From ab8f2c9f402cbbedae7e8811695e5e0fcdd0650e Mon Sep 17 00:00:00 2001 From: bielie Date: Sun, 15 Feb 2026 18:58:51 +0800 Subject: [PATCH] refactor focus concepts: add focusTerms/focusCharacters and switch character filtering --- modules/story-summary/generate/prompt.js | 124 +++++++++++------ .../vector/retrieval/entity-lexicon.js | 128 +++++++++++------- .../story-summary/vector/retrieval/metrics.js | 12 +- .../vector/retrieval/query-builder.js | 28 +++- .../story-summary/vector/retrieval/recall.js | 53 +++++--- 5 files changed, 228 insertions(+), 117 deletions(-) diff --git a/modules/story-summary/generate/prompt.js b/modules/story-summary/generate/prompt.js index b57d99d..07cb0a8 100644 --- a/modules/story-summary/generate/prompt.js +++ b/modules/story-summary/generate/prompt.js @@ -19,6 +19,7 @@ import { getVectorConfig, getSummaryPanelConfig, getSettings } from "../data/con import { recallMemory } from "../vector/retrieval/recall.js"; import { getMeta } from "../vector/storage/chunk-store.js"; import { getEngineFingerprint } from "../vector/utils/embedder.js"; +import { buildTrustedCharacters } from "../vector/retrieval/entity-lexicon.js"; // Metrics import { formatMetricsLog, detectIssues } from "../vector/retrieval/metrics.js"; @@ -239,23 +240,11 @@ function buildPostscript() { * @returns {Set} 角色名称集合（标准化后） */ function getKnownCharacters(store) { - const names = new Set(); - - const arcs = store?.json?.arcs || []; - for (const a of arcs) { - if (a.name) names.add(normalize(a.name)); - } - - const main = store?.json?.characters?.main || []; - for (const m of main) { - const name = typeof m === 'string' ? m : m.name; - if (name) names.add(normalize(name)); - } - const { name1, name2 } = getContext(); + const names = buildTrustedCharacters(store, { name1, name2 }) || new Set(); + // Keep name1 in known-character filtering domain to avoid behavior regression + // for L3 subject filtering (lexicon exclusion and filtering semantics are different concerns). if (name1) names.add(normalize(name1)); - if (name2) names.add(normalize(name2)); - return names; } @@ -272,14 +261,14 @@ function parseRelationTarget(predicate) { /** * 按相关性过滤 facts * @param {object[]} facts - 所有 facts - * @param {string[]} focusEntities - 焦点实体 + * @param {string[]} focusCharacters - 焦点人物 * @param {Set} knownCharacters - 已知角色 * @returns {object[]} 过滤后的 facts */ -function filterConstraintsByRelevance(facts, focusEntities, knownCharacters) { +function filterConstraintsByRelevance(facts, focusCharacters, knownCharacters) { if (!facts?.length) return []; - const focusSet = new Set((focusEntities || []).map(normalize)); + const focusSet = new Set((focusCharacters || []).map(normalize)); return facts.filter(f => { if (f._isState === true) return true; @@ -304,13 +293,13 @@ function filterConstraintsByRelevance(facts, focusEntities, knownCharacters) { /** * Build people dictionary for constraints display. - * Primary source: selected event participants; fallback: focus entities. + * Primary source: selected event participants; fallback: focus characters. * * @param {object|null} recallResult - * @param {string[]} focusEntities + * @param {string[]} focusCharacters * @returns {Map} normalize(name) -> display name */ -function buildConstraintPeopleDict(recallResult, focusEntities = []) { +function buildConstraintPeopleDict(recallResult, focusCharacters = []) { const dict = new Map(); const add = (raw) => { const display = String(raw || '').trim(); @@ -326,7 +315,7 @@ function buildConstraintPeopleDict(recallResult, focusEntities = []) { } if (dict.size === 0) { - for (const f of (focusEntities || [])) add(f); + for (const