1071 lines
43 KiB
JavaScript
1071 lines
43 KiB
JavaScript
// ═══════════════════════════════════════════════════════════════════════════
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// Story Summary - Prompt Injection (v3 - DSL 版 + Orphan 分组修复)
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// - 仅负责"构建注入文本"，不负责写入 extension_prompts
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// - 注入发生在 story-summary.js：GENERATION_STARTED 时写入 extension_prompts
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// ═══════════════════════════════════════════════════════════════════════════
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import { getContext } from "../../../../../../extensions.js";
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import { xbLog } from "../../../core/debug-core.js";
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import { getSummaryStore, getFacts, isRelationFact } from "../data/store.js";
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import { getVectorConfig, getSummaryPanelConfig, getSettings } from "../data/config.js";
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import { recallMemory, buildQueryText } from "../vector/retrieval/recall.js";
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import { getChunksByFloors, getAllChunkVectors, getAllEventVectors, getMeta } from "../vector/storage/chunk-store.js";
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// METRICS
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import { formatMetricsLog, detectIssues } from "../vector/retrieval/metrics.js";
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const MODULE_ID = "summaryPrompt";
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// ─────────────────────────────────────────────────────────────────────────────
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// 召回失败提示节流
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// ─────────────────────────────────────────────────────────────────────────────
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let lastRecallFailAt = 0;
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const RECALL_FAIL_COOLDOWN_MS = 10_000;
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function canNotifyRecallFail() {
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const now = Date.now();
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if (now - lastRecallFailAt < RECALL_FAIL_COOLDOWN_MS) return false;
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lastRecallFailAt = now;
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return true;
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}
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// ─────────────────────────────────────────────────────────────────────────────
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// 预算常量
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// ─────────────────────────────────────────────────────────────────────────────
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const MAIN_BUDGET_MAX = 10000;
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const ORPHAN_MAX = 2500;
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const RECENT_ORPHAN_MAX = 5000;
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const TOTAL_BUDGET_MAX = 15000;
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const L1_MAX = 2000;
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const ARCS_MAX = 1500;
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const TOP_N_STAR = 5;
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// ─────────────────────────────────────────────────────────────────────────────
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// 工具函数
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// ─────────────────────────────────────────────────────────────────────────────
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function estimateTokens(text) {
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if (!text) return 0;
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const s = String(text);
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const zh = (s.match(/[\u4e00-\u9fff]/g) || []).length;
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return Math.ceil(zh + (s.length - zh) / 4);
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}
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function pushWithBudget(lines, text, state) {
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const t = estimateTokens(text);
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if (state.used + t > state.max) return false;
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lines.push(text);
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state.used += t;
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return true;
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}
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function cosineSimilarity(a, b) {
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if (!a?.length || !b?.length || a.length !== b.length) return 0;
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let dot = 0, nA = 0, nB = 0;
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for (let i = 0; i < a.length; i++) {
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dot += a[i] * b[i];
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nA += a[i] * a[i];
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nB += b[i] * b[i];
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}
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return nA && nB ? dot / (Math.sqrt(nA) * Math.sqrt(nB)) : 0;
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}
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function parseFloorRange(summary) {
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if (!summary) return null;
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const match = String(summary).match(/\(#(\d+)(?:-(\d+))?\)/);
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if (!match) return null;
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const start = Math.max(0, parseInt(match[1], 10) - 1);
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const end = Math.max(0, (match[2] ? parseInt(match[2], 10) : parseInt(match[1], 10)) - 1);
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return { start, end };
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}
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function cleanSummary(summary) {
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return String(summary || "")
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.replace(/\s*\(#\d+(?:-\d+)?\)\s*$/, "")
