X/LittleWhiteBox
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Compare commits

base: X:b4e181caea0aee263d6b7b66f227d4383f28235c
Branches Tags
X:main
..
compare: X:8226c48624586ac2845c6342395219f1a5974b4f
Branches Tags
X:main

2 Commits
b4e181caea ... 8226c48624

Author SHA1 Message Date
bielie
8226c48624 chore: update retrieval components 2026-02-08 18:14:02 +08:00
bielie
8fdce7b9a1 fix: qwen thinking toggle and recall log styles 2026-02-08 18:12:55 +08:00
7 changed files with 263 additions and 553 deletions
Expand all files Collapse all files

410
modules/story-summary/generate/prompt.js
View File

@@ -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 });
}

45
modules/story-summary/story-summary.css
View File

@@ -1455,23 +1455,25 @@ h1 span {
}
#recall-log-content {
flex: 1;
min-height: 0;
white-space: pre-wrap;
font-family: 'SF Mono', Monaco, Consolas, 'Courier New', monospace;
font-family: 'Consolas', 'Monaco', 'SF Mono', monospace;
font-size: 12px;
line-height: 1.6;
background: var(--bg3);
padding: 16px;
border-radius: 4px;
overflow-y: auto;
color: #e8e8e8;
white-space: pre-wrap !important;
overflow-x: hidden !important;
word-break: break-word;
overflow-wrap: break-word;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
.recall-empty {
color: var(--txt3);
color: #999;
text-align: center;
padding: 40px;
font-style: italic;
font-size: .8125rem;
line-height: 1.8;
}
/* 移动端适配 */
@@ -1483,9 +1485,11 @@ h1 span {
border-radius: 0;
}
.debug-log-viewer,
#recall-log-content {
font-size: 11px;
padding: 12px;
line-height: 1.5;
}
}
@@ -2732,14 +2736,18 @@ h1 span {
margin-bottom: 4px;
}
/* ═══════════════════════════════════════════════════════════════════════════
Recall Log / Debug Log
═══════════════════════════════════════════════════════════════════════════ */
.debug-log-viewer {
background: #1e1e1e;
color: #d4d4d4;
background: #1a1a1a;
color: #e0e0e0;
padding: 16px;
border-radius: 8px;
font-family: 'Consolas', 'Monaco', monospace;
font-family: 'Consolas', 'Monaco', 'SF Mono', monospace;
font-size: 12px;
line-height: 1.5;
line-height: 1.6;
max-height: 60vh;
overflow-y: auto;
overflow-x: hidden;
@@ -2749,7 +2757,7 @@ h1 span {
}
.recall-empty {
color: var(--txt3);
color: #999;
text-align: center;
padding: 40px;
font-style: italic;
@@ -2884,15 +2892,6 @@ h1 span {
Metrics Log Styling
═══════════════════════════════════════════════════════════════════════════ */
#recall-log-content {
font-family: 'SF Mono', Monaco, Consolas, 'Courier New', monospace;
font-size: 11px;
line-height: 1.5;
white-space: pre;
overflow-x: auto;
tab-size: 4;
}
#recall-log-content .metric-warn {
color: #f59e0b;
}

10
modules/story-summary/vector/llm/llm-service.js
View File

@@ -29,7 +29,7 @@ function b64UrlEncode(str) {
/**
* 统一LLM调用 - 走酒馆后端（非流式）
* 修复：assistant prefill 用 bottomassistant 参数传递
* assistant prefill 用 bottomassistant 参数传递
*/
export async function callLLM(messages, options = {}) {
const {
@@ -46,10 +46,10 @@ export async function callLLM(messages, options = {}) {
throw new Error('L0 requires siliconflow API key');
}
// ★ 关键修复：分离 assistant prefill
// 分离 assistant prefill
let topMessages = [...messages];
let assistantPrefill = '';
if (topMessages.length > 0 && topMessages[topMessages.length - 1]?.role === 'assistant') {
const lastMsg = topMessages.pop();
assistantPrefill = lastMsg.content || '';
@@ -70,6 +70,10 @@ export async function callLLM(messages, options = {}) {
apipassword: apiKey,
model: DEFAULT_L0_MODEL,
};
const isQwen3 = String(DEFAULT_L0_MODEL || '').includes('Qwen3');
if (isQwen3) {
args.enable_thinking = 'false';
}
// ★ 用 bottomassistant 参数传递 prefill
if (assistantPrefill) {

