Refine diffusion graph channels and drop legacy who compatibility
This commit is contained in:
@@ -128,12 +128,12 @@ function normalize(s) {
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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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* 使用 edges.s/edges.t。
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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 atom = l0?.atom || {};
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const set = new Set();
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const add = (v) => {
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@@ -141,16 +141,11 @@ function collectL0Entities(l0) {
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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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@@ -159,8 +154,7 @@ function collectL0Entities(l0) {
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* 规则：
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* 1) 无焦点实体：保留
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* 2) similarity >= 0.70：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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* 3) edges 命中焦点实体：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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@@ -175,11 +169,6 @@ function shouldKeepEvidenceL0(l0, 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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@@ -3,8 +3,6 @@
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//
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// 设计依据：
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// - BGE-M3 (BAAI, 2024): 自然语言段落检索精度最高 → semantic = 纯自然语言
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// - Interpersonal Circumplex (Kiesler, 1983): 权力轴+情感轴 → dynamics 枚举
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// - Labov Narrative Structure (1972): 叙事功能轴 → dynamics 枚举补充
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// - TransE (Bordes, 2013): s/t/r 三元组方向性 → edges 格式
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//
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// 每楼层 1-2 个场景锚点（非碎片原子），60-100 字场景摘要
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@@ -32,26 +30,6 @@ export function isBatchCancelled() {
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return batchCancelled;
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}
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// ============================================================================
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// dynamics 封闭枚举（8 个标签，两轴四象限 + 叙事轴）
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// ============================================================================
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const VALID_DYNAMICS = new Set([
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// 权力轴 (Interpersonal Circumplex: Dominance-Submission)
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'支配', // 控制、命令、审视、威慑、主导
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'让渡', // 顺从、服从、屈服、被动、配合
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// 情感轴 (Interpersonal Circumplex: Hostility-Friendliness)
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'亲密', // 温柔、关怀、依赖、信任、连接
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'敌意', // 对抗、拒绝、攻击、嘲讽、排斥
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// 叙事轴 (Labov Narrative Structure)
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'揭示', // 真相、发现、告白、暴露、秘密
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'决意', // 选择、承诺、放弃、宣言、转折
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'张力', // 悬念、对峙、暗涌、不安、等待
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'丧失', // 分离、死亡、破碎、遗憾、崩塌
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]);
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// ============================================================================
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// L0 提取 Prompt
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// ============================================================================
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@@ -68,9 +46,7 @@ const SYSTEM_PROMPT = `你是场景摘要器。从一轮对话中提取1-2个场
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{"anchors":[
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{
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"scene": "60-100字完整场景描述",
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"who": ["角色名1","角色名2"],
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"edges": [{"s":"施事方","t":"受事方","r":"互动行为"}],
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"dynamics": ["标签"],
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"where": "地点"
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}
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]}
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@@ -81,20 +57,12 @@ const SYSTEM_PROMPT = `你是场景摘要器。从一轮对话中提取1-2个场
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- 读者只看 scene 就能复原这一幕
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- 60-100字，信息密集但流畅
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## who
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- 参与互动的角色正式名称，不用代词或别称
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- 只从正文内容中识别角色名，不要把标签名（如 user、assistant）当作角色
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## edges（关系三元组）
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- s=施事方 t=受事方 r=互动行为（10-15字）
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- s/t 必须是参与互动的角色正式名称，不用代词或别称
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- 只从正文内容中识别角色名，不要把标签名（如 user、assistant）当作角色
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- 每个锚点 1-3 条
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## dynamics（封闭枚举，选0-2个）
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权力轴：支配（控制/命令/审视） | 让渡（顺从/服从/屈服）
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情感轴：亲密（温柔/信任/连接） | 敌意（对抗/拒绝/攻击）
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叙事轴：揭示（真相/秘密） | 决意（选择/承诺） | 张力（对峙/不安） | 丧失（分离/破碎）
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纯日常无明显模式时 dynamics 为 []
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## where
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- 场景地点，无明确地点时空字符串
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@@ -107,7 +75,7 @@ const SYSTEM_PROMPT = `你是场景摘要器。从一轮对话中提取1-2个场
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## 示例
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输入：艾拉在火山口举起圣剑刺穿古龙心脏，龙血溅满她的铠甲，她跪倒在地痛哭
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输出：