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.trim();
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}
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function normalize(s) {
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return String(s || '')
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.normalize('NFKC')
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.replace(/[\u200B-\u200D\uFEFF]/g, '')
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.trim()
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.toLowerCase();
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}
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// ─────────────────────────────────────────────────────────────────────────────
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// 上下文配对工具函数
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// ─────────────────────────────────────────────────────────────────────────────
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function getContextFloor(chunk) {
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if (chunk.isL0) return -1;
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return chunk.isUser ? chunk.floor + 1 : chunk.floor - 1;
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}
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function pickContextChunk(candidates, mainChunk) {
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if (!candidates?.length) return null;
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const targetIsUser = !mainChunk.isUser;
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const opposite = candidates.find(c => c.isUser === targetIsUser);
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if (opposite) return opposite;
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return candidates[0];
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}
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function formatContextChunkLine(chunk, isAbove) {
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const { name1, name2 } = getContext();
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const speaker = chunk.isUser ? (name1 || "用户") : (chunk.speaker || name2 || "角色");
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const text = String(chunk.text || "").trim();
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const symbol = isAbove ? "┌" : "└";
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return ` ${symbol} #${chunk.floor + 1} [${speaker}] ${text}`;
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}
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// ─────────────────────────────────────────────────────────────────────────────
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// 系统前导与后缀
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// ─────────────────────────────────────────────────────────────────────────────
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function buildSystemPreamble() {
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return [
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"以上是还留在眼前的对话",
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"以下是脑海里的记忆：",
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"• [定了的事] 这些是不会变的",
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"• 其余部分是过往经历的回忆碎片",
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"",
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"请内化这些记忆：",
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].join("\n");
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}
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function buildPostscript() {
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return [
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"",
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"这些记忆是真实的，请自然地记住它们。",
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].join("\n");
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}
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// ─────────────────────────────────────────────────────────────────────────────
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// L1 Facts 分层过滤
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// ─────────────────────────────────────────────────────────────────────────────
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function getKnownCharacters(store) {
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const names = new Set();
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const arcs = store?.json?.arcs || [];
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for (const a of arcs) {
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if (a.name) names.add(normalize(a.name));
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}
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const main = store?.json?.characters?.main || [];
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for (const m of main) {
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const name = typeof m === 'string' ? m : m.name;
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if (name) names.add(normalize(name));
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}
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const { name1, name2 } = getContext();
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if (name1) names.add(normalize(name1));
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if (name2) names.add(normalize(name2));
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return names;
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}
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function parseRelationTarget(predicate) {
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const match = String(predicate || '').match(/^对(.+)的/);
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return match ? match[1] : null;
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}
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function filterFactsByRelevance(facts, focusEntities, knownCharacters) {
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if (!facts?.length) return [];
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const focusSet = new Set((focusEntities || []).map(normalize));
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return facts.filter(f => {
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if (f._isState === true) return true;
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if (isRelationFact(f)) {
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const from = normalize(f.s);
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const target = parseRelationTarget(f.p);
