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fix: qwen thinking toggle and recall log styles

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bielie
2026-02-08 18:12:55 +08:00
parent b4e181caea
commit 8fdce7b9a1
4 changed files with 147 additions and 324 deletions
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410
modules/story-summary/generate/prompt.js
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@@ -1,5 +1,5 @@
// ═══════════════════════════════════════════════════════════════════════════
// Story Summary - Prompt Injection (v2 - DSL 版)
// Story Summary - Prompt Injection (v3 - DSL 版 + Orphan 分组修复)
// - 仅负责"构建注入文本"，不负责写入 extension_prompts
// - 注入发生在 story-summary.js：GENERATION_STARTED 时写入 extension_prompts
// ═══════════════════════════════════════════════════════════════════════════
@@ -23,10 +23,6 @@ const MODULE_ID = "summaryPrompt";
let lastRecallFailAt = 0;
const RECALL_FAIL_COOLDOWN_MS = 10_000;
/**
* 检查是否可以通知召回失败
* @returns {boolean}
*/
function canNotifyRecallFail() {
const now = Date.now();
if (now - lastRecallFailAt < RECALL_FAIL_COOLDOWN_MS) return false;
@@ -50,11 +46,6 @@ const TOP_N_STAR = 5;
// 工具函数
// ─────────────────────────────────────────────────────────────────────────────
/**
* 估算 token 数量
* @param {string} text - 文本
* @returns {number} token 数
*/
function estimateTokens(text) {
if (!text) return 0;
const s = String(text);
@@ -62,13 +53,6 @@ function estimateTokens(text) {
return Math.ceil(zh + (s.length - zh) / 4);
}
/**
* 带预算控制的行推入
* @param {Array} lines - 行数组
* @param {string} text - 文本
* @param {object} state - 预算状态 {used, max}
* @returns {boolean} 是否成功
*/
function pushWithBudget(lines, text, state) {
const t = estimateTokens(text);
if (state.used + t > state.max) return false;
@@ -77,12 +61,6 @@ function pushWithBudget(lines, text, state) {
return true;
}
/**
* 计算余弦相似度
* @param {Array} a - 向量 a
* @param {Array} b - 向量 b
* @returns {number} 相似度
*/
function cosineSimilarity(a, b) {
if (!a?.length || !b?.length || a.length !== b.length) return 0;
let dot = 0, nA = 0, nB = 0;
@@ -94,11 +72,6 @@ function cosineSimilarity(a, b) {
return nA && nB ? dot / (Math.sqrt(nA) * Math.sqrt(nB)) : 0;
}
/**
* 解析楼层范围
* @param {string} summary - 摘要文本
* @returns {object|null} {start, end}
*/
function parseFloorRange(summary) {
if (!summary) return null;
const match = String(summary).match(/\(#(\d+)(?:-(\d+))?\)/);
@@ -108,22 +81,12 @@ function parseFloorRange(summary) {
return { start, end };
}
/**
* 清理摘要中的楼层标记
* @param {string} summary - 摘要文本
* @returns {string} 清理后的文本
*/
function cleanSummary(summary) {
return String(summary || "")
.replace(/\s*\(#\d+(?:-\d+)?\)\s*$/, "")
.trim();
}
/**
* 规范化字符串（用于比较）
* @param {string} s - 字符串
* @returns {string} 规范化后的字符串
*/
function normalize(s) {
return String(s || '')
.normalize('NFKC')
@@ -136,22 +99,11 @@ function normalize(s) {
// 上下文配对工具函数
// ─────────────────────────────────────────────────────────────────────────────
/**
* 获取上下文楼层
* @param {object} chunk - chunk 对象
* @returns {number} 配对楼层，-1 表示无效
*/
function getContextFloor(chunk) {
if (chunk.isL0) return -1;
return chunk.isUser ? chunk.floor + 1 : chunk.floor - 1;
}
/**
* 选择配对 chunk
* @param {Array} candidates - 候选 chunks