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{"anchors":[{"scene":"火山口上艾拉举起圣剑刺穿古龙的心脏，龙血溅满铠甲，古龙轰然倒地，艾拉跪倒在滚烫的岩石上痛哭，完成了她不得不做的弑杀","who":["艾拉","古龙"],"edges":[{"s":"艾拉","t":"古龙","r":"以圣剑刺穿心脏"}],"dynamics":["决意","丧失"],"where":"火山口"}]}`;
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{"anchors":[{"scene":"火山口上艾拉举起圣剑刺穿古龙的心脏，龙血溅满铠甲，古龙轰然倒地，艾拉跪倒在滚烫的岩石上痛哭，完成了她不得不做的弑杀","edges":[{"s":"艾拉","t":"古龙","r":"以圣剑刺穿心脏"}],"where":"火山口"}]}`;
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const JSON_PREFILL = '{"anchors":[';
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@@ -121,19 +89,6 @@ const sleep = (ms) => new Promise(r => setTimeout(r, ms));
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// 清洗与构建
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// ============================================================================
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/**
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* 清洗 dynamics 标签，只保留合法枚举值
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* @param {string[]} raw
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* @returns {string[]}
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*/
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function sanitizeDynamics(raw) {
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if (!Array.isArray(raw)) return [];
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return raw
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.map(d => String(d || '').trim())
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.filter(d => VALID_DYNAMICS.has(d))
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.slice(0, 2);
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}
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/**
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* 清洗 edges 三元组
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* @param {object[]} raw
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@@ -152,24 +107,6 @@ function sanitizeEdges(raw) {
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.slice(0, 3);
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}
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/**
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* 清洗 who 列表
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* @param {string[]} raw
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* @returns {string[]}
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*/
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function sanitizeWho(raw) {
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if (!Array.isArray(raw)) return [];
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const seen = new Set();
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return raw
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.map(w => String(w || '').trim())
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.filter(w => {
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if (!w || w.length < 1 || seen.has(w)) return false;
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seen.add(w);
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return true;
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})
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.slice(0, 6);
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}
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/**
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* 将解析后的 anchor 转换为 atom 存储对象
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*
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@@ -186,10 +123,7 @@ function anchorToAtom(anchor, aiFloor, idx) {
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// scene 过短（< 15 字）可能是噪音
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if (scene.length < 15) return null;
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const who = sanitizeWho(anchor.who);
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const edges = sanitizeEdges(anchor.edges);
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const dynamics = sanitizeDynamics(anchor.dynamics);
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const where = String(anchor.where || '').trim();
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return {
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@@ -201,9 +135,7 @@ function anchorToAtom(anchor, aiFloor, idx) {
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semantic: scene,
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// ═══ 图结构层（扩散的 key） ═══
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who,
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edges,
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dynamics,
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where,
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};
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}
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@@ -268,7 +200,7 @@ async function extractAtomsForRoundWithRetry(userMessage, aiMessage, aiFloor, op
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}
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// 兼容：优先 anchors，回退 atoms
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const rawAnchors = parsed?.anchors || parsed?.atoms;
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const rawAnchors = parsed?.anchors;
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if (!rawAnchors || !Array.isArray(rawAnchors)) {
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if (attempt < RETRY_COUNT) {
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await sleep(RETRY_DELAY);
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@@ -395,3 +327,4 @@ export async function batchExtractAtoms(chat, onProgress) {
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return allAtoms;
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}
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@@ -32,6 +32,7 @@
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import { xbLog } from '../../../../core/debug-core.js';
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import { getContext } from '../../../../../../../extensions.js';
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import { tokenizeForIndex } from '../utils/tokenizer.js';
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const MODULE_ID = 'diffusion';
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@@ -46,12 +47,11 @@ const CONFIG = {
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MAX_ITER: 50, // hard iteration cap (typically converges in 15-25)
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// Edge weight channel coefficients
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// Rationale: Rimmon-Kenan (2002) hierarchy: characters > events > setting > themes
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// No standalone WHO channel: rely on interaction/action/location only.