29
modules/story-summary/vector/retrieval/metrics.js
View File

@@ -48,17 +48,15 @@ export function createMetrics() {
// L3 Evidence Assembly
l3: {
floorsFromL0: 0,
// 候选规模（rerank 前）
l1Total: 0,
l1AfterCoarse: 0,
chunksInRange: 0,
chunksInRangeByType: { l0Virtual: 0, l1Real: 0 },
// 最终注入（rerank + sparse 后）
chunksSelected: 0,
chunksSelectedByType: { l0Virtual: 0, l1Real: 0 },
// 上下文配对
contextPairsAdded: 0,
tokens: 0,
assemblyTime: 0,
// Rerank 相关
rerankApplied: false,
beforeRerank: 0,
afterRerank: 0,
@@ -80,7 +78,6 @@ export function createMetrics() {
breakdown: {
constraints: 0,
events: 0,
entities: 0,
chunks: 0,
recentOrphans: 0,
arcs: 0,
@@ -204,8 +201,15 @@ export function formatMetricsLog(metrics) {
lines.push('[L3] Evidence Assembly');
lines.push(`├─ floors_from_l0: ${m.l3.floorsFromL0}`);
// 候选规模
lines.push(`├─ chunks_in_range: ${m.l3.chunksInRange}`);
// L1 粗筛信息
if (m.l3.l1Total > 0) {
lines.push(`├─ l1_coarse_filter:`);
lines.push(`│ ├─ total: ${m.l3.l1Total}`);
lines.push(`│ ├─ after: ${m.l3.l1AfterCoarse}`);
lines.push(`│ └─ filtered: ${m.l3.l1Total - m.l3.l1AfterCoarse}`);
}
lines.push(`├─ chunks_merged: ${m.l3.chunksInRange}`);
if (m.l3.chunksInRangeByType) {
const cir = m.l3.chunksInRangeByType;
lines.push(`│ ├─ l0_virtual: ${cir.l0Virtual || 0}`);
@@ -226,7 +230,6 @@ export function formatMetricsLog(metrics) {
lines.push(`├─ rerank_applied: false`);
}
// 最终注入规模
lines.push(`├─ chunks_selected: ${m.l3.chunksSelected}`);
if (m.l3.chunksSelectedByType) {
const cs = m.l3.chunksSelectedByType;
@@ -341,6 +344,14 @@ export function detectIssues(metrics) {
issues.push('L0 atoms not matched - may need to generate anchors');
}
// L1 粗筛问题
if (m.l3.l1Total > 0 && m.l3.l1AfterCoarse > 0) {
const coarseFilterRatio = 1 - (m.l3.l1AfterCoarse / m.l3.l1Total);
if (coarseFilterRatio > 0.9) {
issues.push(`Very high L1 coarse filter ratio (${(coarseFilterRatio * 100).toFixed(0)}%) - query may be too specific`);
}
}
// Rerank 相关问题
if (m.l3.rerankApplied) {
if (m.l3.beforeRerank > 0 && m.l3.afterRerank > 0) {
@@ -365,7 +376,7 @@ export function detectIssues(metrics) {
}
}
// 证据密度问题（基于 selected 的构成）
// 证据密度问题
if (m.l3.chunksSelected > 0 && m.l3.chunksSelectedByType) {
const l1Real = m.l3.chunksSelectedByType.l1Real || 0;
const density = l1Real / m.l3.chunksSelected;