* @param {object} mainChunk - 主 chunk
* @returns {object|null} 配对 chunk
*/
function pickContextChunk(candidates, mainChunk) {
if (!candidates?.length) return null;
const targetIsUser = !mainChunk.isUser;
@@ -160,12 +112,6 @@ function pickContextChunk(candidates, mainChunk) {
return candidates[0];
}
/**
* 格式化上下文 chunk 行
* @param {object} chunk - chunk 对象
* @param {boolean} isAbove - 是否在主 chunk 上方
* @returns {string} 格式化的行
*/
function formatContextChunkLine(chunk, isAbove) {
const { name1, name2 } = getContext();
const speaker = chunk.isUser ? (name1 || "用户") : (chunk.speaker || name2 || "角色");
@@ -178,10 +124,6 @@ function formatContextChunkLine(chunk, isAbove) {
// 系统前导与后缀
// ─────────────────────────────────────────────────────────────────────────────
/**
* 构建系统前导
* @returns {string}
*/
function buildSystemPreamble() {
return [
"以上是还留在眼前的对话",
@@ -193,10 +135,6 @@ function buildSystemPreamble() {
].join("\n");
}
/**
* 构建后缀
* @returns {string}
*/
function buildPostscript() {
return [
"",
@@ -208,28 +146,20 @@ function buildPostscript() {
// L1 Facts 分层过滤
// ─────────────────────────────────────────────────────────────────────────────
/**
* 从 store 获取所有已知角色名
* @param {object} store - summary store
* @returns {Set<string>} 角色名集合（规范化后）
*/
function getKnownCharacters(store) {
const names = new Set();
// 从 arcs 获取
const arcs = store?.json?.arcs || [];
for (const a of arcs) {
if (a.name) names.add(normalize(a.name));
}
// 从 characters.main 获取
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));
}
// 从当前角色获取
const { name1, name2 } = getContext();
if (name1) names.add(normalize(name1));
if (name2) names.add(normalize(name2));
@@ -237,77 +167,42 @@ function getKnownCharacters(store) {
return names;
}
/**
* 解析关系类 fact 的目标人物
* @param {string} predicate - 谓词，如 "对蓝袖的看法"
* @returns {string|null} 目标人物名
*/
function parseRelationTarget(predicate) {
const match = String(predicate || '').match(/^对(.+)的/);
return match ? match[1] : null;
}
/**
* 过滤 facts（分层策略）
*
* 规则：
* - isState=true：全量保留
* - 关系类（谓词匹配 /^对.+的/）：from 或 to 在 focus 中
* - 人物状态类（主体是已知角色名）：主体在 focus 中
* - 其他（物品/地点/规则）：全量保留
*
* @param {Array} facts - 所有 facts
* @param {Array} focusEntities - 焦点实体
* @param {Set} knownCharacters - 已知角色名集合
* @returns {Array} 过滤后的 facts
*/
function filterFactsByRelevance(facts, focusEntities, knownCharacters) {
if (!facts?.length) return [];
const focusSet = new Set((focusEntities || []).map(normalize));
return facts.filter(f => {
// 1. isState=true：全量保留
if (f._isState === true) return true;
// 2. 关系类：from 或 to 在 focus 中
if (isRelationFact(f)) {
const from = normalize(f.s);
const target = parseRelationTarget(f.p);
const to = target ? normalize(target) : '';
// 任一方在 focus 中即保留
if (focusSet.has(from) || focusSet.has(to)) return true;
// 都不在 focus 中则过滤
return false;
}
// 3. 主体是已知角色名：检查是否在 focus 中
const subjectNorm = normalize(f.s);
if (knownCharacters.has(subjectNorm)) {
return focusSet.has(subjectNorm);
}
// 4. 主体不是人名（物品/地点/规则等）：保留
return true;
});
}
/**
* 格式化 facts 用于注入
* @param {Array} facts - facts 数组
* @param {Array} focusEntities - 焦点实体
* @param {Set} knownCharacters - 已知角色名集合
* @returns {Array} 格式化后的行
*/
function formatFactsForInjection(facts, focusEntities, knownCharacters) {