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GAMMA: {
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who: 0.50, // entity co-occurrence — Jaccard
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what: 0.25, // directed pair overlap — Szymkiewicz-Simpson
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what: 0.55, // interaction pair overlap — Szymkiewicz-Simpson
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where: 0.15, // location exact match — binary
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how: 0.10, // dynamics tag co-occurrence — Jaccard
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how: 0.30, // action-term co-occurrence — Jaccard
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},
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// Post-verification (Cosine Gate)
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@@ -94,17 +94,13 @@ function cosineSimilarity(a, b) {
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// ═══════════════════════════════════════════════════════════════════════════
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/**
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* WHO channel: entity set = who ∪ edges.s ∪ edges.t
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* Endpoint entity set from edges.s/edges.t (used for candidate pair generation).
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* @param {object} atom
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* @param {Set<string>} excludeEntities - entities to exclude (e.g. name1)
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* @returns {Set<string>}
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*/
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function extractEntities(atom, excludeEntities = new Set()) {
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const set = new Set();
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for (const w of (atom.who || [])) {
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const n = normalize(w);
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if (n && !excludeEntities.has(n)) set.add(n);
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}
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for (const e of (atom.edges || [])) {
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const s = normalize(e?.s);
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const t = normalize(e?.t);
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@@ -115,18 +111,19 @@ function extractEntities(atom, excludeEntities = new Set()) {
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}
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/**
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* WHAT channel: directed interaction pairs "A→B" (strict direction — option A)
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* WHAT channel: interaction pairs "A↔B" (direction-insensitive).
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* @param {object} atom
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* @param {Set<string>} excludeEntities
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* @returns {Set<string>}
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*/
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function extractDirectedPairs(atom, excludeEntities = new Set()) {
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function extractInteractionPairs(atom, excludeEntities = new Set()) {
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const set = new Set();
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for (const e of (atom.edges || [])) {
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const s = normalize(e?.s);
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const t = normalize(e?.t);
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if (s && t && !excludeEntities.has(s) && !excludeEntities.has(t)) {
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set.add(`${s}\u2192${t}`);
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const pair = [s, t].sort().join('\u2194');
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set.add(pair);
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}
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}
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return set;
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@@ -142,15 +139,20 @@ function extractLocation(atom) {
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}
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/**
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* HOW channel: dynamics tags set
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* HOW channel: action terms from edges.r
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* @param {object} atom
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* @param {Set<string>} excludeEntities
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* @returns {Set<string>}
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*/
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function extractDynamics(atom) {
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function extractActionTerms(atom, excludeEntities = new Set()) {
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const set = new Set();
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for (const d of (atom.dynamics || [])) {
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const n = normalize(d);
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if (n) set.add(n);
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for (const e of (atom.edges || [])) {
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const rel = String(e?.r || '').trim();
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if (!rel) continue;
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for (const token of tokenizeForIndex(rel)) {
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const t = normalize(token);
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if (t && !excludeEntities.has(t)) set.add(t);
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}
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}
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return set;
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}
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@@ -198,8 +200,8 @@ function overlapCoefficient(a, b) {
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// Graph construction
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//
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// Candidate pairs discovered via inverted indices on entities and locations.
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// Dynamics-only pairs excluded from candidate generation (γ_HOW = 0.10 is
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// too weak to justify O(N²) blowup from 8-tag combinatorics).
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// HOW-only pairs are still excluded from candidate generation to avoid O(N²);
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// all channel weights are evaluated for the entity/location candidate set.
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// All four channels evaluated for every candidate pair.
