fix(recall): keep only matched atoms and filter evidence by focus entities
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@@ -53,6 +53,8 @@ const TOP_N_STAR = 5;
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// L0 显示文本:分号拼接 vs 多行模式的阈值
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const L0_JOINED_MAX_LENGTH = 120;
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// 背景证据实体过滤旁通阈值(与事件过滤策略一致)
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const EVIDENCE_ENTITY_BYPASS_SIM = 0.80;
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// ─────────────────────────────────────────────────────────────────────────────
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// 工具函数
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@@ -123,6 +125,63 @@ function normalize(s) {
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.toLowerCase();
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}
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/**
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* 收集 L0 的实体集合(用于背景证据实体过滤)
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* 支持新结构 who/edges,也兼容旧结构 subject/object。
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* @param {object} l0
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* @returns {Set<string>}
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*/
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function collectL0Entities(l0) {
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const atom = l0?.atom || l0?._atom || {};
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const set = new Set();
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const add = (v) => {
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const n = normalize(v);
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if (n) set.add(n);
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};
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for (const w of (atom.who || [])) add(w);
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for (const e of (atom.edges || [])) {
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add(e?.s);
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add(e?.t);
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}
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// 兼容旧数据
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add(atom.subject);
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add(atom.object);
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return set;
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}
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/**
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* 背景证据是否保留(按焦点实体过滤)
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* 规则:
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* 1) 无焦点实体:保留
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* 2) similarity >= 0.80:保留(旁通)
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* 3) who/edges 命中焦点实体:保留
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* 4) 兼容旧数据:semantic 文本包含焦点实体:保留
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* 否则过滤。
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* @param {object} l0
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* @param {Set<string>} focusSet
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* @returns {boolean}
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*/
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function shouldKeepEvidenceL0(l0, focusSet) {
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if (!focusSet?.size) return true;
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if ((l0?.similarity || 0) >= EVIDENCE_ENTITY_BYPASS_SIM) return true;
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const entities = collectL0Entities(l0);
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for (const f of focusSet) {
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if (entities.has(f)) return true;
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}
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// 旧数据兜底:从 semantic 文本里做包含匹配
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const textNorm = normalize(l0?.atom?.semantic || l0?.text || '');
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for (const f of focusSet) {
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if (f && textNorm.includes(f)) return true;
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}
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return false;
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}
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/**
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* 获取事件排序键
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* @param {object} event - 事件对象
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@@ -894,8 +953,11 @@ async function buildVectorPrompt(store, recallResult, causalById, focusEntities,
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const keepVisible = store.keepVisibleCount ?? 3;
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// 收集未被事件消费的 L0,按 rerankScore 降序
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const focusSetForEvidence = new Set((focusEntities || []).map(normalize).filter(Boolean));
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const remainingL0 = l0Selected
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.filter(l0 => !usedL0Ids.has(l0.id))
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.filter(l0 => shouldKeepEvidenceL0(l0, focusSetForEvidence))
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.sort((a, b) => (b.rerankScore || 0) - (a.rerankScore || 0));
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// 远期:floor <= lastSummarized
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