f of (focusCharacters || [])) add(f); } return dict; @@ -375,16 +364,19 @@ function formatConstraintLine(f, includeSubject = false) { * @param {{ people: Map, world: object[] }} grouped * @returns {string[]} */ -function formatConstraintsStructured(grouped) { +function formatConstraintsStructured(grouped, order = 'desc') { const lines = []; const people = grouped?.people || new Map(); const world = grouped?.world || []; + const sorter = order === 'asc' + ? ((a, b) => (a.since || 0) - (b.since || 0)) + : ((a, b) => (b.since || 0) - (a.since || 0)); if (people.size > 0) { lines.push('people:'); for (const [name, facts] of people.entries()) { lines.push(` ${name}:`); - const sorted = [...facts].sort((a, b) => (b.since || 0) - (a.since || 0)); + const sorted = [...facts].sort(sorter); for (const f of sorted) { lines.push(` ${formatConstraintLine(f, false)}`); } @@ -393,7 +385,7 @@ function formatConstraintsStructured(grouped) { if (world.length > 0) { lines.push('world:'); - const sortedWorld = [...world].sort((a, b) => (b.since || 0) - (a.since || 0)); + const sortedWorld = [...world].sort(sorter); for (const f of sortedWorld) { lines.push(` ${formatConstraintLine(f, true)}`); } @@ -402,6 +394,58 @@ function formatConstraintsStructured(grouped) { return lines; } +function tryConsumeConstraintLineBudget(line, budgetState) { + const cost = estimateTokens(line); + if (budgetState.used + cost > budgetState.max) return false; + budgetState.used += cost; + return true; +} + +function selectConstraintsByBudgetDesc(grouped, budgetState) { + const selectedPeople = new Map(); + const selectedWorld = []; + const people = grouped?.people || new Map(); + const world = grouped?.world || []; + + if (people.size > 0) { + if (!tryConsumeConstraintLineBudget('people:', budgetState)) { + return { people: selectedPeople, world: selectedWorld }; + } + for (const [name, facts] of people.entries()) { + const header = ` ${name}:`; + if (!tryConsumeConstraintLineBudget(header, budgetState)) { + return { people: selectedPeople, world: selectedWorld }; + } + const picked = []; + const sorted = [...facts].sort((a, b) => (b.since || 0) - (a.since || 0)); + for (const f of sorted) { + const line = ` ${formatConstraintLine(f, false)}`; + if (!tryConsumeConstraintLineBudget(line, budgetState)) { + return { people: selectedPeople, world: selectedWorld }; + } + picked.push(f); + } + selectedPeople.set(name, picked); + } + } + + if (world.length > 0) { + if (!tryConsumeConstraintLineBudget('world:', budgetState)) { + return { people: selectedPeople, world: selectedWorld }; + } + const sortedWorld = [...world].sort((a, b) => (b.since || 0) - (a.since || 0)); + for (const f of sortedWorld) { + const line = ` ${formatConstraintLine(f, true)}`; + if (!tryConsumeConstraintLineBudget(line, budgetState)) { + return { people: selectedPeople, world: selectedWorld }; + } + selectedWorld.push(f); + } + } + + return { people: selectedPeople, world: selectedWorld }; +} + // ───────────────────────────────────────────────────────────────────────────── // 格式化函数 // ───────────────────────────────────────────────────────────────────────────── @@ -703,7 +747,7 @@ function buildNonVectorPrompt(store) { nonVectorKnownCharacters ); const groupedConstraints = groupConstraintsForDisplay(filteredConstraints, nonVectorPeopleDict); - const constraintLines = formatConstraintsStructured(groupedConstraints); + const constraintLines = formatConstraintsStructured(groupedConstraints, 'asc'); if (constraintLines.length) { sections.push(`[定了的事] 已确立的事实\n${constraintLines.join("\n")}`); @@ -772,12 +816,12 @@ export function buildNonVectorPromptText() { * @param {object} store - 存储对象 * @param {object} recallResult - 召回结果 * @param {Map} causalById - 因果事件索引 - * @param {string[]} focusEntities - 焦点实体 + * @param {string[]} focusCharacters - 焦点人物 * @param {object} meta - 元数据 * @param {object} metrics - 指标对象 * @returns {Promise<{promptText: string, injectionStats: object, metrics: object}>} */ -async function buildVectorPrompt(store, recallResult, causalById, focusEntities, meta, metrics) { +async function buildVectorPrompt(store, recallResult, causalById, focusCharacters, meta, metrics) { const T_Start = performance.now(); const data = store.json || {}; @@ -825,21 +869,21 @@ async function buildVectorPrompt(store, recallResult, causalById, focusEntities, const allFacts = getFacts(); const knownCharacters = getKnownCharacters(store); - const filteredConstraints = filterConstraintsByRelevance(allFacts, focusEntities, knownCharacters); - const constraintPeopleDict = buildConstraintPeopleDict(recallResult, focusEntities); + const filteredConstraints = filterConstraintsByRelevance(allFacts, focusCharacters, knownCharacters); + const constraintPeopleDict = buildConstraintPeopleDict(recallResult, focusCharacters); const groupedConstraints = groupConstraintsForDisplay(filteredConstraints, constraintPeopleDict); - const constraintLines = formatConstraintsStructured(groupedConstraints); if (metrics) { metrics.constraint.total = allFacts.length; metrics.constraint.filtered = allFacts.length - filteredConstraints.length; } + const constraintBudget = { used: 0, max: Math.min(CONSTRAINT_MAX, total.max - total.used) }; + const groupedSelectedConstraints = selectConstraintsByBudgetDesc(groupedConstraints, constraintBudget); + const constraintLines = formatConstraintsStructured(groupedSelectedConstraints, 'asc'); + if (constraintLines.length) { - const constraintBudget = { used: 0, max: Math.min(CONSTRAINT_MAX, total.max - total.used) }; - for (const line of constraintLines) { - if (!pushWithBudget(assembled.constraints.lines, line, constraintBudget)) break; - } + assembled.constraints.lines.push(...constraintLines); assembled.constraints.tokens = constraintBudget.used; total.used += constraintBudget.used; injectionStats.constraint.count = assembled.constraints.lines.length; @@ -867,7 +911,7 @@ async function buildVectorPrompt(store, recallResult, causalById, focusEntities, const userName = String(name1 || "").trim(); const relevant = new Set( - [userName, ...(focusEntities || [])] + [userName, ...(focusCharacters || [])] .map(s => String(s || "").trim()) .filter(Boolean) ); @@ -1048,7 +1092,7 @@ async function buildVectorPrompt(store, recallResult, causalById, focusEntities, const keepVisible = store.keepVisibleCount ?? 3; // 收集未被事件消费的 L0，按 rerankScore 降序 - const focusSetForEvidence = new Set((focusEntities || []).map(normalize).filter(Boolean)); + const focusSetForEvidence = new Set((focusCharacters || []).map(normalize).filter(Boolean)); const remainingL0 = l0Selected .filter(l0 => !usedL0Ids.has(l0.id)) @@ -1279,7 +1323,9 @@ export async function buildVectorPromptText(excludeLastAi = false, hooks = {}) { l0Selected: recallResult?.l0Selected || [], l1ByFloor: recallResult?.l1ByFloor || new Map(), causalChain: recallResult?.causalChain || [], - focusEntities: recallResult?.focusEntities || [], + focusTerms: recallResult?.focusTerms || recallResult?.focusEntities || [], + focusEntities: recallResult?.focusTerms || recallResult?.focusEntities || [], // compat alias + focusCharacters: recallResult?.focusCharacters || [], metrics: recallResult?.metrics || null, }; @@ -1340,7 +1386,7 @@ export async function buildVectorPromptText(excludeLastAi = false, hooks = {}) { store, recallResult, causalById, - recallResult?.focusEntities || [], + recallResult?.focusCharacters || [], meta, recallResult?.metrics || null ); diff --git a/modules/story-summary/vector/retrieval/entity-lexicon.js b/modules/story-summary/vector/retrieval/entity-lexicon.js index c78b29e..cd1846a 100644 --- a/modules/story-summary/vector/retrieval/entity-lexicon.js +++ b/modules/story-summary/vector/retrieval/entity-lexicon.js @@ -36,6 +36,83 @@ function isBlacklistedPersonTerm(raw) { return PERSON_LEXICON_BLACKLIST.has(normalize(raw)); } +function addPersonTerm(set, raw) { + const n = normalize(raw); + if (!n || n.length < 2) return; + if (isBlacklistedPersonTerm(n)) return; + set.add(n); +} + +function collectTrustedCharacters(store, context) { + const trusted = new Set(); + + const main = store?.json?.characters?.main || []; + for (const m of main) { + addPersonTerm(trusted, typeof m === 'string' ? m : m.name); + } + + const arcs = store?.json?.arcs || []; + for (const a of arcs) { + addPersonTerm(trusted, a.name); + } + + if (context?.name2) { + addPersonTerm(trusted, context.name2); + } + + const events = store?.json?.events || []; + for (const ev of events) { + for (const p of (ev?.participants || [])) { + addPersonTerm(trusted, p); + } + } + + if (context?.name1) { + trusted.delete(normalize(context.name1)); + } + + return trusted; +} + +/** + * Build trusted character pool only (without scanning L0 candidate atoms). + * trustedCharacters: main/arcs/name2/L2 participants, excludes name1. + * + * @param {object} store + * @param {object} context + * @returns {Set} + */ +export function buildTrustedCharacters(store, context) { + return collectTrustedCharacters(store, context); +} + +function collectCandidateCharactersFromL0(context) { + const candidate = new Set(); + const atoms = getStateAtoms(); + for (const atom of atoms) { + for (const e of (atom.edges || [])) { + addPersonTerm(candidate, e?.s); + addPersonTerm(candidate, e?.t); + } + } + if (context?.name1) { + candidate.delete(normalize(context.name1)); + } + return candidate; +} + +/** + * Build character pools with trust tiers. + * trustedCharacters: main/arcs/name2/L2 participants (clean source) + * candidateCharacters: L0 edges.s/t (blacklist-cleaned) + */ +export function buildCharacterPools(store, context) { + const trustedCharacters = collectTrustedCharacters(store, context); + const candidateCharacters = collectCandidateCharactersFromL0(context); + const allCharacters = new Set([...trustedCharacters, ...candidateCharacters]); + return { trustedCharacters, candidateCharacters, allCharacters }; +} + /** * 构建实体词典 * @@ -53,56 +130,7 @@ function isBlacklistedPersonTerm(raw) { * @returns {Set} 标准化后的实体集合 */ export function buildEntityLexicon(store, context) { - const lexicon = new Set(); - - // 内部辅助：添加非空实体 - const add = (raw) => { - const n = normalize(raw); - if (!n || n.length < 2) return; - if (isBlacklistedPersonTerm(n)) return; - lexicon.add(n); - }; - - // 1. 主要角色 - const main = store?.json?.characters?.main || []; - for (const m of main) { - add(typeof m === 'string' ? m : m.name); - } - - // 2. 弧光角色 - const arcs = store?.json?.arcs || []; - for (const a of arcs) { - add(a.name); - } - - // 3. 