// 先过滤
const filtered = filterFactsByRelevance(facts, focusEntities, knownCharacters);
if (!filtered.length) return [];
// 按 since 降序排序（最新的优先）
return filtered
.sort((a, b) => (b.since || 0) - (a.since || 0))
.map(f => {
@@ -323,11 +218,6 @@ function formatFactsForInjection(facts, focusEntities, knownCharacters) {
// 格式化函数
// ─────────────────────────────────────────────────────────────────────────────
/**
* 格式化角色弧光行
* @param {object} a - 弧光对象
* @returns {string}
*/
function formatArcLine(a) {
const moments = (a.moments || [])
.map(m => (typeof m === "string" ? m : m.text))
@@ -339,11 +229,6 @@ function formatArcLine(a) {
return `- ${a.name}：${a.trajectory}`;
}
/**
* 格式化 chunk 完整行
* @param {object} c - chunk 对象
* @returns {string}
*/
function formatChunkFullLine(c) {
const { name1, name2 } = getContext();
@@ -355,38 +240,6 @@ function formatChunkFullLine(c) {
return `› #${c.floor + 1} [${speaker}] ${String(c.text || "").trim()}`;
}
/**
* 格式化带上下文的 chunk
* @param {object} mainChunk - 主 chunk
* @param {object|null} contextChunk - 上下文 chunk
* @returns {Array} 格式化的行数组
*/
function formatChunkWithContext(mainChunk, contextChunk) {
const lines = [];
const mainLine = formatChunkFullLine(mainChunk);
if (!contextChunk) {
lines.push(mainLine);
return lines;
}
if (contextChunk.floor < mainChunk.floor) {
lines.push(formatContextChunkLine(contextChunk, true));
lines.push(mainLine);
} else {
lines.push(mainLine);
lines.push(formatContextChunkLine(contextChunk, false));
}
return lines;
}
/**
* 格式化因果事件行
* @param {object} causalItem - 因果项
* @param {Map} causalById - 因果映射
* @returns {string}
*/
function formatCausalEventLine(causalItem, causalById) {
const ev = causalItem?.event || {};
const depth = Math.max(1, Math.min(9, causalItem?._causalDepth || 1));
@@ -415,22 +268,11 @@ function formatCausalEventLine(causalItem, causalById) {
return lines.join("\n");
}
/**
* 重新编号事件文本
* @param {string} text - 事件文本
* @param {number} newIndex - 新编号
* @returns {string}
*/
function renumberEventText(text, newIndex) {
const s = String(text || "");
return s.replace(/^(\s*)\d+(\.\s*(?:【)?)/, `$1${newIndex}$2`);
}
/**
* 获取事件排序键
* @param {object} ev - 事件对象
* @returns {number}
*/
function getEventSortKey(ev) {
const r = parseFloorRange(ev?.summary);
if (r) return r.start;
@@ -438,20 +280,98 @@ function getEventSortKey(ev) {
return m ? parseInt(m[1], 10) : Number.MAX_SAFE_INTEGER;
}
// ─────────────────────────────────────────────────────────────────────────────
// 按楼层分组装配 orphan chunks（修复上下文重复）
// ─────────────────────────────────────────────────────────────────────────────
function assembleOrphansByFloor(orphanCandidates, contextChunksByFloor, budget) {
if (!orphanCandidates?.length) {
return { lines: [], l0Count: 0, contextPairsCount: 0 };
}
// 1. 按楼层分组
const byFloor = new Map();
for (const c of orphanCandidates) {
const arr = byFloor.get(c.floor) || [];
arr.push(c);
byFloor.set(c.floor, arr);
}
// 2. 楼层内按 chunkIdx 排序
for (const [, chunks] of byFloor) {
chunks.sort((a, b) => (a.chunkIdx ?? 0) - (b.chunkIdx ?? 0));
}
// 3. 按楼层顺序装配
const floorsSorted = Array.from(byFloor.keys()).sort((a, b) => a - b);
const lines = [];
let l0Count = 0;