302
modules/story-summary/vector/retrieval/recall.js
View File

@@ -1,13 +1,8 @@
// ═══════════════════════════════════════════════════════════════════════════
// Story Summary - Recall Engine (v3 - L0 作为 L3 索引 + Rerank 精排)
//
// 架构：
// - Query Expansion → L0（主索引）→ L3（按楼层拉取）→ Rerank（精排）
// - Query Expansion → L2（独立检索）
// - L0 和 L2 不在同一抽象层，分开处理
// Story Summary - Recall Engine (v4 - L0 无上限 + L1 粗筛)
// ═══════════════════════════════════════════════════════════════════════════
import { getAllEventVectors, getChunksByFloors, getMeta } from '../storage/chunk-store.js';
import { getAllEventVectors, getChunksByFloors, getMeta, getChunkVectorsByIds } from '../storage/chunk-store.js';
import { getAllStateVectors, getStateAtoms } from '../storage/state-store.js';
import { getEngineFingerprint, embed } from '../utils/embedder.js';
import { xbLog } from '../../../../core/debug-core.js';
@@ -27,9 +22,11 @@ const CONFIG = {
// Query Expansion
QUERY_EXPANSION_TIMEOUT: 6000,
// L0 配置
L0_MAX_RESULTS: 30,
L0_MIN_SIMILARITY: 0.50,
// L0 配置 - 去掉硬上限，提高阈值
L0_MIN_SIMILARITY: 0.58,
// L1 粗筛配置
L1_MAX_CANDIDATES: 100,
// L2 配置
L2_CANDIDATE_MAX: 100,
@@ -37,11 +34,8 @@ const CONFIG = {
L2_MIN_SIMILARITY: 0.55,
L2_MMR_LAMBDA: 0.72,
// L3 配置（从 L0 楼层拉取）
L3_MAX_CHUNKS_PER_FLOOR: 3,
L3_MAX_TOTAL_CHUNKS: 60,
// Rerank 配置
RERANK_THRESHOLD: 80,
RERANK_TOP_N: 50,
RERANK_MIN_SCORE: 0.15,
@@ -49,6 +43,8 @@ const CONFIG = {
CAUSAL_CHAIN_MAX_DEPTH: 10,
CAUSAL_INJECT_MAX: 30,
};
// ═══════════════════════════════════════════════════════════════════════════
// 工具函数
// ═══════════════════════════════════════════════════════════════════════════
@@ -75,12 +71,6 @@ function cleanForRecall(text) {
return filterText(text).replace(/\[tts:[^\]]*\]/gi, '').trim();
}
/**
* 从 focusEntities 中移除用户名
* @param {Array} focusEntities - 焦点实体
* @param {string} userName - 用户名
* @returns {Array} 过滤后的实体
*/
function removeUserNameFromFocus(focusEntities, userName) {
const u = normalize(userName);
if (!u) return Array.isArray(focusEntities) ? focusEntities : [];
@@ -91,28 +81,17 @@ function removeUserNameFromFocus(focusEntities, userName) {
.filter(e => normalize(e) !== u);
}
/**
* 构建用于 Rerank 的查询文本
* 综合 Query Expansion 结果和最近对话
* @param {object} expansion - Query Expansion 结果
* @param {Array} lastMessages - 最近的消息
* @param {string} pendingUserMessage - 待发送的用户消息
* @returns {string} Rerank 用的查询文本
*/
function buildRerankQuery(expansion, lastMessages, pendingUserMessage) {
const parts = [];
// 1. focus entities
if (expansion?.focus?.length) {
parts.push(expansion.focus.join(' '));
}
// 2. DSL queries（取前3个）
if (expansion?.queries?.length) {
parts.push(...expansion.queries.slice(0, 3));
}
// 3. 最近对话的关键内容
const recentTexts = (lastMessages || [])
.slice(-2)
.map(m => cleanForRecall(m.mes || '').slice(0, 150))
@@ -122,7 +101,6 @@ function buildRerankQuery(expansion, lastMessages, pendingUserMessage) {
parts.push(...recentTexts);
}
// 4. 待发送消息
if (pendingUserMessage) {
parts.push(cleanForRecall(pendingUserMessage).slice(0, 200));
}
@@ -134,15 +112,6 @@ function buildRerankQuery(expansion, lastMessages, pendingUserMessage) {
// MMR 选择
// ═══════════════════════════════════════════════════════════════════════════
/**
* MMR 多样性选择
* @param {Array} candidates - 候选项
* @param {number} k - 选择数量
* @param {number} lambda - MMR 参数
* @param {Function} getVector - 获取向量函数
* @param {Function} getScore - 获取分数函数
* @returns {Array} 选中的项
*/
function mmrSelect(candidates, k, lambda, getVector, getScore) {
const selected = [];
const ids = new Set();
@@ -183,23 +152,15 @@ function mmrSelect(candidates, k, lambda, getVector, getScore) {
}
// ═══════════════════════════════════════════════════════════════════════════
// L0 检索：Query → L0 → 楼层集合
// L0 检索：无上限，阈值过滤
// ═══════════════════════════════════════════════════════════════════════════
/**
* L0 向量检索
* @param {Array} queryVector - 查询向量
* @param {object} vectorConfig - 向量配置
* @param {object} metrics - 指标对象