当前角色 name2 - if (context?.name2) { - add(context.name2); - } - - // 4. L2 events 参与者 - const events = store?.json?.events || []; - for (const ev of events) { - for (const p of (ev?.participants || [])) { - add(p); - } - } - - // 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 e of (atom.edges || [])) { - add(e?.s); - add(e?.t); - } - } - - // ★ 硬约束：删除 name1 - if (context?.name1) { - lexicon.delete(normalize(context.name1)); - } - - return lexicon; + return buildCharacterPools(store, context).allCharacters; } /** diff --git a/modules/story-summary/vector/retrieval/metrics.js b/modules/story-summary/vector/retrieval/metrics.js index 9c49128..4530788 100644 --- a/modules/story-summary/vector/retrieval/metrics.js +++ b/modules/story-summary/vector/retrieval/metrics.js @@ -36,6 +36,8 @@ export function createMetrics() { // Anchor (L0 StateAtoms) - 语义锚点 anchor: { needRecall: false, + focusTerms: [], + focusCharacters: [], focusEntities: [], matched: 0, floorsHit: 0, @@ -85,6 +87,7 @@ export function createMetrics() { causalChainDepth: 0, causalCount: 0, entitiesUsed: 0, + focusTermsCount: 0, entityNames: [], }, @@ -254,7 +257,8 @@ export function formatMetricsLog(metrics) { lines.push('[Anchor] L0 StateAtoms - 语义锚点'); lines.push(`├─ need_recall: ${m.anchor.needRecall}`); if (m.anchor.needRecall) { - lines.push(`├─ focus_entities: [${(m.anchor.focusEntities || []).join(', ')}]`); + lines.push(`├─ focus_terms: [${(m.anchor.focusTerms || m.anchor.focusEntities || []).join(', ')}]`); + lines.push(`├─ focus_characters: [${(m.anchor.focusCharacters || []).join(', ')}]`); lines.push(`├─ matched: ${m.anchor.matched || 0}`); lines.push(`└─ floors_hit: ${m.anchor.floorsHit || 0}`); } @@ -310,7 +314,7 @@ export function formatMetricsLog(metrics) { if (m.event.entityFilter) { const ef = m.event.entityFilter; lines.push(`├─ entity_filter:`); - lines.push(`│ ├─ focus_entities: [${(ef.focusEntities || []).join(', ')}]`); + lines.push(`│ ├─ focus_characters: [${(ef.focusCharacters || ef.focusEntities || []).join(', ')}]`); lines.push(`│ ├─ before: ${ef.before}`); lines.push(`│ ├─ after: ${ef.after}`); lines.push(`│ └─ filtered: ${ef.filtered}`); @@ -338,7 +342,7 @@ export function formatMetricsLog(metrics) { } lines.push(`├─ causal_chain: depth=${m.event.causalChainDepth}, count=${m.event.causalCount}`); - lines.push(`└─ entities_used: ${m.event.entitiesUsed} [${(m.event.entityNames || []).join(', ')}]`); + lines.push(`└─ focus_characters_used: ${m.event.entitiesUsed} [${(m.event.entityNames || []).join(', ')}], focus_terms_count=${m.event.focusTermsCount || 0}`); lines.push(''); // Evidence (Two-Stage: Floor Rerank → L1 Pull) @@ -485,7 +489,7 @@ export function detectIssues(metrics) { // 查询构建问题 // ───────────────────────────────────────────────────────────────── - if ((m.anchor.focusEntities || []).length === 0) { + if ((m.anchor.focusTerms || m.anchor.focusEntities || []).length === 0) { issues.push('No focus entities extracted - entity lexicon may be empty or messages too short'); } diff --git a/modules/story-summary/vector/retrieval/query-builder.js b/modules/story-summary/vector/retrieval/query-builder.js index e424555..c5593a0 100644 --- a/modules/story-summary/vector/retrieval/query-builder.js +++ b/modules/story-summary/vector/retrieval/query-builder.js @@ -19,7 +19,7 @@ // ═══════════════════════════════════════════════════════════════════════════ import { getContext } from '../../../../../../../extensions.js'; -import { buildEntityLexicon, buildDisplayNameMap, extractEntitiesFromText } from './entity-lexicon.js'; +import { buildEntityLexicon, buildDisplayNameMap, extractEntitiesFromText, buildCharacterPools } from './entity-lexicon.js'; import { getSummaryStore } from '../../data/store.js'; import { filterText } from '../utils/text-filter.js'; import { tokenizeForIndex as tokenizerTokenizeForIndex } from '../utils/tokenizer.js'; @@ -137,7 +137,13 @@ function extractKeyTerms(text, maxTerms = LEXICAL_TERMS_MAX) { * @property {QuerySegment|null} hintsSegment - R2 hints 段（refinement 后填充） * @property {string} rerankQuery - rerank 用的纯自然语言查询（焦点在前） * @property {string[]} lexicalTerms - MiniSearch 查询词 - * @property {string[]} focusEntities - 焦点实体（原词形，已排除 name1） + * @property {string[]} focusTerms - 焦点词（原 focusEntities） + * @property {string[]} focusCharacters - 焦点人物（focusTerms ∩ trustedCharacters） + * @property {string[]} focusEntities - Deprecated alias of focusTerms + * @property {Set} allEntities - Full entity lexicon (includes non-character entities) + * @property {Set} allCharacters - Union of trusted and candidate character pools + * @property {Set} trustedCharacters - Clean character pool (main/arcs/name2/L2 participants) + * @property {Set} candidateCharacters - Extended character pool from L0 edges.s/t after cleanup * @property {Set} _lexicon - 实体词典（内部使用） * @property {Map} _displayMap - 标准化→原词形映射（内部使用） */ @@ -203,9 +209,10 @@ export function buildQueryBundle(lastMessages, pendingUserMessage, store = null, context = { name1: ctx.name1, name2: ctx.name2 }; } - // 1. 实体词典 + // 1. 实体/人物词典 const lexicon = buildEntityLexicon(store, context); const displayMap = buildDisplayNameMap(store, context); + const { trustedCharacters, candidateCharacters, allCharacters } = buildCharacterPools(store, context); // 2. 分离焦点与上下文 const contextEntries = []; @@ -253,9 +260,10 @@ export function buildQueryBundle(lastMessages, pendingUserMessage, store = null, } } - // 3. 提取焦点实体 + // 3. 提取焦点词与焦点人物 const combinedText = allCleanTexts.join(' '); - const focusEntities = extractEntitiesFromText(combinedText, lexicon, displayMap); + const focusTerms = extractEntitiesFromText(combinedText, lexicon, displayMap); + const focusCharacters = focusTerms.filter(term => trustedCharacters.has(term.toLowerCase())); // 4. 构建 querySegments // 上下文在前（oldest → newest），焦点在末尾 @@ -286,7 +294,7 @@ export function buildQueryBundle(lastMessages, pendingUserMessage, store = null, : contextLines.join('\n'); // 6. lexicalTerms（实体优先 + 高频实词补充） - const entityTerms = focusEntities.map(e => e.toLowerCase()); + const entityTerms = focusTerms.map(e => e.toLowerCase()); const textTerms = extractKeyTerms(combinedText); const termSet = new Set(entityTerms); for (const t of textTerms) { @@ -299,7 +307,13 @@ export function buildQueryBundle(lastMessages, pendingUserMessage, store = null, hintsSegment: null, rerankQuery, lexicalTerms: Array.from(termSet), - focusEntities, + focusTerms, + focusCharacters, + focusEntities: focusTerms, // deprecated alias (compat) + allEntities: lexicon, + allCharacters, + trustedCharacters, + candidateCharacters, _lexicon: lexicon, _displayMap: displayMap, }; diff --git a/modules/story-summary/vector/retrieval/recall.js b/modules/story-summary/vector/retrieval/recall.js index ff0e601..3484c04 100644 --- a/modules/story-summary/vector/retrieval/recall.js +++ b/modules/story-summary/vector/retrieval/recall.js @@ -319,7 +319,7 @@ async function recallAnchors(queryVector, vectorConfig, metrics) { // 返回 { events, vectorMap } // ═══════════════════════════════════════════════════════════════════════════ -async function recallEvents(queryVector, allEvents, vectorConfig, focusEntities, metrics) { +async function recallEvents(queryVector, allEvents, vectorConfig, focusCharacters, metrics) { const { chatId } = getContext(); if (!chatId || !queryVector?.length || !allEvents?.length) { return { events: [], vectorMap: new Map() }; @@ -339,7 +339,7 @@ async function recallEvents(queryVector, allEvents, vectorConfig, focusEntities, return { events: [], vectorMap }; } - const focusSet = new Set((focusEntities || []).map(normalize)); + const focusSet = new Set((focusCharacters || []).map(normalize)); const scored = allEvents.map(event => { const v = vectorMap.get(event.id); @@ -381,7 +381,8 @@ async function recallEvents(queryVector, allEvents, vectorConfig, focusEntities, if (metrics) { metrics.event.entityFilter = { - focusEntities: focusEntities || [], + focusCharacters: focusCharacters || [], + focusEntities: focusCharacters || [], before: beforeFilter, after: candidates.length, filtered: beforeFilter - candidates.length, @@ -962,6 +963,8 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) { l1ByFloor: new Map(), causalChain: [], focusEntities: [], + focusTerms: [], + focusCharacters: [], elapsed: metrics.timing.total, logText: 'No events.', metrics, @@ -982,9 +985,13 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) { const lastMessages = getLastMessages(chat, lastMessagesCount, excludeLastAi); const bundle = buildQueryBundle(lastMessages, pendingUserMessage); + const focusTerms = bundle.focusTerms || bundle.focusEntities || []; + const focusCharacters = bundle.focusCharacters || []; metrics.query.buildTime = Math.round(performance.now() - T_Build_Start); - metrics.anchor.focusEntities = bundle.focusEntities; + metrics.anchor.focusTerms = focusTerms; + metrics.anchor.focusEntities = focusTerms; // compat + metrics.anchor.focusCharacters = focusCharacters; if (metrics.query?.lengths) { metrics.query.lengths.v0Chars = bundle.querySegments.reduce((sum, s) => sum + s.text.length, 0); @@ -993,7 +1000,7 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) { } xbLog.info(MODULE_ID, - `Query Build: focus=[${bundle.focusEntities.join(',')}] segments=${bundle.querySegments.length} lexTerms=[${bundle.lexicalTerms.slice(0, 5).join(',')}]` + `Query Build: focus_terms=[${focusTerms.join(',')}] focus_characters=[${focusCharacters.join(',')}] segments=${bundle.querySegments.length} lexTerms=[${bundle.lexicalTerms.slice(0, 5).join(',')}]` ); // ═══════════════════════════════════════════════════════════════════ @@ -1005,7 +1012,9 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) { metrics.timing.total = Math.round(performance.now() - T0); return { events: [], l0Selected: [], l1ByFloor: new Map(), causalChain: [], - focusEntities: bundle.focusEntities, + focusEntities: focusTerms, + focusTerms, + focusCharacters, elapsed: metrics.timing.total, logText: 'No query segments.', metrics, @@ -1025,7 +1034,9 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) { metrics.timing.total = Math.round(performance.now() - T0); return { events: [], l0Selected: [], l1ByFloor: new Map(), causalChain: [], - focusEntities: bundle.focusEntities, + focusEntities: focusTerms, + focusTerms, + focusCharacters, elapsed: metrics.timing.total, logText: 'Embedding failed (round 1, after retry).', metrics, @@ -1037,7 +1048,9 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) { metrics.timing.total = Math.round(performance.now() - T0); return { events: [], l0Selected: [], l1ByFloor: new Map(), causalChain: [], - focusEntities: bundle.focusEntities, + focusEntities: focusTerms, + focusTerms, + focusCharacters, elapsed: metrics.timing.total, logText: 'Empty query vectors (round 1).', metrics, @@ -1055,7 +1068,9 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) { metrics.timing.total = Math.round(performance.now() - T0); return { events: [], l0Selected: [], l1ByFloor: new Map(), causalChain: [], - focusEntities: bundle.focusEntities, + focusEntities: focusTerms, + focusTerms, + focusCharacters, elapsed: metrics.timing.total, logText: 'Weighted average produced empty vector.', metrics, @@ -1067,7 +1082,7 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) { const r1AnchorTime = Math.round(performance.now() - T_R1_Anchor_Start); const