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const to = target ? normalize(target) : '';
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if (focusSet.has(from) || focusSet.has(to)) return true;
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return false;
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}
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const subjectNorm = normalize(f.s);
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if (knownCharacters.has(subjectNorm)) {
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return focusSet.has(subjectNorm);
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}
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return true;
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});
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}
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function formatFactsForInjection(facts, focusEntities, knownCharacters) {
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const filtered = filterFactsByRelevance(facts, focusEntities, knownCharacters);
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if (!filtered.length) return [];
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return filtered
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.sort((a, b) => (b.since || 0) - (a.since || 0))
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.map(f => {
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const since = f.since ? ` (#${f.since + 1})` : '';
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if (isRelationFact(f) && f.trend) {
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return `- ${f.s} ${f.p}: ${f.o} [${f.trend}]${since}`;
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}
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return `- ${f.s}的${f.p}: ${f.o}${since}`;
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});
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}
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// ─────────────────────────────────────────────────────────────────────────────
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// 格式化函数
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// ─────────────────────────────────────────────────────────────────────────────
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function formatArcLine(a) {
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const moments = (a.moments || [])
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.map(m => (typeof m === "string" ? m : m.text))
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.filter(Boolean);
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if (moments.length) {
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return `- ${a.name}：${moments.join(" → ")}`;
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}
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return `- ${a.name}：${a.trajectory}`;
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}
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function formatChunkFullLine(c) {
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const { name1, name2 } = getContext();
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if (c.isL0) {
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return `› #${c.floor + 1} [📌] ${String(c.text || "").trim()}`;
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}
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const speaker = c.isUser ? (name1 || "用户") : (c.speaker || name2 || "角色");
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return `› #${c.floor + 1} [${speaker}] ${String(c.text || "").trim()}`;
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}
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function formatCausalEventLine(causalItem, causalById) {
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const ev = causalItem?.event || {};
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const depth = Math.max(1, Math.min(9, causalItem?._causalDepth || 1));
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const indent = " │" + " ".repeat(depth - 1);
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const prefix = `${indent}├─ 前因`;
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const time = ev.timeLabel ? `【${ev.timeLabel}】` : "";
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const people = (ev.participants || []).join(" / ");
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const summary = cleanSummary(ev.summary);
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const r = parseFloorRange(ev.summary);
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const floorHint = r ? `(#${r.start + 1}${r.end !== r.start ? `-${r.end + 1}` : ""})` : "";
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const lines = [];
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lines.push(`${prefix}${time}${people ? ` ${people}` : ""}`);
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const body = `${summary}${floorHint ? ` ${floorHint}` : ""}`.trim();
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lines.push(`${indent} ${body}`);
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const evidence = causalItem._evidenceChunk;
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if (evidence) {
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const speaker = evidence.speaker || "角色";
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const text = String(evidence.text || "").trim();
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lines.push(`${indent} › #${evidence.floor + 1} [${speaker}] ${text}`);
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}
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return lines.join("\n");
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}
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function renumberEventText(text, newIndex) {
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const s = String(text || "");
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return s.replace(/^(\s*)\d+(\.\s*(?:【)?)/, `$1${newIndex}$2`);
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}
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function getEventSortKey(ev) {
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const r = parseFloorRange(ev?.summary);
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if (r) return r.start;
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const m = String(ev?.id || "").match(/evt-(\d+)/);
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return m ? parseInt(m[1], 10) : Number.MAX_SAFE_INTEGER;