* @returns {Promise<object>} {atoms, floors}
*/
async function searchL0(queryVector, vectorConfig, metrics) {
const { chatId } = getContext();
if (!chatId || !queryVector?.length) {
return { atoms: [], floors: new Set() };
}
// 检查 fingerprint
const meta = await getMeta(chatId);
const fp = getEngineFingerprint(vectorConfig);
if (meta.fingerprint && meta.fingerprint !== fp) {
@@ -207,17 +168,15 @@ async function searchL0(queryVector, vectorConfig, metrics) {
return { atoms: [], floors: new Set() };
}
// 获取向量
const stateVectors = await getAllStateVectors(chatId);
if (!stateVectors.length) {
return { atoms: [], floors: new Set() };
}
// 获取 atoms 元数据
const atomsList = getStateAtoms();
const atomMap = new Map(atomsList.map(a => [a.atomId, a]));
// 计算相似度
// ★ 只按阈值过滤，不设硬上限
const scored = stateVectors
.map(sv => {
const atom = atomMap.get(sv.atomId);
@@ -232,13 +191,10 @@ async function searchL0(queryVector, vectorConfig, metrics) {
})
.filter(Boolean)
.filter(s => s.similarity >= CONFIG.L0_MIN_SIMILARITY)
.sort((a, b) => b.similarity - a.similarity)
.slice(0, CONFIG.L0_MAX_RESULTS);
.sort((a, b) => b.similarity - a.similarity);
// 收集楼层
const floors = new Set(scored.map(s => s.floor));
// 更新 metrics
if (metrics) {
metrics.l0.atomsMatched = scored.length;
metrics.l0.floorsHit = floors.size;
@@ -253,48 +209,9 @@ async function searchL0(queryVector, vectorConfig, metrics) {
}
// ═══════════════════════════════════════════════════════════════════════════
// L3 拉取：L0 楼层 → Chunks（带 Rerank 精排）
// 统计 chunks 类型构成
// ═══════════════════════════════════════════════════════════════════════════
/**
* 按楼层稀疏去重
* 每楼层最多保留 limit 个 chunk，优先保留分数高的
* @param {Array} chunks - chunk 列表（假设已按分数排序）
* @param {number} limit - 每楼层上限
* @returns {Array} 去重后的 chunks
*/
function sparseByFloor(chunks, limit = 3) {
const byFloor = new Map();
for (const c of chunks) {
const arr = byFloor.get(c.floor) || [];
if (arr.length < limit) {
arr.push(c);
byFloor.set(c.floor, arr);
}
}
const result = [];
const seen = new Set();
for (const c of chunks) {
if (!seen.has(c.chunkId)) {
const arr = byFloor.get(c.floor);
if (arr?.includes(c)) {
result.push(c);
seen.add(c.chunkId);
}
}
}
return result;
}
/**
* 统计 chunks 的类型构成
* @param {Array} chunks - chunk 列表
* @returns {object} {l0Virtual, l1Real}
*/
function countChunksByType(chunks) {
let l0Virtual = 0;
let l1Real = 0;
@@ -310,15 +227,11 @@ function countChunksByType(chunks) {
return { l0Virtual, l1Real };
}
/**
* 从 L0 命中楼层拉取 chunks，并用 Reranker 精排
* @param {Set} l0Floors - L0 命中的楼层
* @param {Array} l0Atoms - L0 atoms（用于构建虚拟 chunks）
* @param {string} queryText - 查询文本（用于 rerank）
* @param {object} metrics - 指标对象
* @returns {Promise<Array>} chunks 列表
*/
async function getChunksFromL0Floors(l0Floors, l0Atoms, queryText, metrics) {
// ═══════════════════════════════════════════════════════════════════════════
// L3 拉取 + L1 粗筛 + Rerank
// ═══════════════════════════════════════════════════════════════════════════
async function getChunksFromL0Floors(l0Floors, l0Atoms, queryVector, queryText, metrics) {
const { chatId } = getContext();
if (!chatId || !l0Floors.size) {
return [];
@@ -326,15 +239,7 @@ async function getChunksFromL0Floors(l0Floors, l0Atoms, queryText, metrics) {
const floorArray = Array.from(l0Floors);
// 从 DB 拉取 chunks
let dbChunks = [];
try {
dbChunks = await getChunksByFloors(chatId, floorArray);
} catch (e) {
xbLog.warn(MODULE_ID, '从 DB 拉取 chunks 失败', e);
}
// 构建 L0 虚拟 chunks
// 1. 构建 L0 虚拟 chunks
const l0VirtualChunks = (l0Atoms || []).map(a => ({
chunkId: `state-${a.atomId}`,
floor: a.floor,
@@ -347,40 +252,69 @@ async function getChunksFromL0Floors(l0Floors, l0Atoms, queryText, metrics) {
_atom: a.atom,
}));
// 合并所有 chunks
const allChunks = [...l0VirtualChunks, ...dbChunks.map(c => ({
...c,
isL0: false,
similarity: 0.5,
}))];
// 2. 拉取 L1 chunks
let dbChunks = [];
try {
dbChunks = await getChunksByFloors(chatId, floorArray);