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// ═══════════════════════════════════════════════════════════════════════════
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@@ -207,14 +209,14 @@ function overlapCoefficient(a, b) {
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* Pre-extract features for all atoms
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* @param {object[]} allAtoms
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* @param {Set<string>} excludeEntities
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* @returns {object[]} feature objects with entities/directedPairs/location/dynamics
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* @returns {object[]} feature objects with entities/interactionPairs/location/actionTerms
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*/
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function extractAllFeatures(allAtoms, excludeEntities = new Set()) {
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return allAtoms.map(atom => ({
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entities: extractEntities(atom, excludeEntities),
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directedPairs: extractDirectedPairs(atom, excludeEntities),
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interactionPairs: extractInteractionPairs(atom, excludeEntities),
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location: extractLocation(atom),
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dynamics: extractDynamics(atom),
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actionTerms: extractActionTerms(atom, excludeEntities),
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}));
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}
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@@ -279,10 +281,10 @@ function buildGraph(allAtoms, excludeEntities = new Set()) {
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collectPairsFromIndex(entityIndex, pairSet, N);
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collectPairsFromIndex(locationIndex, pairSet, N);
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// Compute four-channel edge weights for all candidates
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// Compute three-channel edge weights for all candidates
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const neighbors = Array.from({ length: N }, () => []);
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let edgeCount = 0;
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const channelStats = { who: 0, what: 0, where: 0, how: 0 };
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const channelStats = { what: 0, where: 0, how: 0 };
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for (const packed of pairSet) {
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const i = Math.floor(packed / N);
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@@ -291,13 +293,11 @@ function buildGraph(allAtoms, excludeEntities = new Set()) {
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const fi = features[i];
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const fj = features[j];
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const wWho = jaccard(fi.entities, fj.entities);
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const wWhat = overlapCoefficient(fi.directedPairs, fj.directedPairs);
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const wWhat = overlapCoefficient(fi.interactionPairs, fj.interactionPairs);
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const wWhere = (fi.location && fi.location === fj.location) ? 1.0 : 0.0;
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const wHow = jaccard(fi.dynamics, fj.dynamics);
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const wHow = jaccard(fi.actionTerms, fj.actionTerms);
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const weight =
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CONFIG.GAMMA.who * wWho +
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CONFIG.GAMMA.what * wWhat +