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}
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// ─────────────────────────────────────────────────────────────────────────────
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// 按楼层分组装配 orphan chunks（修复上下文重复）
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// ─────────────────────────────────────────────────────────────────────────────
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function assembleOrphansByFloor(orphanCandidates, contextChunksByFloor, budget) {
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if (!orphanCandidates?.length) {
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return { lines: [], l0Count: 0, contextPairsCount: 0 };
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}
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// 1. 按楼层分组
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const byFloor = new Map();
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for (const c of orphanCandidates) {
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const arr = byFloor.get(c.floor) || [];
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arr.push(c);
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byFloor.set(c.floor, arr);
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}
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// 2. 楼层内按 chunkIdx 排序
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for (const [, chunks] of byFloor) {
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chunks.sort((a, b) => (a.chunkIdx ?? 0) - (b.chunkIdx ?? 0));
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}
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// 3. 按楼层顺序装配
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const floorsSorted = Array.from(byFloor.keys()).sort((a, b) => a - b);
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const lines = [];
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let l0Count = 0;
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let contextPairsCount = 0;
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for (const floor of floorsSorted) {
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const chunks = byFloor.get(floor);
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if (!chunks?.length) continue;
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// 分离 L0 和 L1
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const l0Chunks = chunks.filter(c => c.isL0);
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const l1Chunks = chunks.filter(c => !c.isL0);
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// L0 直接输出（不需要上下文）
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for (const c of l0Chunks) {
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const line = formatChunkFullLine(c);
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if (!pushWithBudget(lines, line, budget)) {
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return { lines, l0Count, contextPairsCount };
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}
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l0Count++;
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}
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// L1 按楼层统一处理
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if (l1Chunks.length > 0) {
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const firstChunk = l1Chunks[0];
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const pairFloor = getContextFloor(firstChunk);
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const pairCandidates = contextChunksByFloor.get(pairFloor) || [];
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const contextChunk = pickContextChunk(pairCandidates, firstChunk);
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// 上下文在前
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if (contextChunk && contextChunk.floor < floor) {
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const contextLine = formatContextChunkLine(contextChunk, true);
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if (!pushWithBudget(lines, contextLine, budget)) {
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return { lines, l0Count, contextPairsCount };
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}
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contextPairsCount++;
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}
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// 输出该楼层所有 L1 chunks
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for (const c of l1Chunks) {
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const line = formatChunkFullLine(c);
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if (!pushWithBudget(lines, line, budget)) {
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return { lines, l0Count, contextPairsCount };
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}
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}
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// 上下文在后
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if (contextChunk && contextChunk.floor > floor) {
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const contextLine = formatContextChunkLine(contextChunk, false);
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if (!pushWithBudget(lines, contextLine, budget)) {
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return { lines, l0Count, contextPairsCount };
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}
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contextPairsCount++;
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}
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}
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}
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return { lines, l0Count, contextPairsCount };
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}
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// ─────────────────────────────────────────────────────────────────────────────
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// 非向量模式
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// ─────────────────────────────────────────────────────────────────────────────
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function buildNonVectorPrompt(store) {
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const data = store.json || {};
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const sections = [];
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const allFacts = getFacts();
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const factLines = allFacts
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.filter(f => !f.retracted)