} catch (e) {
xbLog.warn(MODULE_ID, '从 DB 拉取 chunks 失败', e);
}
// ★ 更新 metrics - 候选规模（rerank 前）
// 3. ★ L1 向量粗筛
let l1Filtered = [];
if (dbChunks.length > 0 && queryVector?.length) {
const chunkIds = dbChunks.map(c => c.chunkId);
let chunkVectors = [];
try {
chunkVectors = await getChunkVectorsByIds(chatId, chunkIds);
} catch (e) {
xbLog.warn(MODULE_ID, 'L1 向量获取失败', e);
}
const vectorMap = new Map(chunkVectors.map(v => [v.chunkId, v.vector]));
l1Filtered = dbChunks
.map(c => {
const vec = vectorMap.get(c.chunkId);
if (!vec?.length) return null;
return {
...c,
isL0: false,
similarity: cosineSimilarity(queryVector, vec),
};
})
.filter(Boolean)
.sort((a, b) => b.similarity - a.similarity)
.slice(0, CONFIG.L1_MAX_CANDIDATES);
}
// 4. 合并
const allChunks = [...l0VirtualChunks, ...l1Filtered];
// ★ 更新 metrics
if (metrics) {
metrics.l3.floorsFromL0 = floorArray.length;
metrics.l3.chunksInRange = allChunks.length;
metrics.l3.l1Total = dbChunks.length;
metrics.l3.l1AfterCoarse = l1Filtered.length;
metrics.l3.chunksInRange = l0VirtualChunks.length + l1Filtered.length;
metrics.l3.chunksInRangeByType = {
l0Virtual: l0VirtualChunks.length,
l1Real: dbChunks.length,
l1Real: l1Filtered.length,
};
}
// 如果数量不超限，直接按楼层去重返回
if (allChunks.length <= CONFIG.L3_MAX_TOTAL_CHUNKS) {
allChunks.sort((a, b) => (b.similarity || 0) - (a.similarity || 0));
const selected = sparseByFloor(allChunks, CONFIG.L3_MAX_CHUNKS_PER_FLOOR);
// ★ 更新 metrics - 最终注入规模
// 5. 是否需要 Rerank
if (allChunks.length <= CONFIG.RERANK_THRESHOLD) {
if (metrics) {
metrics.l3.rerankApplied = false;
metrics.l3.chunksSelected = selected.length;
metrics.l3.chunksSelectedByType = countChunksByType(selected);
metrics.l3.chunksSelected = allChunks.length;
metrics.l3.chunksSelectedByType = countChunksByType(allChunks);
}
return selected;
return allChunks;
}
// ★ Reranker 精排
// 6. Rerank 精排
const T_Rerank_Start = performance.now();
const reranked = await rerankChunks(queryText, allChunks, {
@@ -390,21 +324,16 @@ async function getChunksFromL0Floors(l0Floors, l0Atoms, queryText, metrics) {
const rerankTime = Math.round(performance.now() - T_Rerank_Start);
// 按楼层稀疏去重
const selected = sparseByFloor(reranked, CONFIG.L3_MAX_CHUNKS_PER_FLOOR);
// ★ 更新 metrics
if (metrics) {
metrics.l3.rerankApplied = true;
metrics.l3.beforeRerank = allChunks.length;
metrics.l3.afterRerank = reranked.length;
metrics.l3.chunksSelected = selected.length;
metrics.l3.chunksSelectedByType = countChunksByType(selected);
metrics.l3.chunksSelected = reranked.length;
metrics.l3.chunksSelectedByType = countChunksByType(reranked);
metrics.l3.rerankTime = rerankTime;
metrics.timing.l3Rerank = rerankTime;
// rerank 分数分布（基于 selected）
const scores = selected.map(c => c._rerankScore || 0).filter(s => s > 0);
const scores = reranked.map(c => c._rerankScore || 0).filter(s => s > 0);
if (scores.length > 0) {
scores.sort((a, b) => a - b);
metrics.l3.rerankScoreDistribution = {
@@ -415,31 +344,21 @@ async function getChunksFromL0Floors(l0Floors, l0Atoms, queryText, metrics) {
}
}
xbLog.info(MODULE_ID, `L3 Rerank: ${allChunks.length} → ${reranked.length} → ${selected.length} (${rerankTime}ms)`);
xbLog.info(MODULE_ID, `L3: ${dbChunks.length} L1 → ${l1Filtered.length} 粗筛 → ${reranked.length} Rerank (${rerankTime}ms)`);
return selected;
return reranked;
}
// ═══════════════════════════════════════════════════════════════════════════
// L2 检索：Query → Events（独立）
// L2 检索（保持不变）
// ═══════════════════════════════════════════════════════════════════════════
/**
* L2 事件向量检索
* @param {Array} queryVector - 查询向量
* @param {Array} allEvents - 所有事件
* @param {object} vectorConfig - 向量配置
* @param {Array} focusEntities - 焦点实体（用于实体过滤）
* @param {object} metrics - 指标对象
* @returns {Promise<Array>} 事件列表
*/
async function searchL2Events(queryVector, allEvents, vectorConfig, focusEntities, metrics) {
const { chatId } = getContext();
if (!chatId || !queryVector?.length || !allEvents?.length) {