let contextPairsCount = 0;
for (const floor of floorsSorted) {
const chunks = byFloor.get(floor);
if (!chunks?.length) continue;
// 分离 L0 和 L1
const l0Chunks = chunks.filter(c => c.isL0);
const l1Chunks = chunks.filter(c => !c.isL0);
// L0 直接输出（不需要上下文）
for (const c of l0Chunks) {
const line = formatChunkFullLine(c);
if (!pushWithBudget(lines, line, budget)) {
return { lines, l0Count, contextPairsCount };
}
l0Count++;
}
// L1 按楼层统一处理
if (l1Chunks.length > 0) {
const firstChunk = l1Chunks[0];
const pairFloor = getContextFloor(firstChunk);
const pairCandidates = contextChunksByFloor.get(pairFloor) || [];
const contextChunk = pickContextChunk(pairCandidates, firstChunk);
// 上下文在前
if (contextChunk && contextChunk.floor < floor) {
const contextLine = formatContextChunkLine(contextChunk, true);
if (!pushWithBudget(lines, contextLine, budget)) {
return { lines, l0Count, contextPairsCount };
}
contextPairsCount++;
}
// 输出该楼层所有 L1 chunks
for (const c of l1Chunks) {
const line = formatChunkFullLine(c);
if (!pushWithBudget(lines, line, budget)) {
return { lines, l0Count, contextPairsCount };
}
}
// 上下文在后
if (contextChunk && contextChunk.floor > floor) {
const contextLine = formatContextChunkLine(contextChunk, false);
if (!pushWithBudget(lines, contextLine, budget)) {
return { lines, l0Count, contextPairsCount };
}
contextPairsCount++;
}
}
}
return { lines, l0Count, contextPairsCount };
}
// ─────────────────────────────────────────────────────────────────────────────
// 非向量模式
// ─────────────────────────────────────────────────────────────────────────────
/**
* 构建非向量模式的 prompt
* @param {object} store - summary store
* @returns {string}
*/
function buildNonVectorPrompt(store) {
const data = store.json || {};
const sections = [];
// L1 facts（非向量模式不做分层过滤，全量注入）
const allFacts = getFacts();
const factLines = allFacts
.filter(f => !f.retracted)
@@ -494,10 +414,6 @@ function buildNonVectorPrompt(store) {
);
}
/**
* 构建非向量模式的注入文本
* @returns {string}
*/
export function buildNonVectorPromptText() {
if (!getSettings().storySummary?.enabled) {
return "";
@@ -524,16 +440,6 @@ export function buildNonVectorPromptText() {
// 向量模式：预算装配
// ─────────────────────────────────────────────────────────────────────────────
/**
* 构建向量模式的 prompt
* @param {object} store - summary store
* @param {object} recallResult - 召回结果
* @param {Map} causalById - 因果映射
* @param {Array} focusEntities - 焦点实体
* @param {object} meta - 元数据
* @param {object} metrics - 指标对象
* @returns {Promise<object>} {promptText, injectionLogText, injectionStats, metrics}
*/
async function buildVectorPrompt(store, recallResult, causalById, focusEntities = [], meta = null, metrics = null) {
const T_Start = performance.now();
@@ -541,7 +447,6 @@ async function buildVectorPrompt(store, recallResult, causalById, focusEntities
const data = store.json || {};
const total = { used: 0, max: MAIN_BUDGET_MAX };
// 预装配容器
const assembled = {
facts: { lines: [], tokens: 0 },
arcs: { lines: [], tokens: 0 },
@@ -573,7 +478,7 @@ async function buildVectorPrompt(store, recallResult, causalById, focusEntities
};
// ═══════════════════════════════════════════════════════════════════════
// [优先级 1] 世界约束 - 最高优先级（带分层过滤）
// [优先级 1] 世界约束
