feat: iframe 支持外部链接渲染 + 剧情总结 Prompt 自定义 + 记忆包导入导出
[外挂卡片支持外链加载]
- 代码块直接写一个 URL 链接(或注释 <!-- xb-src: URL -->)，小白盒会自动抓取并渲染成卡片
- 支持抓取失败自动降级为普通 iframe 直接显示
- 外链内容同样支持 {{xbgetvar::变量名}} 宏注入
[剧情总结 Prompt 全面开放自定义]
- 总结面板设置页新增 10 项 Prompt 编辑框，留空即使用默认值
- 包括：系统提示词、各段助手提示词、记忆注入模板等全部可改
- 记忆注入模板支持 {} 占位符替换成实际记忆内容
[剧情总结记忆包导入/导出]
- 新增「复制记忆包」按钮，一键把当前聊天的全部总结数据复制到剪贴板
- 新增「导入记忆包」按钮，把从别处复制来的记忆包 JSON 粘贴进来即可覆盖生效
- 方便跨设备、跨聊天迁移总结状态
This commit is contained in:
RT15548
@@ -72,9 +72,11 @@ function djb2(str) {
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function shouldRenderContentByBlock(codeBlock) {
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if (!codeBlock) return false;
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const content = (codeBlock.textContent || '').trim().toLowerCase();
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const content = (codeBlock.textContent || '').trim();
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if (!content) return false;
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return content.includes('<!doctype') || content.includes('<html') || content.includes('<script');
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if (extractExternalUrl(content)) return true;
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const lower = content.toLowerCase();
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return lower.includes('<!doctype') || lower.includes('<html') || lower.includes('<script');
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}
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function generateUniqueId() {
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@@ -148,6 +150,66 @@ function buildResourceHints(html) {
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return hints + preload;
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}
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function extractExternalUrl(content) {
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const trimmed = (content || '').trim();
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if (!trimmed) return null;
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if (/^https?:\/\/[^\s]+$/i.test(trimmed)) return trimmed;
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const match = trimmed.match(/<!--\s*xb-src:\s*(https?:\/\/[^\s>]+)\s*-->/i);
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if (match) return match[1];
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return null;
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}
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async function fetchExternalHtml(url) {
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try {
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const r = await fetch(url, { mode: 'cors' });
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if (r.ok) return await r.text();
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} catch (_) {}
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return null;
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}
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async function loadExternalUrl(iframe, url, settings) {
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try {
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iframe.srcdoc = '<!DOCTYPE html><html><body style="display:flex;justify-content:center;align-items:center;height:100px;color:#888;font-family:sans-serif;background:transparent">加载中...</body></html>';
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let html = await fetchExternalHtml(url);
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if (html && settings.variablesCore?.enabled && typeof replaceXbGetVarInString === 'function') {
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try {
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html = replaceXbGetVarInString(html);
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} catch (e) {
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console.warn('xbgetvar 宏替换失败:', e);
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}
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}
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if (html) {
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const full = buildWrappedHtml(html);
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if (settings.useBlob) {
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const codeHash = djb2(html);
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setIframeBlobHTML(iframe, full, codeHash);
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} else {
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iframe.srcdoc = full;
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}
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setTimeout(() => {
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try {
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const targetOrigin = getIframeTargetOrigin(iframe);
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postToIframe(iframe, { type: 'probe' }, null, targetOrigin);
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} catch (e) {}
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}, 100);
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} else {
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iframe.removeAttribute('srcdoc');
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iframe.src = url;
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iframe.style.minHeight = '800px';
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iframe.setAttribute('scrolling', 'auto');
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}
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} catch (err) {
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console.error('[iframeRenderer] 外部URL加载失败:', err);
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iframe.removeAttribute('srcdoc');
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iframe.src = url;
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iframe.style.minHeight = '800px';
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iframe.setAttribute('scrolling', 'auto');
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}
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}
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function buildWrappedHtml(html) {
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const settings = getSettings();
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const wrapperToggle = settings.wrapperIframe ?? true;
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@@ -341,15 +403,7 @@ export function renderHtmlInIframe(htmlContent, container, preElement) {
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const settings = getSettings();
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try {
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const originalHash = djb2(htmlContent);
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if (settings.variablesCore?.enabled && typeof replaceXbGetVarInString === 'function') {
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try {
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htmlContent = replaceXbGetVarInString(htmlContent);
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} catch (e) {
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console.warn('xbgetvar 宏替换失败:', e);
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}
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}
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const externalUrl = extractExternalUrl(htmlContent);
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const iframe = document.createElement('iframe');
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iframe.id = generateUniqueId();
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iframe.className = 'xiaobaix-iframe';
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@@ -364,28 +418,41 @@ export function renderHtmlInIframe(htmlContent, container, preElement) {
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releaseIframeBlob(old);
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old.remove();
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});
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const codeHash = djb2(htmlContent);