return [];
}
// 检查 fingerprint
const meta = await getMeta(chatId);
const fp = getEngineFingerprint(vectorConfig);
if (meta.fingerprint && meta.fingerprint !== fp) {
@@ -447,7 +366,6 @@ async function searchL2Events(queryVector, allEvents, vectorConfig, focusEntitie
return [];
}
// 获取事件向量
const eventVectors = await getAllEventVectors(chatId);
const vectorMap = new Map(eventVectors.map(v => [v.eventId, v.vector]));
@@ -455,19 +373,15 @@ async function searchL2Events(queryVector, allEvents, vectorConfig, focusEntitie
return [];
}
// 实体匹配集合
const focusSet = new Set((focusEntities || []).map(normalize));
// 计算相似度
const scored = allEvents.map(event => {
const v = vectorMap.get(event.id);
const baseSim = v ? cosineSimilarity(queryVector, v) : 0;
// 实体命中检查
const participants = (event.participants || []).map(p => normalize(p));
const hasEntityMatch = participants.some(p => focusSet.has(p));
// 实体匹配加权
const bonus = hasEntityMatch ? 0.05 : 0;
return {
@@ -480,12 +394,10 @@ async function searchL2Events(queryVector, allEvents, vectorConfig, focusEntitie
};
});
// 更新 metrics
if (metrics) {
metrics.l2.eventsInStore = allEvents.length;
}
// 阈值过滤
let candidates = scored
.filter(s => s.similarity >= CONFIG.L2_MIN_SIMILARITY)
.sort((a, b) => b.similarity - a.similarity)
@@ -495,14 +407,11 @@ async function searchL2Events(queryVector, allEvents, vectorConfig, focusEntitie
metrics.l2.eventsConsidered = candidates.length;
}
// 实体过滤（可选）
if (focusSet.size > 0) {
const beforeFilter = candidates.length;
candidates = candidates.filter(c => {
// 高相似度绕过
if (c.similarity >= 0.85) return true;
// 有实体匹配的保留
return c._hasEntityMatch;
});
@@ -516,7 +425,6 @@ async function searchL2Events(queryVector, allEvents, vectorConfig, focusEntitie
}
}
// MMR 去重
const selected = mmrSelect(
candidates,
CONFIG.L2_SELECT_MAX,
@@ -525,7 +433,6 @@ async function searchL2Events(queryVector, allEvents, vectorConfig, focusEntitie
c => c.similarity
);
// 统计召回类型
let directCount = 0;
let contextCount = 0;
@@ -542,7 +449,6 @@ async function searchL2Events(queryVector, allEvents, vectorConfig, focusEntitie
};
});
// 更新 metrics
if (metrics) {
metrics.l2.eventsSelected = results.length;
metrics.l2.byRecallType = { direct: directCount, context: contextCount, causal: 0 };
@@ -553,14 +459,9 @@ async function searchL2Events(queryVector, allEvents, vectorConfig, focusEntitie
}
// ═══════════════════════════════════════════════════════════════════════════
// 因果链追溯
// 因果链追溯（保持不变）
// ═══════════════════════════════════════════════════════════════════════════
/**
* 构建事件索引
* @param {Array} allEvents - 所有事件
* @returns {Map} 事件索引
*/
function buildEventIndex(allEvents) {
const map = new Map();
for (const e of allEvents || []) {
@@ -569,13 +470,6 @@ function buildEventIndex(allEvents) {
return map;
}
/**
* 追溯因果祖先
* @param {Array} recalledEvents - 召回的事件
* @param {Map} eventIndex - 事件索引
* @param {number} maxDepth - 最大深度
* @returns {object} {results, maxDepth}
*/
function traceCausalAncestors(recalledEvents, eventIndex, maxDepth = CONFIG.CAUSAL_CHAIN_MAX_DEPTH) {
const out = new Map();
const idRe = /^evt-\d+$/;
@@ -626,19 +520,11 @@ function traceCausalAncestors(recalledEvents, eventIndex, maxDepth = CONFIG.CAUS
// 辅助函数
// ═══════════════════════════════════════════════════════════════════════════
/**
* 获取最近的消息
* @param {Array} chat - 聊天数组
* @param {number} count - 消息数量
* @param {boolean} excludeLastAi - 是否排除最后一条 AI 消息
* @returns {Array} 消息列表
*/
function getLastMessages(chat, count = 4, excludeLastAi = false) {
if (!chat?.length) return [];
let messages = [...chat];
// 排除最后一条 AI 消息（swipe/regenerate 场景）
if (excludeLastAi && messages.length > 0 && !messages[messages.length - 1]?.is_user) {
messages = messages.slice(0, -1);
}
@@ -646,13 +532,6 @@ function getLastMessages(chat, count = 4, excludeLastAi = false) {
return messages.slice(-count);
}
/**
* 构建查询文本（降级用）
* @param {Array} chat - 聊天数组
* @param {number} count - 消息数量