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.sort((a, b) => (b.since || 0) - (a.since || 0))
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.map(f => {
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const since = f.since ? ` (#${f.since + 1})` : '';
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if (isRelationFact(f) && f.trend) {
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return `- ${f.s} ${f.p}: ${f.o} [${f.trend}]${since}`;
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}
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return `- ${f.s}的${f.p}: ${f.o}${since}`;
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});
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if (factLines.length) {
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sections.push(`[定了的事] 已确立的事实\n${factLines.join("\n")}`);
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}
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if (data.events?.length) {
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const lines = data.events.map((ev, i) => {
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const time = ev.timeLabel || "";
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const title = ev.title || "";
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const people = (ev.participants || []).join(" / ");
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const summary = cleanSummary(ev.summary);
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const header = time ? `${i + 1}.【${time}】${title || people}` : `${i + 1}. ${title || people}`;
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return `${header}\n ${summary}`;
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});
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sections.push(`[剧情记忆]\n\n${lines.join("\n\n")}`);
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}
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if (data.arcs?.length) {
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const lines = data.arcs.map(formatArcLine);
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sections.push(`[人物弧光]\n${lines.join("\n")}`);
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}
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if (!sections.length) return "";
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return (
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`${buildSystemPreamble()}\n` +
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`<剧情记忆>\n\n${sections.join("\n\n")}\n\n</剧情记忆>\n` +
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`${buildPostscript()}`
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);
|
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}
|
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|
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export function buildNonVectorPromptText() {
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if (!getSettings().storySummary?.enabled) {
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return "";
|
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}
|
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|
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const store = getSummaryStore();
|
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if (!store?.json) {
|
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return "";
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}
|
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|
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let text = buildNonVectorPrompt(store);
|
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if (!text.trim()) {
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return "";
|
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}
|
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|
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const cfg = getSummaryPanelConfig();
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if (cfg.trigger?.wrapperHead) text = cfg.trigger.wrapperHead + "\n" + text;
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if (cfg.trigger?.wrapperTail) text = text + "\n" + cfg.trigger.wrapperTail;
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return text;
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}
|
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|
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// ─────────────────────────────────────────────────────────────────────────────
|
||
// 向量模式：预算装配
|
||
// ─────────────────────────────────────────────────────────────────────────────
|
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|
||
async function buildVectorPrompt(store, recallResult, causalById, focusEntities = [], meta = null, metrics = null) {
|
||
const T_Start = performance.now();
|
||
|
||
const { chatId } = getContext();
|
||
const data = store.json || {};
|
||
const total = { used: 0, max: MAIN_BUDGET_MAX };
|
||
|
||
const assembled = {
|
||
facts: { lines: [], tokens: 0 },
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arcs: { lines: [], tokens: 0 },
|
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events: { direct: [], similar: [] },
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orphans: { lines: [], tokens: 0 },
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recentOrphans: { lines: [], tokens: 0 },
|
||
};
|
||
|
||
const injectionStats = {
|
||
budget: { max: TOTAL_BUDGET_MAX, used: 0 },
|
||
facts: { count: 0, tokens: 0, filtered: 0 },
|
||
arcs: { count: 0, tokens: 0 },
|
||
events: { selected: 0, tokens: 0 },
|
||
evidence: { attached: 0, tokens: 0 },
|
||
orphans: { injected: 0, tokens: 0, l0Count: 0, contextPairs: 0 },
|
||
};
|
||
|
||
const recentOrphanStats = {
|
||
injected: 0,
|
||
tokens: 0,
|
||
floorRange: "N/A",
|
||
contextPairs: 0,
|
||
};
|
||
|
||
const details = {
|
||
eventList: [],
|
||
directCount: 0,
|
||
similarCount: 0,
|
||
};
|
||
|
||
// ═══════════════════════════════════════════════════════════════════════
|
||
// [优先级 1] 世界约束
|
||
// ═══════════════════════════════════════════════════════════════════════
|
||
|
||
const T_L1_Start = performance.now();
|
||
|
||
const allFacts = getFacts();
|
||
const knownCharacters = getKnownCharacters(store);
|
||
const factLines = formatFactsForInjection(allFacts, focusEntities, knownCharacters);
|
||
|
||
if (metrics) {
|
||
metrics.l1.factsTotal = allFacts.length;
|
||
metrics.l1.factsFiltered = allFacts.length - factLines.length;
|
||
}
|
||
|
||
if (factLines.length) {
|
||
const l1Budget = { used: 0, max: Math.min(L1_MAX, total.max - total.used) };
|
||
for (const line of factLines) {
|
||