// ═══════════════════════════════════════════════════════════════════════
const T_L1_Start = performance.now();
@@ -582,7 +487,6 @@ async function buildVectorPrompt(store, recallResult, causalById, focusEntities
const knownCharacters = getKnownCharacters(store);
const factLines = formatFactsForInjection(allFacts, focusEntities, knownCharacters);
// METRICS: L1 指标
if (metrics) {
metrics.l1.factsTotal = allFacts.length;
metrics.l1.factsFiltered = allFacts.length - factLines.length;
@@ -599,7 +503,6 @@ async function buildVectorPrompt(store, recallResult, causalById, focusEntities
injectionStats.facts.tokens = l1Budget.used;
injectionStats.facts.filtered = allFacts.length - factLines.length;
// METRICS
if (metrics) {
metrics.l1.factsInjected = assembled.facts.lines.length;
metrics.l1.tokens = l1Budget.used;
@@ -613,7 +516,7 @@ async function buildVectorPrompt(store, recallResult, causalById, focusEntities
}
// ═══════════════════════════════════════════════════════════════════════
// [优先级 2] 人物弧光 - 预留预算
// [优先级 2] 人物弧光
// ═══════════════════════════════════════════════════════════════════════
if (data.arcs?.length && total.used < total.max) {
@@ -652,13 +555,6 @@ async function buildVectorPrompt(store, recallResult, causalById, focusEntities
const chunks = recallResult?.chunks || [];
const usedChunkIds = new Set();
/**
* 为事件选择最佳证据 chunk
* @param {object} eventObj - 事件对象
* @returns {object|null} 最佳 chunk
*/
// 优先 L0 虚拟 chunk，否则按 chunkIdx 选第一个
function pickBestChunkForEvent(eventObj) {
const range = parseFloorRange(eventObj?.summary);
if (!range) return null;
@@ -667,27 +563,18 @@ async function buildVectorPrompt(store, recallResult, causalById, focusEntities
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) {
// L0 优先
best = c;
} else if (c.isL0 === best.isL0 && (c.chunkIdx ?? 0) < (best.chunkIdx ?? 0)) {
// 同类型按 chunkIdx 选靠前的
best = c;
}
}
return best;
}
}
/**
* 格式化带证据的事件
* @param {object} e - 事件召回项
* @param {number} idx - 索引
* @param {object|null} chunk - 证据 chunk
* @returns {string}
*/
function formatEventWithEvidence(e, idx, chunk) {
const ev = e.event || {};
const time = ev.timeLabel || "";
@@ -775,7 +662,6 @@ async function buildVectorPrompt(store, recallResult, causalById, focusEntities
});
}
// 重排
selectedDirect.sort((a, b) => getEventSortKey(a.event) - getEventSortKey(b.event));
selectedSimilar.sort((a, b) => getEventSortKey(a.event) - getEventSortKey(b.event));
@@ -829,47 +715,22 @@ async function buildVectorPrompt(store, recallResult, causalById, focusEntities
}
if (orphanCandidates.length && total.used < total.max) {
const orphans = orphanCandidates
.sort((a, b) => (a.floor - b.floor) || ((a.chunkIdx ?? 0) - (b.chunkIdx ?? 0)));
const l1Budget = { used: 0, max: Math.min(ORPHAN_MAX, total.max - total.used) };
let l0Count = 0;
let contextPairsCount = 0;
for (const c of orphans) {
if (c.isL0) {
const line = formatChunkFullLine(c);
if (!pushWithBudget(assembled.orphans.lines, line, l1Budget)) break;
injectionStats.orphans.injected++;
l0Count++;
continue;
}
const pairFloor = getContextFloor(c);
const pairCandidates = contextChunksByFloor.get(pairFloor) || [];
const contextChunk = pickContextChunk(pairCandidates, c);