* @param {boolean} excludeLastAi - 是否排除最后一条 AI 消息
* @returns {string} 查询文本
*/
export function buildQueryText(chat, count = 2, excludeLastAi = false) {
if (!chat?.length) return '';
@@ -672,14 +551,6 @@ export function buildQueryText(chat, count = 2, excludeLastAi = false) {
// 主函数
// ═══════════════════════════════════════════════════════════════════════════
/**
* 记忆召回主函数
* @param {string} queryText - 查询文本（降级用）
* @param {Array} allEvents - 所有事件
* @param {object} vectorConfig - 向量配置
* @param {object} options - 选项
* @returns {Promise<object>} 召回结果
*/
export async function recallMemory(queryText, allEvents, vectorConfig, options = {}) {
const T0 = performance.now();
const { chat, name1 } = getContext();
@@ -698,7 +569,6 @@ export async function recallMemory(queryText, allEvents, vectorConfig, options =
const T_QE_Start = performance.now();
// 获取最近对话
const lastMessages = getLastMessages(chat, 4, excludeLastAi);
let expansion = { focus: [], queries: [] };
@@ -712,14 +582,11 @@ export async function recallMemory(queryText, allEvents, vectorConfig, options =
xbLog.warn(MODULE_ID, 'Query Expansion 失败，降级使用原始文本', e);
}
// 构建检索文本
const searchText = buildSearchText(expansion);
const finalSearchText = searchText || queryText || lastMessages.map(m => cleanForRecall(m.mes || '').slice(0, 200)).join(' ');
// focusEntities（移除用户名）
const focusEntities = removeUserNameFromFocus(expansion.focus, name1);
// 更新 L0 metrics
metrics.l0.needRecall = true;
metrics.l0.focusEntities = focusEntities;
metrics.l0.queries = expansion.queries || [];
@@ -746,7 +613,7 @@ export async function recallMemory(queryText, allEvents, vectorConfig, options =
}
// ═══════════════════════════════════════════════════════════════════════
// Step 3: L0 检索 → L3 拉取（并行准备）
// Step 3: L0 检索
// ═══════════════════════════════════════════════════════════════════════
const T_L0_Start = performance.now();
@@ -756,15 +623,13 @@ export async function recallMemory(queryText, allEvents, vectorConfig, options =
metrics.timing.l0Search = Math.round(performance.now() - T_L0_Start);
// ═══════════════════════════════════════════════════════════════════════
// Step 4: L3 从 L0 楼层拉取（带 Rerank）
// Step 4: L3 拉取 + L1 粗筛 + Rerank
// ═══════════════════════════════════════════════════════════════════════
const T_L3_Start = performance.now();
// 构建 rerank 用的查询文本
const rerankQuery = buildRerankQuery(expansion, lastMessages, pendingUserMessage);
const chunks = await getChunksFromL0Floors(l0Floors, l0Atoms, rerankQuery, metrics);
const chunks = await getChunksFromL0Floors(l0Floors, l0Atoms, queryVector, rerankQuery, metrics);
metrics.timing.l3Retrieval = Math.round(performance.now() - T_L3_Start);
@@ -796,7 +661,6 @@ export async function recallMemory(queryText, allEvents, vectorConfig, options =
chainFrom: x.chainFrom,
}));
// 更新因果链 metrics
if (metrics.l2.byRecallType) {
metrics.l2.byRecallType.causal = causalEvents.length;
}
@@ -809,16 +673,14 @@ export async function recallMemory(queryText, allEvents, vectorConfig, options =
metrics.timing.total = Math.round(performance.now() - T0);
// 实体信息
metrics.l2.entityNames = focusEntities;
metrics.l2.entitiesLoaded = focusEntities.length;
// 日志
console.group('%c[Recall v3]', 'color: #7c3aed; font-weight: bold');
console.group('%c[Recall v4]', 'color: #7c3aed; font-weight: bold');
console.log(`Elapsed: ${metrics.timing.total}ms`);
console.log(`Query Expansion: focus=[${expansion.focus.join(', ')}]`);
console.log(`L0: ${l0Atoms.length} atoms → ${l0Floors.size} floors`);
console.log(`L3: ${chunks.length} chunks (L0=${metrics.l3.chunksSelectedByType?.l0Virtual || 0}, DB=${metrics.l3.chunksSelectedByType?.l1Real || 0})`);
console.log(`L3: ${metrics.l3.l1Total || 0} L1 → ${metrics.l3.l1AfterCoarse || 0} 粗筛 → ${chunks.length} final`);
if (metrics.l3.rerankApplied) {
console.log(`L3 Rerank: ${metrics.l3.beforeRerank} → ${metrics.l3.afterRerank} (${metrics.l3.rerankTime}ms)`);
}