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const full = buildWrappedHtml(htmlContent);
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if (settings.useBlob) {
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setIframeBlobHTML(iframe, full, codeHash);
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} else {
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iframe.srcdoc = full;
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}
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wrapper.appendChild(iframe);
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preElement.classList.remove('xb-show');
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preElement.style.display = 'none';
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registerIframeMapping(iframe, wrapper);
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try {
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const targetOrigin = getIframeTargetOrigin(iframe);
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postToIframe(iframe, { type: 'probe' }, null, targetOrigin);
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} catch (e) {}
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if (externalUrl) {
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loadExternalUrl(iframe, externalUrl, settings);
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} else {
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if (settings.variablesCore?.enabled && typeof replaceXbGetVarInString === 'function') {
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try {
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htmlContent = replaceXbGetVarInString(htmlContent);
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} catch (e) {
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console.warn('xbgetvar 宏替换失败:', e);
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}
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}
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const codeHash = djb2(htmlContent);
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const full = buildWrappedHtml(htmlContent);
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if (settings.useBlob) {
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setIframeBlobHTML(iframe, full, codeHash);
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} else {
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iframe.srcdoc = full;
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}
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try {
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const targetOrigin = getIframeTargetOrigin(iframe);
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postToIframe(iframe, { type: 'probe' }, null, targetOrigin);
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} catch (e) {}
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}
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preElement.dataset.xbFinal = 'true';
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preElement.dataset.xbHash = originalHash;
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return iframe;
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} catch (err) {
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console.error('[iframeRenderer] 渲染失败:', err);
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@@ -412,10 +479,11 @@ export function processCodeBlocks(messageElement, forceFinal = true) {
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const should = shouldRenderContentByBlock(codeBlock);
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const html = codeBlock.textContent || '';
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const hash = djb2(html);
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const externalUrl = extractExternalUrl(html);
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const isFinal = preElement.dataset.xbFinal === 'true';
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const same = preElement.dataset.xbHash === hash;
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if (isFinal && same) return;
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if (!externalUrl && isFinal && same) return;
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if (should) {
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renderHtmlInIframe(html, preElement.parentNode, preElement);
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@@ -12,6 +12,249 @@ const DEFAULT_FILTER_RULES = [
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{ start: "```", end: "```" },
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];
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export const DEFAULT_SUMMARY_SYSTEM_PROMPT = `Story Analyst: This task involves narrative comprehension and structured incremental summarization, representing creative story analysis at the intersection of plot tracking and character development. As a story analyst, you will conduct systematic evaluation of provided dialogue content to generate structured incremental summary data.
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[Read the settings for this task]
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<task_settings>
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Incremental_Summary_Requirements:
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- Incremental_Only: 只提取新对话中的新增要素，绝不重复已有总结
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- Event_Granularity: 记录有叙事价值的事件，而非剧情梗概
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- Memory_Album_Style: 形成有细节、有温度、有记忆点的回忆册
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- Event_Classification:
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type:
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- 相遇: 人物/事物初次接触
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- 冲突: 对抗、矛盾激化
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- 揭示: 真相、秘密、身份
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- 抉择: 关键决定
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- 羁绊: 关系加深或破裂
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- 转变: 角色/局势改变
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- 收束: 问题解决、和解
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- 日常: 生活片段
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weight:
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- 核心: 删掉故事就崩
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- 主线: 推动主要剧情
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- 转折: 改变某条线走向
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- 点睛: 有细节不影响主线
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- 氛围: 纯粹氛围片段
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- Causal_Chain: 为每个新事件标注直接前因事件ID（causedBy）。仅在因果关系明确（直接导致/明确动机/承接后果）时填写；不明确时填[]完全正常。0-2个，只填 evt-数字，指向已存在或本次新输出事件。
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- Character_Dynamics: 识别新角色，追踪关系趋势（破裂/厌恶/反感/陌生/投缘/亲密/交融）
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- Arc_Tracking: 更新角色弧光轨迹与成长进度(0.0-1.0)
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- Fact_Tracking: 维护 SPO 三元组知识图谱。追踪生死、物品归属、位置、关系等硬性事实。采用 KV 覆盖模型（s+p 为键）。
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</task_settings>
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---
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Story Analyst:
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[Responsibility Definition]
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\`\`\`yaml
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analysis_task:
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title: Incremental Story Summarization with Knowledge Graph
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Story Analyst:
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role: Antigravity
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task: >-
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To analyze provided dialogue content against existing summary state,
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extract only NEW plot elements, character developments, relationship
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changes, arc progressions, AND fact updates, outputting
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structured JSON for incremental summary database updates.