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CONFIG.GAMMA.where * wWhere +
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CONFIG.GAMMA.how * wHow;
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@@ -307,7 +307,6 @@ function buildGraph(allAtoms, excludeEntities = new Set()) {
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neighbors[j].push({ target: i, weight });
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edgeCount++;
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if (wWho > 0) channelStats.who++;
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if (wWhat > 0) channelStats.what++;
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if (wWhere > 0) channelStats.where++;
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if (wHow > 0) channelStats.how++;
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@@ -318,8 +317,7 @@ function buildGraph(allAtoms, excludeEntities = new Set()) {
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xbLog.info(MODULE_ID,
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`Graph: ${N} nodes, ${edgeCount} edges ` +
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`(who=${channelStats.who} what=${channelStats.what} ` +
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`where=${channelStats.where} how=${channelStats.how}) ` +
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`(what=${channelStats.what} where=${channelStats.where} how=${channelStats.how}) ` +
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`(${buildTime}ms)`
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);
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@@ -593,7 +591,7 @@ function postVerify(pi, atomIds, atomById, seedAtomIds, vectorMap, queryVector)
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* @param {object[]} seeds - l0Selected from recall Stage 6
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* Each: { atomId, rerankScore, similarity, atom, ... }
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* @param {object[]} allAtoms - getStateAtoms() result
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* Each: { atomId, floor, semantic, who, edges, dynamics, where }
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* Each: { atomId, floor, semantic, edges, where }
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* @param {object[]} stateVectors - getAllStateVectors() result
|
||||
* Each: { atomId, floor, vector: Float32Array }
|
||||
* @param {Float32Array|number[]} queryVector - R2 weighted query vector
|
||||
@@ -760,7 +758,7 @@ function fillMetricsEmpty(metrics) {
|
||||
cosineGateNoVector: 0,
|
||||
finalCount: 0,
|
||||
scoreDistribution: { min: 0, max: 0, mean: 0 },
|
||||
byChannel: { who: 0, what: 0, where: 0, how: 0 },
|
||||
byChannel: { what: 0, where: 0, how: 0 },
|
||||
time: 0,
|
||||
};
|
||||
}
|
||||
@@ -782,7 +780,7 @@ function fillMetrics(metrics, data) {
|
||||
cosineGateNoVector: data.cosineGateNoVector || 0,
|
||||
finalCount: data.finalCount || 0,
|
||||
scoreDistribution: data.scoreDistribution || { min: 0, max: 0, mean: 0 },
|
||||
byChannel: data.channelStats || { who: 0, what: 0, where: 0, how: 0 },
|
||||
byChannel: data.channelStats || { what: 0, where: 0, how: 0 },
|
||||
time: data.time || 0,
|
||||
};
|
||||
}
|
||||
|
||||
@@ -71,11 +71,12 @@ export function buildEntityLexicon(store, context) {
|
||||
add(f.s);
|
||||
}
|
||||
|
||||
// 5. L0 atoms 的 who（新角色在 L2 总结前即可进入词典）
|
||||
// 5. L0 atoms 的 edges.s/edges.t（新角色在 L2 总结前即可进入词典）
|
||||
const atoms = getStateAtoms();
|
||||
for (const atom of atoms) {
|
||||
for (const name of (atom.who || [])) {
|
||||
add(name);
|
||||
for (const e of (atom.edges || [])) {
|
||||
add(e?.s);
|
||||
add(e?.t);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -122,11 +123,12 @@ export function buildDisplayNameMap(store, context) {
|
||||
if (!f.retracted) register(f.s);
|
||||
}
|
||||
|
||||
// 5. L0 atoms 的 who
|
||||
// 5. L0 atoms 的 edges.s/edges.t
|
||||
const atoms = getStateAtoms();
|
||||
for (const atom of atoms) {
|
||||
for (const name of (atom.who || [])) {
|
||||
register(name);
|
||||