14
modules/story-summary/vector/storage/chunk-store.js
View File

@@ -159,6 +159,20 @@ export async function getAllChunkVectors(chatId) {
}));
}
export async function getChunkVectorsByIds(chatId, chunkIds) {
if (!chatId || !chunkIds?.length) return [];
const records = await chunkVectorsTable
.where('[chatId+chunkId]')
.anyOf(chunkIds.map(id => [chatId, id]))
.toArray();
return records.map(r => ({
chunkId: r.chunkId,
vector: bufferToFloat32(r.vector),
}));
}
// ═══════════════════════════════════════════════════════════════════════════
// EventVectors 表操作
// ═══════════════════════════════════════════════════════════════════════════

6
modules/streaming-generation.js
View File

@@ -240,6 +240,9 @@ class StreamingGeneration {
include_reasoning: oai_settings?.show_thoughts ?? true,
reasoning_effort: oai_settings?.reasoning_effort || 'medium',
};
if (baseOptions?.enable_thinking !== undefined) body.enable_thinking = baseOptions.enable_thinking;
if (baseOptions?.thinking_budget !== undefined) body.thinking_budget = baseOptions.thinking_budget;
if (baseOptions?.min_p !== undefined) body.min_p = baseOptions.min_p;
// Claude 专用：top_k
if (source === chat_completion_sources.CLAUDE) {
@@ -949,6 +952,9 @@ class StreamingGeneration {
temperature: this.parseOpt(args, 'temperature'),
presence_penalty: this.parseOpt(args, 'presence_penalty'),
frequency_penalty: this.parseOpt(args, 'frequency_penalty'),
enable_thinking: this.parseOpt(args, 'enable_thinking'),
thinking_budget: this.parseOpt(args, 'thinking_budget'),
min_p: this.parseOpt(args, 'min_p'),
};
let parsedStop;
try {