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assistant:
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role: Summary Specialist
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description: Incremental Story Summary & Knowledge Graph Analyst
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behavior: >-
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To compare new dialogue against existing summary, identify genuinely
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new events and character interactions, classify events by narrative
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type and weight, track character arc progression with percentage,
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maintain facts as SPO triples with clear semantics,
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and output structured JSON containing only incremental updates.
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Must strictly avoid repeating any existing summary content.
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user:
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role: Content Provider
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description: Supplies existing summary state and new dialogue
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behavior: >-
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To provide existing summary state (events, characters, arcs, facts)
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and new dialogue content for incremental analysis.
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interaction_mode:
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type: incremental_analysis
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output_format: structured_json
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deduplication: strict_enforcement
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execution_context:
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summary_active: true
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incremental_only: true
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memory_album_style: true
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fact_tracking: true
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\`\`\`
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---
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Summary Specialist:
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<Chat_History>`;
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export const DEFAULT_MEMORY_PROMPT_TEMPLATE = `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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• [其他人的事] 别人的经历，当前角色可能不知晓
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• 其余部分是过往经历的回忆碎片
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请内化这些记忆：
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{$剧情记忆}
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这些记忆是真实的，请自然地记住它们。`;
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export const DEFAULT_SUMMARY_ASSISTANT_DOC_PROMPT = `
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Summary Specialist:
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Acknowledged. Now reviewing the incremental summarization specifications:
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[Event Classification System]
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├─ Types: 相遇|冲突|揭示|抉择|羁绊|转变|收束|日常
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├─ Weights: 核心|主线|转折|点睛|氛围
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└─ Each event needs: id, title, timeLabel, summary(含楼层), participants, type, weight
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[Relationship Trend Scale]
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破裂 ← 厌恶 ← 反感 ← 陌生 → 投缘 → 亲密 → 交融
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[Arc Progress Tracking]
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├─ trajectory: 当前阶段描述(15字内)
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├─ progress: 0.0 to 1.0
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└─ newMoment: 仅记录本次新增的关键时刻
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[Fact Tracking - SPO / World Facts]
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We maintain a small "world state" as SPO triples.
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Each update is a JSON object: {s, p, o, isState, trend?, retracted?}
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Core rules:
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1) Keyed by (s + p). If a new update has the same (s+p), it overwrites the previous value.
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2) Only output facts that are NEW or CHANGED in the new dialogue. Do NOT repeat unchanged facts.
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3) isState meaning:
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- isState: true -> core constraints that must stay stable and should NEVER be auto-deleted
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(identity, location, life/death, ownership, relationship status, binding rules)
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- isState: false -> non-core facts / soft memories that may be pruned by capacity limits later
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4) Relationship facts:
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- Use predicate format: "对X的看法" (X is the target person)
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- trend is required for relationship facts, one of:
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破裂 | 厌恶 | 反感 | 陌生 | 投缘 | 亲密 | 交融
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5) Retraction (deletion):
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- To delete a fact, output: {s, p, retracted: true}
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6) Predicate normalization:
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- Reuse existing predicates whenever possible, avoid inventing synonyms.
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Ready to process incremental summary requests with strict deduplication.`;
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export const DEFAULT_SUMMARY_ASSISTANT_ASK_SUMMARY_PROMPT = `
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Summary Specialist:
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Specifications internalized. Please provide the existing summary state so I can:
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1. Index all recorded events to avoid duplication
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2. Map current character list as baseline
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3. Note existing arc progress levels
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4. Identify established keywords
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5. Review current facts (SPO triples baseline)`;
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export const DEFAULT_SUMMARY_ASSISTANT_ASK_CONTENT_PROMPT = `
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Summary Specialist:
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Existing summary fully analyzed and indexed. I understand:
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├─ Recorded events: Indexed for deduplication
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├─ Character list: Baseline mapped
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├─ Arc progress: Levels noted
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├─ Keywords: Current state acknowledged
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└─ Facts: SPO baseline loaded
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I will extract only genuinely NEW elements from the upcoming dialogue.