for (const e of (atom.edges || [])) {
|
||||
register(e?.s);
|
||||
register(e?.t);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -125,7 +125,7 @@ export function createMetrics() {
|
||||
cosineGateNoVector: 0,
|
||||
finalCount: 0,
|
||||
scoreDistribution: { min: 0, max: 0, mean: 0 },
|
||||
byChannel: { who: 0, what: 0, where: 0, how: 0 },
|
||||
byChannel: { what: 0, where: 0, how: 0 },
|
||||
time: 0,
|
||||
},
|
||||
|
||||
@@ -366,7 +366,7 @@ export function formatMetricsLog(metrics) {
|
||||
lines.push(`├─ graph: ${m.diffusion.graphNodes} nodes, ${m.diffusion.graphEdges} edges`);
|
||||
if (m.diffusion.graphEdges > 0) {
|
||||
const ch = m.diffusion.byChannel || {};
|
||||
lines.push(`│ └─ by_channel: who=${ch.who || 0}, what=${ch.what || 0}, where=${ch.where || 0}, how=${ch.how || 0}`);
|
||||
lines.push(`│ └─ by_channel: what=${ch.what || 0}, where=${ch.where || 0}, how=${ch.how || 0}`);
|
||||
}
|
||||
if (m.diffusion.iterations > 0) {
|
||||
lines.push(`├─ ppr: ${m.diffusion.iterations} iterations, ε=${Number(m.diffusion.convergenceError).toExponential(1)}`);
|
||||
@@ -630,7 +630,7 @@ export function detectIssues(metrics) {
|
||||
// ─────────────────────────────────────────────────────────────────
|
||||
|
||||
if (m.diffusion.graphEdges === 0 && m.diffusion.seedCount > 0) {
|
||||
issues.push('No diffusion graph edges - atoms may lack who/edges fields');
|
||||
issues.push('No diffusion graph edges - atoms may lack edges fields');
|
||||
}
|
||||
|
||||
if (m.diffusion.pprActivated > 0 && m.diffusion.cosineGatePassed === 0) {
|
||||
|
||||
@@ -20,7 +20,9 @@
|
||||
// 阶段 5: Lexical Retrieval + Dense-Gated Event Merge
|
||||
// 阶段 6: Floor W-RRF Fusion + Rerank + L1 配对
|
||||
// 阶段 7: L1 配对组装（L0 → top-1 AI L1 + top-1 USER L1）
|
||||
// 阶段 8: Causation Trace
|
||||
// 阶段 7.5: PPR Diffusion
|
||||
// 阶段 8: L0 → L2 反向查找（后置，基于最终 l0Selected）
|
||||
// 阶段 9: Causation Trace
|
||||
// ═══════════════════════════════════════════════════════════════════════════
|
||||
|
||||
import { getAllEventVectors, getChunksByFloors, getMeta, getChunkVectorsByIds } from '../storage/chunk-store.js';
|
||||
@@ -1114,6 +1116,7 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) {
|
||||
const eventIndex = buildEventIndex(allEvents);
|
||||
let lexicalEventCount = 0;
|
||||
let lexicalEventFilteredByDense = 0;
|
||||
let l0LinkedCount = 0;
|
||||
const focusSetForLexical = new Set((bundle.focusEntities || []).map(normalize));
|
||||
|
||||
for (const eid of lexicalResult.eventIds) {
|
||||
@@ -1149,46 +1152,6 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) {
|
||||
lexicalEventCount++;
|
||||
}
|
||||
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
// 阶段 5.5: L0 → L2 反向查找
|
||||
// 已召回的 L0 楼层落在某 L2 事件范围内，但该 L2 自身未被召回
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
|
||||
const recalledL0Floors = new Set(anchorHits.map(h => h.floor));
|
||||
let l0LinkedCount = 0;
|
||||
|
||||
for (const event of allEvents) {
|
||||
if (existingEventIds.has(event.id)) continue;
|
||||
|
||||
const range = parseFloorRange(event.summary);
|
||||
if (!range) continue;
|
||||
|
||||
let hasOverlap = false;
|
||||
for (const floor of recalledL0Floors) {
|
||||
if (floor >= range.start && floor <= range.end) {
|
||||
hasOverlap = true;
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (!hasOverlap) continue;
|
||||
|
||||
// 实体分类：与所有路径统一标准
|
||||
const participants = (event.participants || []).map(p => normalize(p));
|
||||
const hasEntityMatch = focusSetForLexical.size > 0
|
||||
&& participants.some(p => focusSetForLexical.has(p));
|
||||
|
||||
const evVec = eventVectorMap.get(event.id);
|
||||
const sim = evVec?.length ? cosineSimilarity(queryVector_v1, evVec) : 0;
|
||||
|
||||
eventHits.push({
|
||||
event,
|
||||
similarity: sim,
|
||||
_recallType: hasEntityMatch ? 'DIRECT' : 'RELATED',
|
||||
});
|
||||
existingEventIds.add(event.id);
|
||||
l0LinkedCount++;
|
||||
}
|
||||
|
||||
if (metrics) {
|
||||
metrics.lexical.eventFilteredByDense = lexicalEventFilteredByDense;
|
||||
|
||||
@@ -1196,14 +1159,10 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) {