if (!pushWithBudget(assembled.facts.lines, line, l1Budget)) break;
|
||
}
|
||
assembled.facts.tokens = l1Budget.used;
|
||
total.used += l1Budget.used;
|
||
injectionStats.facts.count = assembled.facts.lines.length;
|
||
injectionStats.facts.tokens = l1Budget.used;
|
||
injectionStats.facts.filtered = allFacts.length - factLines.length;
|
||
|
||
if (metrics) {
|
||
metrics.l1.factsInjected = assembled.facts.lines.length;
|
||
metrics.l1.tokens = l1Budget.used;
|
||
metrics.l1.samples = assembled.facts.lines.slice(0, 3).map(line =>
|
||
line.length > 60 ? line.slice(0, 60) + '...' : line
|
||
);
|
||
metrics.timing.l1Constraints = Math.round(performance.now() - T_L1_Start);
|
||
}
|
||
} else if (metrics) {
|
||
metrics.timing.l1Constraints = Math.round(performance.now() - T_L1_Start);
|
||
}
|
||
|
||
// ═══════════════════════════════════════════════════════════════════════
|
||
// [优先级 2] 人物弧光
|
||
// ═══════════════════════════════════════════════════════════════════════
|
||
|
||
if (data.arcs?.length && total.used < total.max) {
|
||
const { name1 } = getContext();
|
||
const userName = String(name1 || "").trim();
|
||
|
||
const relevant = new Set(
|
||
[userName, ...(focusEntities || [])]
|
||
.map(s => String(s || "").trim())
|
||
.filter(Boolean)
|
||
);
|
||
|
||
const filtered = (data.arcs || []).filter(a => {
|
||
const n = String(a?.name || "").trim();
|
||
return n && relevant.has(n);
|
||
});
|
||
|
||
if (filtered.length) {
|
||
const arcBudget = { used: 0, max: Math.min(ARCS_MAX, total.max - total.used) };
|
||
for (const a of filtered) {
|
||
const line = formatArcLine(a);
|
||
if (!pushWithBudget(assembled.arcs.lines, line, arcBudget)) break;
|
||
}
|
||
assembled.arcs.tokens = arcBudget.used;
|
||
total.used += arcBudget.used;
|
||
injectionStats.arcs.count = assembled.arcs.lines.length;
|
||
injectionStats.arcs.tokens = arcBudget.used;
|
||
}
|
||
}
|
||
|
||
// ═══════════════════════════════════════════════════════════════════════
|
||
// [优先级 3] 事件 + 证据
|
||
// ═══════════════════════════════════════════════════════════════════════
|
||
|
||
const recalledEvents = (recallResult?.events || []).filter(e => e?.event?.summary);
|
||
const chunks = recallResult?.chunks || [];
|
||
const usedChunkIds = new Set();
|
||
|
||
function pickBestChunkForEvent(eventObj) {
|
||
const range = parseFloorRange(eventObj?.summary);
|
||
if (!range) return null;
|
||
|
||
let best = null;
|
||
for (const c of chunks) {
|
||
if (usedChunkIds.has(c.chunkId)) continue;
|
||
if (c.floor < range.start || c.floor > range.end) continue;
|
||
|
||
if (!best) {
|
||
best = c;
|
||
} else if (c.isL0 && !best.isL0) {
|
||
best = c;
|
||
} else if (c.isL0 === best.isL0 && (c.chunkIdx ?? 0) < (best.chunkIdx ?? 0)) {
|
||
best = c;
|
||
}
|
||
}
|
||
return best;
|
||
}
|
||
|
||
function formatEventWithEvidence(e, idx, chunk) {
|
||
const ev = e.event || {};
|
||
const time = ev.timeLabel || "";
|
||
const title = String(ev.title || "").trim();
|
||
const people = (ev.participants || []).join(" / ").trim();
|
||
const summary = cleanSummary(ev.summary);
|
||
|
||
const displayTitle = title || people || ev.id || "事件";
|
||
const header = time ? `${idx}.【${time}】${displayTitle}` : `${idx}. ${displayTitle}`;
|
||
|
||
const lines = [header];
|
||
if (people && displayTitle !== people) lines.push(` ${people}`);
|
||
lines.push(` ${summary}`);
|
||
|
||
for (const cid of ev.causedBy || []) {
|
||
const c = causalById?.get(cid);
|
||
if (c) lines.push(formatCausalEventLine(c, causalById));
|
||
}
|
||
|
||
if (chunk) {
|
||
lines.push(` ${formatChunkFullLine(chunk)}`);
|
||
}
|
||
|
||
return lines.join("\n");
|
||
}
|
||
|
||
const candidates = [...recalledEvents].sort((a, b) => (b.similarity || 0) - (a.similarity || 0));
|
||
|
||
const selectedDirect = [];
|
||
const selectedSimilar = [];
|
||
|
||
for (let candidateRank = 0; candidateRank < candidates.length; candidateRank++) {
|
||
const e = candidates[candidateRank];
|
||
|
||
if (total.used >= total.max) break;
|
||
|
||
const isDirect = e._recallType === "DIRECT";
|
||
|
||
const bestChunk = pickBestChunkForEvent(e.event);
|
||
|
||
let text = formatEventWithEvidence(e, 0, bestChunk);
|
||
let cost = estimateTokens(text);
|
||
let hasEvidence = !!bestChunk;
|
||
let chosenChunk = bestChunk || null;
|
||
|
||
if (total.used + cost > total.max) {
|
||
text = formatEventWithEvidence(e, 0, null);
|
||
cost = estimateTokens(text);
|
||
hasEvidence = false;
|
||
chosenChunk = null;
|
||
|
||
if (total.used + cost > total.max) {
|
||
continue;
|
||
}
|
||
}
|
||
|
||
if (isDirect) {
|
||
selectedDirect.push({ event: e.event, text, tokens: cost, chunk: chosenChunk, hasEvidence, candidateRank });
|
||
} else {
|
||
selectedSimilar.push({ event: e.event, text, tokens: cost, chunk: chosenChunk, hasEvidence, candidateRank });
|
||
}
|
||
|
||
injectionStats.events.selected++;
|
||
total.used += cost;
|
||
|
||
if (hasEvidence && bestChunk) {
|
||
const chunkLine = formatChunkFullLine(bestChunk);
|
||
const ct = estimateTokens(chunkLine);
|
||
injectionStats.evidence.attached++;
|
||
injectionStats.evidence.tokens += ct;
|
||
usedChunkIds.add(bestChunk.chunkId);
|
||
|
||
injectionStats.events.tokens += Math.max(0, cost - ct);
|
||
} else {
|
||
injectionStats.events.tokens += cost;
|
||
}
|
||
|
||
details.eventList.push({
|
||
title: e.event?.title || e.event?.id,
|
||
isDirect,
|
||
hasEvidence,
|
||
tokens: cost,
|
||
similarity: e.similarity || 0,
|
||
hasL0Evidence: bestChunk?.isL0 || false,
|
||
});
|
||
}
|
||
|
||
selectedDirect.sort((a, b) => getEventSortKey(a.event) - getEventSortKey(b.event));
|
||
selectedSimilar.sort((a, b) => getEventSortKey(a.event) - getEventSortKey(b.event));
|
||
|
||
const selectedDirectTexts = selectedDirect.map((it, i) => {
|
||
const numbered = renumberEventText(it.text, i + 1);
|
||
return it.candidateRank < TOP_N_STAR ? `⭐${numbered}` : numbered;
|
||
});
|
||
|
||
const selectedSimilarTexts = selectedSimilar.map((it, i) => {
|
||
const numbered = renumberEventText(it.text, i + 1);
|
||
return it.candidateRank < TOP_N_STAR ? `⭐${numbered}` : numbered;
|
||
});
|
||
|
||
details.directCount = selectedDirect.length;
|
||
details.similarCount = selectedSimilar.length;
|
||
assembled.events.direct = selectedDirectTexts;
|
||
assembled.events.similar = selectedSimilarTexts;
|
||
|
||
// ═══════════════════════════════════════════════════════════════════════
|
||
// [优先级 4] 远期片段（已总结范围的 orphan chunks）
|
||
// ═══════════════════════════════════════════════════════════════════════
|
||
|
||
const lastSummarized = store.lastSummarizedMesId ?? -1;
|
||
const lastChunkFloor = meta?.lastChunkFloor ?? -1;
|
||
const keepVisible = store.keepVisibleCount ?? 3;
|