const formattedLines = formatChunkWithContext(c, contextChunk);
let allAdded = true;
for (const line of formattedLines) {
if (!pushWithBudget(assembled.orphans.lines, line, l1Budget)) {
allAdded = false;
break;
}
}
if (!allAdded) break;
injectionStats.orphans.injected++;
if (contextChunk) contextPairsCount++;
}
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 = l0Count;
injectionStats.orphans.contextPairs = contextPairsCount;
injectionStats.orphans.l0Count = result.l0Count;
injectionStats.orphans.contextPairs = result.contextPairsCount;
}
// ═══════════════════════════════════════════════════════════════════════
@@ -891,7 +752,6 @@ async function buildVectorPrompt(store, recallResult, causalById, focusEntities
if (pairFloor >= 0) recentContextFloors.add(pairFloor);
}
let recentContextChunksByFloor = new Map();
if (chatId && recentContextFloors.size > 0) {
const newFloors = Array.from(recentContextFloors).filter(f => !contextChunksByFloor.has(f));
if (newFloors.length > 0) {
@@ -907,47 +767,25 @@ async function buildVectorPrompt(store, recallResult, causalById, focusEntities
xbLog.warn(MODULE_ID, "获取近期配对chunks失败", e);
}
}
recentContextChunksByFloor = contextChunksByFloor;
}
const recentOrphans = recentOrphanCandidates
.sort((a, b) => (a.floor - b.floor) || ((a.chunkIdx ?? 0) - (b.chunkIdx ?? 0)));
if (recentOrphanCandidates.length) {
const recentBudget = { used: 0, max: RECENT_ORPHAN_MAX };
const recentBudget = { used: 0, max: RECENT_ORPHAN_MAX };
let recentContextPairsCount = 0;
const result = assembleOrphansByFloor(
recentOrphanCandidates.sort((a, b) => (a.floor - b.floor) || ((a.chunkIdx ?? 0) - (b.chunkIdx ?? 0))),
contextChunksByFloor,
recentBudget
);
for (const c of recentOrphans) {
if (c.isL0) {
const line = formatChunkFullLine(c);
if (!pushWithBudget(assembled.recentOrphans.lines, line, recentBudget)) break;
recentOrphanStats.injected++;
continue;
}
assembled.recentOrphans.lines = result.lines;
assembled.recentOrphans.tokens = recentBudget.used;
const pairFloor = getContextFloor(c);
const pairCandidates = recentContextChunksByFloor.get(pairFloor) || [];
const contextChunk = pickContextChunk(pairCandidates, c);
const formattedLines = formatChunkWithContext(c, contextChunk);
let allAdded = true;
for (const line of formattedLines) {
if (!pushWithBudget(assembled.recentOrphans.lines, line, recentBudget)) {
allAdded = false;
break;
}
}
if (!allAdded) break;
recentOrphanStats.injected++;
if (contextChunk) recentContextPairsCount++;
recentOrphanStats.injected = result.lines.length;
recentOrphanStats.tokens = recentBudget.used;
recentOrphanStats.floorRange = `${recentStart + 1}~${recentEnd + 1}楼`;
recentOrphanStats.contextPairs = result.contextPairsCount;
}
assembled.recentOrphans.tokens = recentBudget.used;
recentOrphanStats.tokens = recentBudget.used;
recentOrphanStats.floorRange = `${recentStart + 1}~${recentEnd + 1}楼`;
recentOrphanStats.contextPairs = recentContextPairsCount;
}
// ═══════════════════════════════════════════════════════════════════════
@@ -990,9 +828,7 @@ async function buildVectorPrompt(store, recallResult, causalById, focusEntities
`<剧情记忆>\n\n${sections.join("\n\n")}\n\n</剧情记忆>\n` +
`${buildPostscript()}`;
// METRICS: 更新 L4 和 Budget 指标