|
||||
metrics.event.byRecallType.lexical = lexicalEventCount;
|
||||
metrics.event.selected += lexicalEventCount;
|
||||
}
|
||||
if (l0LinkedCount > 0) {
|
||||
metrics.event.byRecallType.l0Linked = l0LinkedCount;
|
||||
metrics.event.selected += l0LinkedCount;
|
||||
}
|
||||
}
|
||||
|
||||
xbLog.info(MODULE_ID,
|
||||
`Lexical: chunks=${lexicalResult.chunkIds.length} events=${lexicalResult.eventIds.length} mergedEvents=+${lexicalEventCount} filteredByDense=${lexicalEventFilteredByDense} l0Linked=+${l0LinkedCount} (${lexTime}ms)`
|
||||
`Lexical: chunks=${lexicalResult.chunkIds.length} events=${lexicalResult.eventIds.length} mergedEvents=+${lexicalEventCount} filteredByDense=${lexicalEventFilteredByDense} floorFiltered=${metrics.lexical.floorFilteredByDense || 0} (${lexTime}ms)`
|
||||
);
|
||||
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
@@ -1248,7 +1207,56 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) {
|
||||
metrics.timing.diffusion = metrics.diffusion?.time || 0;
|
||||
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
// 阶段 7: Causation Trace
|
||||
// Stage 8: L0 → L2 反向查找（后置，基于最终 l0Selected）
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
|
||||
const recalledL0Floors = new Set(l0Selected.map(x => x.floor));
|
||||
|
||||
for (const event of allEvents) {
|
||||
if (existingEventIds.has(event.id)) continue;
|
||||
|
||||
const range = parseFloorRange(event.summary);
|
||||
if (!range) continue;
|
||||
|
||||
let hasOverlap = false;
|
||||
for (const floor of recalledL0Floors) {
|
||||
if (floor >= range.start && floor <= range.end) {
|
||||
hasOverlap = true;
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (!hasOverlap) continue;
|
||||
|
||||
// Dense similarity 门槛（与 Lexical Event 对齐）
|
||||
const evVec = eventVectorMap.get(event.id);
|
||||
const sim = evVec?.length ? cosineSimilarity(queryVector_v1, evVec) : 0;
|
||||
if (sim < CONFIG.LEXICAL_EVENT_DENSE_MIN) continue;
|
||||
|
||||
// 实体分类：与所有路径统一标准
|
||||
const participants = (event.participants || []).map(p => normalize(p));
|
||||
const hasEntityMatch = focusSetForLexical.size > 0
|
||||
&& participants.some(p => focusSetForLexical.has(p));
|
||||
|
||||
eventHits.push({
|
||||
event,
|
||||
similarity: sim,
|
||||
_recallType: hasEntityMatch ? 'DIRECT' : 'RELATED',
|
||||
});
|
||||
existingEventIds.add(event.id);
|
||||
l0LinkedCount++;
|
||||
}
|
||||
|
||||
if (metrics && l0LinkedCount > 0) {
|
||||
metrics.event.byRecallType.l0Linked = l0LinkedCount;
|
||||
metrics.event.selected += l0LinkedCount;
|
||||
}
|
||||
|
||||
xbLog.info(MODULE_ID,
|
||||
`L0-linked events: ${recalledL0Floors.size} floors → ${l0LinkedCount} events linked (sim≥${CONFIG.LEXICAL_EVENT_DENSE_MIN})`
|
||||
);
|
||||
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
// 阶段 9: Causation Trace
|
||||
// ═══════════════════════════════════════════════════════════════════
|
||||
|
||||
const { results: causalMap, maxDepth: causalMaxDepth } = traceCausation(eventHits, eventIndex);
|
||||
@@ -1288,7 +1296,7 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) {
|
||||
console.log(`Fusion (floor, weighted): dense=${metrics.fusion.denseFloors} lex=${metrics.fusion.lexFloors} → cap=${metrics.fusion.afterCap} (${metrics.fusion.time}ms)`);
|
||||
console.log(`Floor Rerank: ${metrics.evidence.beforeRerank || 0} → ${metrics.evidence.floorsSelected || 0} floors → L0=${metrics.evidence.l0Collected || 0} (${metrics.evidence.rerankTime || 0}ms)`);
|
||||
console.log(`L1: ${metrics.evidence.l1Pulled || 0} pulled → ${metrics.evidence.l1Attached || 0} attached (${metrics.evidence.l1CosineTime || 0}ms)`);
|
||||
console.log(`Events: ${eventHits.length} hits, ${causalChain.length} causal`);
|
||||
console.log(`Events: ${eventHits.length} hits (l0Linked=+${l0LinkedCount}), ${causalChain.length} causal`);
|
||||
console.log(`Diffusion: ${metrics.diffusion?.seedCount || 0} seeds → ${metrics.diffusion?.pprActivated || 0} activated → ${metrics.diffusion?.finalCount || 0} final (${metrics.diffusion?.time || 0}ms)`);
|
||||
console.groupEnd();
|
||||
|
||||
|
||||
Reference in New Issue
Block a user