||
|
||
const orphanContextFloors = new Set();
|
||
const orphanCandidates = chunks
|
||
.filter(c => !usedChunkIds.has(c.chunkId))
|
||
.filter(c => c.floor <= lastSummarized);
|
||
|
||
for (const c of orphanCandidates) {
|
||
if (c.isL0) continue;
|
||
const pairFloor = getContextFloor(c);
|
||
if (pairFloor >= 0) orphanContextFloors.add(pairFloor);
|
||
}
|
||
|
||
let contextChunksByFloor = new Map();
|
||
if (chatId && orphanContextFloors.size > 0) {
|
||
try {
|
||
const contextChunks = await getChunksByFloors(chatId, Array.from(orphanContextFloors));
|
||
for (const pc of contextChunks) {
|
||
if (!contextChunksByFloor.has(pc.floor)) {
|
||
contextChunksByFloor.set(pc.floor, []);
|
||
}
|
||
contextChunksByFloor.get(pc.floor).push(pc);
|
||
}
|
||
} catch (e) {
|
||
xbLog.warn(MODULE_ID, "获取配对chunks失败", e);
|
||
}
|
||
}
|
||
|
||
if (orphanCandidates.length && total.used < total.max) {
|
||
const l1Budget = { used: 0, max: Math.min(ORPHAN_MAX, total.max - total.used) };
|
||
|
||
const result = assembleOrphansByFloor(
|
||
orphanCandidates.sort((a, b) => (a.floor - b.floor) || ((a.chunkIdx ?? 0) - (b.chunkIdx ?? 0))),
|
||
contextChunksByFloor,
|
||
l1Budget
|
||
);
|
||
|
||
assembled.orphans.lines = result.lines;
|
||
assembled.orphans.tokens = l1Budget.used;
|
||
total.used += l1Budget.used;
|
||
|
||
injectionStats.orphans.injected = result.lines.length;
|
||
injectionStats.orphans.tokens = l1Budget.used;
|
||
injectionStats.orphans.l0Count = result.l0Count;
|
||
injectionStats.orphans.contextPairs = result.contextPairsCount;
|
||
}
|
||
|
||
// ═══════════════════════════════════════════════════════════════════════
|
||
// [独立预算] 待整理（未总结范围）
|
||
// ═══════════════════════════════════════════════════════════════════════
|
||
|
||
const recentStart = lastSummarized + 1;
|
||
const recentEnd = lastChunkFloor - keepVisible;
|
||
|
||
if (chunks.length && recentEnd >= recentStart) {
|
||
const recentOrphanCandidates = chunks
|
||
.filter(c => !usedChunkIds.has(c.chunkId))
|
||
.filter(c => c.floor >= recentStart && c.floor <= recentEnd);
|
||
|
||
const recentContextFloors = new Set();
|
||
for (const c of recentOrphanCandidates) {
|
||
if (c.isL0) continue;
|
||
const pairFloor = getContextFloor(c);
|
||
if (pairFloor >= 0) recentContextFloors.add(pairFloor);
|
||
}
|
||
|
||
if (chatId && recentContextFloors.size > 0) {
|
||
const newFloors = Array.from(recentContextFloors).filter(f => !contextChunksByFloor.has(f));
|
||
if (newFloors.length > 0) {
|
||
try {
|
||
const newContextChunks = await getChunksByFloors(chatId, newFloors);
|
||
for (const pc of newContextChunks) {
|
||
if (!contextChunksByFloor.has(pc.floor)) {
|
||
contextChunksByFloor.set(pc.floor, []);
|
||
}
|
||
contextChunksByFloor.get(pc.floor).push(pc);
|
||
}
|
||
} catch (e) {
|
||
xbLog.warn(MODULE_ID, "获取近期配对chunks失败", e);
|
||
}
|
||
}
|
||
}
|
||
|
||
if (recentOrphanCandidates.length) {
|
||
const recentBudget = { used: 0, max: RECENT_ORPHAN_MAX };
|
||
|
||
const result = assembleOrphansByFloor(
|
||
recentOrphanCandidates.sort((a, b) => (a.floor - b.floor) || ((a.chunkIdx ?? 0) - (b.chunkIdx ?? 0))),
|
||
contextChunksByFloor,
|
||
recentBudget
|
||
);
|
||
|
||
assembled.recentOrphans.lines = result.lines;
|
||
assembled.recentOrphans.tokens = recentBudget.used;
|
||
|
||
recentOrphanStats.injected = result.lines.length;
|
||
recentOrphanStats.tokens = recentBudget.used;
|
||
recentOrphanStats.floorRange = `${recentStart + 1}~${recentEnd + 1}楼`;
|
||
recentOrphanStats.contextPairs = result.contextPairsCount;
|
||
}
|
||
}
|
||
|
||
// ═══════════════════════════════════════════════════════════════════════
|
||
// 按注入顺序拼接 sections
|
||
// ═══════════════════════════════════════════════════════════════════════
|
||
|
||
const T_L4_Start = performance.now();
|
||
|
||
const sections = [];
|
||
|
||
if (assembled.facts.lines.length) {
|
||
sections.push(`[定了的事] 已确立的事实\n${assembled.facts.lines.join("\n")}`);
|
||
}
|
||
if (assembled.events.direct.length) {
|
||
sections.push(`[印象深的事] 记得很清楚\n\n${assembled.events.direct.join("\n\n")}`);
|
||
}
|
||
if (assembled.events.similar.length) {
|
||
sections.push(`[好像有关的事] 听说过或有点模糊\n\n${assembled.events.similar.join("\n\n")}`);
|
||
}
|
||
if (assembled.orphans.lines.length) {
|
||
sections.push(`[更早以前] 记忆里残留的老画面\n${assembled.orphans.lines.join("\n")}`);
|
||
}
|
||
if (assembled.recentOrphans.lines.length) {
|
||
sections.push(`[近期] 清晰但还没整理\n${assembled.recentOrphans.lines.join("\n")}`);
|
||
}
|
||
if (assembled.arcs.lines.length) {
|
||
sections.push(`[这些人] 他们的弧光\n${assembled.arcs.lines.join("\n")}`);
|
||
}
|
||
|
||
if (!sections.length) {
|
||
if (metrics) {
|
||
metrics.timing.l3Assembly = Math.round(performance.now() - T_Start - (metrics.timing.l1Constraints || 0));
|
||
metrics.timing.l4Formatting = 0;
|
||
}
|
||
return { promptText: "", injectionLogText: "", injectionStats, metrics };
|
||
}
|
||
|
||
const promptText =
|
||
`${buildSystemPreamble()}\n` +
|
||
`<剧情记忆>\n\n${sections.join("\n\n")}\n\n</剧情记忆>\n` +
|
||
`${buildPostscript()}`;
|
||
|
||
if (metrics) {
|
||
metrics.l4.sectionsIncluded = [];
|
||
if (assembled.facts.lines.length) metrics.l4.sectionsIncluded.push('constraints');
|
||
if (assembled.events.direct.length) metrics.l4.sectionsIncluded.push('direct_events');
|
||
if (assembled.events.similar.length) metrics.l4.sectionsIncluded.push('similar_events');
|
||
if (assembled.orphans.lines.length) metrics.l4.sectionsIncluded.push('orphans');
|
||
if (assembled.recentOrphans.lines.length) metrics.l4.sectionsIncluded.push('recent_orphans');
|
||
if (assembled.arcs.lines.length) metrics.l4.sectionsIncluded.push('arcs');
|
||
|
||
metrics.l4.formattingTime = Math.round(performance.now() - T_L4_Start);
|
||
metrics.timing.l4Formatting = metrics.l4.formattingTime;
|
||
|
||
metrics.budget.total = total.used + (assembled.recentOrphans.tokens || 0);
|
||
metrics.budget.limit = TOTAL_BUDGET_MAX;
|
||
metrics.budget.utilization = Math.round(metrics.budget.total / TOTAL_BUDGET_MAX * 100);
|
||
metrics.budget.breakdown = {
|
||
constraints: assembled.facts.tokens,
|
||
events: injectionStats.events.tokens + injectionStats.evidence.tokens,
|
||
chunks: injectionStats.orphans.tokens,
|
||
recentOrphans: recentOrphanStats.tokens || 0,
|
||
arcs: assembled.arcs.tokens,
|
||
};
|
||
|
||
metrics.l3.tokens = injectionStats.orphans.tokens + (recentOrphanStats.tokens || 0);
|
||
metrics.l3.contextPairsAdded = injectionStats.orphans.contextPairs + recentOrphanStats.contextPairs;
|
||
metrics.l3.assemblyTime = Math.round(performance.now() - T_Start - (metrics.timing.l1Constraints || 0) - metrics.l4.formattingTime);
|
||
metrics.timing.l3Assembly = metrics.l3.assemblyTime;
|
||
|