if (metrics) {
// L4 指标
metrics.l4.sectionsIncluded = [];
if (assembled.facts.lines.length) metrics.l4.sectionsIncluded.push('constraints');
if (assembled.events.direct.length) metrics.l4.sectionsIncluded.push('direct_events');
@@ -1004,7 +840,6 @@ async function buildVectorPrompt(store, recallResult, causalById, focusEntities
metrics.l4.formattingTime = Math.round(performance.now() - T_L4_Start);
metrics.timing.l4Formatting = metrics.l4.formattingTime;
// Budget 指标
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);
@@ -1016,13 +851,11 @@ async function buildVectorPrompt(store, recallResult, causalById, focusEntities
arcs: assembled.arcs.tokens,
};
// L3 额外指标
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;
// 质量指标
const totalFacts = allFacts.length;
metrics.quality.constraintCoverage = totalFacts > 0
? Math.round(assembled.facts.lines.length / totalFacts * 100)
@@ -1035,7 +868,6 @@ async function buildVectorPrompt(store, recallResult, causalById, focusEntities
? Math.round(chunksWithEvents / totalChunks * 100)
: 0;
// 检测问题
metrics.quality.potentialIssues = detectIssues(metrics);
}
@@ -1046,13 +878,6 @@ async function buildVectorPrompt(store, recallResult, causalById, focusEntities
// 因果证据补充
// ─────────────────────────────────────────────────────────────────────────────
/**
* 为因果事件附加证据 chunk
* @param {Array} causalEvents - 因果事件列表
* @param {Map} eventVectorMap - 事件向量映射
* @param {Map} chunkVectorMap - chunk 向量映射
* @param {Map} chunksMap - chunk 映射
*/
async function attachEvidenceToCausalEvents(causalEvents, eventVectorMap, chunkVectorMap, chunksMap) {
for (const c of causalEvents) {
c._evidenceChunk = null;
@@ -1100,12 +925,6 @@ async function attachEvidenceToCausalEvents(causalEvents, eventVectorMap, chunkV
// 向量模式：召回 + 注入
// ─────────────────────────────────────────────────────────────────────────────
/**
* 构建向量模式的注入文本
* @param {boolean} excludeLastAi - 是否排除最后一条 AI 消息
* @param {object} hooks - 钩子 {postToFrame, echo, pendingUserMessage}
* @returns {Promise<object>} {text, logText}
*/
export async function buildVectorPromptText(excludeLastAi = false, hooks = {}) {
const { postToFrame = null, echo = null, pendingUserMessage = null } = hooks;
@@ -1156,7 +975,6 @@ export async function buildVectorPromptText(excludeLastAi = false, hooks = {}) {
metrics: recallResult?.metrics || null,
};
// 给因果事件挂证据
const causalEvents = recallResult.causalEvents || [];
if (causalEvents.length > 0) {
if (chatId) {
@@ -1228,7 +1046,6 @@ export async function buildVectorPromptText(excludeLastAi = false, hooks = {}) {
return { text: "", logText: "\n[Vector Recall Empty]\nNo recall candidates / vectors not ready.\n" };
}
// 拼装向量 prompt，传入 focusEntities 和 metrics
const { promptText, metrics: promptMetrics } = await buildVectorPrompt(
store,
recallResult,
@@ -1238,16 +1055,13 @@ export async function buildVectorPromptText(excludeLastAi = false, hooks = {}) {
recallResult?.metrics || null
);
// wrapper
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;
// METRICS: 生成完整的指标日志
const metricsLogText = promptMetrics ? formatMetricsLog(promptMetrics) : '';
// 发给 iframe
if (postToFrame) {
postToFrame({ type: "RECALL_LOG", text: metricsLogText });
}