T_R1_Event_Start = performance.now(); - const { events: eventHits_v0 } = await recallEvents(queryVector_v0, allEvents, vectorConfig, bundle.focusEntities, null); + const { events: eventHits_v0 } = await recallEvents(queryVector_v0, allEvents, vectorConfig, focusCharacters, null); const r1EventTime = Math.round(performance.now() - T_R1_Event_Start); xbLog.info(MODULE_ID, @@ -1089,7 +1104,7 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) { } xbLog.info(MODULE_ID, - `Refinement: focus=[${bundle.focusEntities.join(',')}] hasHints=${!!bundle.hintsSegment} (${metrics.query.refineTime}ms)` + `Refinement: focus_terms=[${focusTerms.join(',')}] focus_characters=[${focusCharacters.join(',')}] hasHints=${!!bundle.hintsSegment} (${metrics.query.refineTime}ms)` ); // ═══════════════════════════════════════════════════════════════════ @@ -1129,7 +1144,7 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) { metrics.timing.anchorSearch = Math.round(performance.now() - T_R2_Anchor_Start); const T_R2_Event_Start = performance.now(); - let { events: eventHits, vectorMap: eventVectorMap } = await recallEvents(queryVector_v1, allEvents, vectorConfig, bundle.focusEntities, metrics); + let { events: eventHits, vectorMap: eventVectorMap } = await recallEvents(queryVector_v1, allEvents, vectorConfig, focusCharacters, metrics); metrics.timing.eventRetrieval = Math.round(performance.now() - T_R2_Event_Start); xbLog.info(MODULE_ID, @@ -1178,7 +1193,7 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) { let lexicalEventCount = 0; let lexicalEventFilteredByDense = 0; let l0LinkedCount = 0; - const focusSetForLexical = new Set((bundle.focusEntities || []).map(normalize)); + const focusSetForLexical = new Set((focusCharacters || []).map(normalize)); for (const eid of lexicalResult.eventIds) { if (existingEventIds.has(eid)) continue; @@ -1351,14 +1366,16 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) { // ═══════════════════════════════════════════════════════════════════ metrics.timing.total = Math.round(performance.now() - T0); - metrics.event.entityNames = bundle.focusEntities; - metrics.event.entitiesUsed = bundle.focusEntities.length; + metrics.event.entityNames = focusCharacters; + metrics.event.entitiesUsed = focusCharacters.length; + metrics.event.focusTermsCount = focusTerms.length; console.group('%c[Recall v9]', 'color: #7c3aed; font-weight: bold'); console.log(`Total: ${metrics.timing.total}ms`); console.log(`Query Build: ${metrics.query.buildTime}ms | Refine: ${metrics.query.refineTime}ms`); console.log(`R1 weights: [${r1Weights.map(w => w.toFixed(2)).join(', ')}]`); - console.log(`Focus: [${bundle.focusEntities.join(', ')}]`); + console.log(`Focus terms: [${focusTerms.join(', ')}]`); + console.log(`Focus characters: [${focusCharacters.join(', ')}]`); console.log(`Round 2 Anchors: ${anchorHits.length} hits → ${anchorFloors_dense.size} floors`); console.log(`Lexical: chunks=${lexicalResult.chunkIds.length} events=${lexicalResult.eventIds.length} evtMerged=+${lexicalEventCount} evtFiltered=${lexicalEventFilteredByDense} floorFiltered=${metrics.lexical.floorFilteredByDense || 0} (idx=${indexReadyTime}ms search=${lexicalResult.searchTime || 0}ms total=${lexTime}ms)`); console.log(`Fusion (floor, weighted): dense=${metrics.fusion.denseFloors} lex=${metrics.fusion.lexFloors} → cap=${metrics.fusion.afterCap} (${metrics.fusion.time}ms)`); @@ -1373,7 +1390,9 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) { causalChain, l0Selected, l1ByFloor, - focusEntities: bundle.focusEntities, + focusEntities: focusTerms, + focusTerms, + focusCharacters, elapsed: metrics.timing.total, metrics, };