||
const totalFacts = allFacts.length;
|
||
metrics.quality.constraintCoverage = totalFacts > 0
|
||
? Math.round(assembled.facts.lines.length / totalFacts * 100)
|
||
: 100;
|
||
metrics.quality.eventPrecisionProxy = metrics.l2?.similarityDistribution?.mean || 0;
|
||
|
||
const totalChunks = metrics.l3.chunksSelected || 0;
|
||
const chunksWithEvents = injectionStats.evidence.attached;
|
||
metrics.quality.evidenceDensity = totalChunks > 0
|
||
? Math.round(chunksWithEvents / totalChunks * 100)
|
||
: 0;
|
||
|
||
metrics.quality.potentialIssues = detectIssues(metrics);
|
||
}
|
||
|
||
return { promptText, injectionLogText: "", injectionStats, metrics };
|
||
}
|
||
|
||
// ─────────────────────────────────────────────────────────────────────────────
|
||
// 因果证据补充
|
||
// ─────────────────────────────────────────────────────────────────────────────
|
||
|
||
async function attachEvidenceToCausalEvents(causalEvents, eventVectorMap, chunkVectorMap, chunksMap) {
|
||
for (const c of causalEvents) {
|
||
c._evidenceChunk = null;
|
||
|
||
const ev = c.event;
|
||
if (!ev?.id) continue;
|
||
|
||
const evVec = eventVectorMap.get(ev.id);
|
||
if (!evVec?.length) continue;
|
||
|
||
const range = parseFloorRange(ev.summary);
|
||
if (!range) continue;
|
||
|
||
const candidateChunks = [];
|
||
for (const [chunkId, chunk] of chunksMap) {
|
||
if (chunk.floor >= range.start && chunk.floor <= range.end) {
|
||
const vec = chunkVectorMap.get(chunkId);
|
||
if (vec?.length) candidateChunks.push({ chunk, vec });
|
||
}
|
||
}
|
||
if (!candidateChunks.length) continue;
|
||
|
||
let best = null;
|
||
let bestSim = -1;
|
||
for (const { chunk, vec } of candidateChunks) {
|
||
const sim = cosineSimilarity(evVec, vec);
|
||
if (sim > bestSim) {
|
||
bestSim = sim;
|
||
best = chunk;
|
||
}
|
||
}
|
||
|
||
if (best && bestSim > 0.3) {
|
||
c._evidenceChunk = {
|
||
floor: best.floor,
|
||
speaker: best.speaker,
|
||
text: best.text,
|
||
similarity: bestSim,
|
||
};
|
||
}
|
||
}
|
||
}
|
||
|
||
// ─────────────────────────────────────────────────────────────────────────────
|
||
// 向量模式：召回 + 注入
|
||
// ─────────────────────────────────────────────────────────────────────────────
|
||
|
||
export async function buildVectorPromptText(excludeLastAi = false, hooks = {}) {
|
||
const { postToFrame = null, echo = null, pendingUserMessage = null } = hooks;
|
||
|
||
if (!getSettings().storySummary?.enabled) {
|
||
return { text: "", logText: "" };
|
||
}
|
||
|
||
const { chat } = getContext();
|
||
const store = getSummaryStore();
|
||
|
||
if (!store?.json) {
|
||
return { text: "", logText: "" };
|
||
}
|
||
|
||
const allEvents = store.json.events || [];
|
||
const lastIdx = store.lastSummarizedMesId ?? 0;
|
||
const length = chat?.length || 0;
|
||
|
||
if (lastIdx >= length) {
|
||
return { text: "", logText: "" };
|
||
}
|
||
|
||
const vectorCfg = getVectorConfig();
|
||
if (!vectorCfg?.enabled) {
|
||
return { text: "", logText: "" };
|
||
}
|
||
|
||
const { chatId } = getContext();
|
||
const meta = chatId ? await getMeta(chatId) : null;
|
||
|
||
let recallResult = null;
|
||
let causalById = new Map();
|
||
|
||
try {
|
||
const queryText = buildQueryText(chat, 2, excludeLastAi);
|
||
recallResult = await recallMemory(queryText, allEvents, vectorCfg, {
|
||
excludeLastAi,
|
||
pendingUserMessage,
|
||
});
|
||
|
||
recallResult = {
|
||
...recallResult,
|
||
events: recallResult?.events || [],
|
||
chunks: recallResult?.chunks || [],
|
||
causalEvents: recallResult?.causalEvents || [],
|
||
focusEntities: recallResult?.focusEntities || [],
|
||
logText: recallResult?.logText || "",
|
||
metrics: recallResult?.metrics || null,
|
||
};
|
||
|
||
const causalEvents = recallResult.causalEvents || [];
|
||
if (causalEvents.length > 0) {
|
||
if (chatId) {
|
||
try {
|
||
const floors = new Set();
|
||
for (const c of causalEvents) {
|
||
const r = parseFloorRange(c.event?.summary);
|
||
if (!r) continue;
|
||
for (let f = r.start; f <= r.end; f++) floors.add(f);
|
||
}
|
||
|
||
const [chunksList, chunkVecs, eventVecs] = await Promise.all([
|
||
getChunksByFloors(chatId, Array.from(floors)),
|
||
getAllChunkVectors(chatId),
|
||
getAllEventVectors(chatId),
|
||
]);
|
||
|
||
const chunksMap = new Map(chunksList.map(c => [c.chunkId, c]));
|
||
const chunkVectorMap = new Map(chunkVecs.map(v => [v.chunkId, v.vector]));
|
||
const eventVectorMap = new Map(eventVecs.map(v => [v.eventId, v.vector]));
|
||
|
||
await attachEvidenceToCausalEvents(causalEvents, eventVectorMap, chunkVectorMap, chunksMap);
|
||
} catch (e) {
|
||
xbLog.warn(MODULE_ID, "Causal evidence attachment failed", e);
|
||
}
|
||
}
|
||
}
|
||
|
||
causalById = new Map(
|
||
recallResult.causalEvents
|
||
.map(c => [c?.event?.id, c])
|
||
.filter(x => x[0])
|
||
);
|
||
} catch (e) {
|
||
xbLog.error(MODULE_ID, "向量召回失败", e);
|
||
|
||
if (echo && canNotifyRecallFail()) {
|
||
const msg = String(e?.message || "未知错误").replace(/\s+/g, " ").slice(0, 200);
|
||
await echo(`/echo severity=warning 向量召回失败：${msg}`);
|
||
}
|
||
|
||
if (postToFrame) {
|
||
postToFrame({
|
||
type: "RECALL_LOG",
|
||
text: `\n[Vector Recall Failed]\n${String(e?.stack || e?.message || e)}\n`,
|
||
});
|
||
}
|
||
|
||
return { text: "", logText: `\n[Vector Recall Failed]\n${String(e?.stack || e?.message || e)}\n` };
|
||
}
|
||
|
||
const hasUseful =
|
||
(recallResult?.events?.length || 0) > 0 ||
|
||
(recallResult?.chunks?.length || 0) > 0 ||
|
||
(recallResult?.causalEvents?.length || 0) > 0;
|
||
|
||
if (!hasUseful) {
|
||
if (echo && canNotifyRecallFail()) {
|
||
await echo(
|
||
"/echo severity=warning 向量召回失败：没有可用召回结果（请先在面板中生成向量，或检查指纹不匹配）"
|
||
);
|
||
}
|
||
if (postToFrame) {
|
||
postToFrame({
|
||
type: "RECALL_LOG",
|
||
text: "\n[Vector Recall Empty]\nNo recall candidates / vectors not ready.\n",
|
||
});
|
||
}
|
||
return { text: "", logText: "\n[Vector Recall Empty]\nNo recall candidates / vectors not ready.\n" };
|
||
}
|
||
|
||
const { promptText, metrics: promptMetrics } = await buildVectorPrompt(
|
||
store,
|
||
recallResult,
|
||
causalById,
|
||
recallResult?.focusEntities || [],
|
||
meta,
|
||
recallResult?.metrics || null
|
||
);
|
||
|
||
const cfg = getSummaryPanelConfig();
|
||
let finalText = String(promptText || "");
|
||
if (cfg.trigger?.wrapperHead) finalText = cfg.trigger.wrapperHead + "\n" + finalText;
|
||
if (cfg.trigger?.wrapperTail) finalText = finalText + "\n" + cfg.trigger.wrapperTail;
|
||
|
||
const metricsLogText = promptMetrics ? formatMetricsLog(promptMetrics) : '';
|
||
|
||
if (postToFrame) {
|
||
postToFrame({ type: "RECALL_LOG", text: metricsLogText });
|
||
}
|
||
|
||
return { text: finalText, logText: metricsLogText };
|
||
}
|