\n if (!best) break;\n selected.push(best);\n remaining.delete(best);\n }\n\n return selected;\n}\n\n/**\n * Adapter: apply MMR to an array of MemoryEntry-shaped hits.\n *\n * Uses (path|id|index) as the stable ID so two hits from the same file at\n * different content still get distinct MMR identities.\n */\nexport function applyMMRToMemoryHits<\n T extends { id: string; 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