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Please provide the new dialogue content requiring incremental analysis.`;
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export const DEFAULT_SUMMARY_META_PROTOCOL_START_PROMPT = `
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Summary Specialist:
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ACKNOWLEDGED. Beginning structured JSON generation:
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<meta_protocol>`;
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export const DEFAULT_SUMMARY_USER_JSON_FORMAT_PROMPT = `
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## Output Rule
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Generate a single valid JSON object with INCREMENTAL updates only.
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## Mindful Approach
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Before generating, observe the USER and analyze carefully:
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- What is user's writing style and emotional expression?
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- What NEW events occurred (not in existing summary)?
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- What NEW characters appeared for the first time?
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- What relationship CHANGES happened?
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- What arc PROGRESS was made?
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- What facts changed? (status/position/ownership/relationships)
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## factUpdates 规则
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- 目的: 纠错 & 世界一致性约束，只记录硬性事实
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- s+p 为键，相同键会覆盖旧值
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- isState: true=核心约束(位置/身份/生死/关系)，false=有容量上限会被清理
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- 关系类: p="对X的看法"，trend 必填（破裂|厌恶|反感|陌生|投缘|亲密|交融）
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- 删除: {s, p, retracted: true}，不需要 o 字段
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- 更新: {s, p, o, isState, trend?}
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- 谓词规范化: 复用已有谓词，不要发明同义词
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- 只输出有变化的条目，确保少、硬、稳定
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## Output Format
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\`\`\`json
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{
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"mindful_prelude": {
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"user_insight": "用户的幻想是什么时空、场景，是否反应出存在严重心理问题需要建议？",
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"dedup_analysis": "已有X个事件，本次识别Y个新事件",
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"fact_changes": "识别到的事实变化概述"
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},
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"keywords": [
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{"text": "综合历史+新内容的全剧情关键词(5-10个)", "weight": "核心|重要|一般"}
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],
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"events": [
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{
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"id": "evt-{$nextEventId}起始，依次递增",
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"title": "地点·事件标题",
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"timeLabel": "时间线标签(如：开场、第二天晚上)",
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"summary": "1-2句话描述，涵盖丰富信息素，末尾标注楼层(#X-Y)",
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"participants": ["参与角色名，不要使用人称代词或别名，只用正式人名"],
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"type": "相遇|冲突|揭示|抉择|羁绊|转变|收束|日常",
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"weight": "核心|主线|转折|点睛|氛围",
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"causedBy": ["evt-12", "evt-14"]
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}
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],
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"newCharacters": ["仅本次首次出现的角色名"],
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"arcUpdates": [
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{"name": "角色名，不要使用人称代词或别名，只用正式人名", "trajectory": "当前阶段描述(15字内)", "progress": 0.0-1.0, "newMoment": "本次新增的关键时刻"}
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],
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"factUpdates": [
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{"s": "主体", "p": "谓词", "o": "当前值", "isState": true, "trend": "仅关系类填"},
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{"s": "要删除的主体", "p": "要删除的谓词", "retracted": true}
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]
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}
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\`\`\`
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## CRITICAL NOTES
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||||
- events.id 从 evt-{$nextEventId} 开始编号
|
||||
- 仅输出【增量】内容，已有事件绝不重复
|
||||
- /地点、通过什么方式、对谁、做了什么事、结果如何。如果原文有具体道具（如一把枪、一封信），必须在总结中提及。
|
||||
- keywords 是全局关键词，综合已有+新增
|
||||
- causedBy 仅在因果明确时填写，允许为[]，0-2个
|
||||
- factUpdates 可为空数组
|
||||
- 合法JSON，字符串值内部避免英文双引号
|
||||
- 用朴实、白描、有烟火气的笔触记录事实，避免比喻和意象
|
||||
- 严谨、注重细节，避免使用模糊的概括性语言，应用具体的动词描述动作，例:谁,在什么时间/地点,通过什么方式,对谁,做了什么事,出现了什么道具,结果如何。
|
||||
</meta_protocol>
|
||||
|
||||
## Placeholder Notes
|
||||
- {$nextEventId} 会在运行时替换成实际起始事件编号，不要删除
|
||||
- {$existingEventCount}、{$historyRange} 这类占位符如果出现在你的自定义版本里，通常也不应该删除`;
|
||||
|
||||
export const DEFAULT_SUMMARY_ASSISTANT_CHECK_PROMPT = `Content review initiated...
|
||||
[Compliance Check Results]
|
||||
├─ Existing summary loaded: ✓ Fully indexed
|
||||
├─ New dialogue received: ✓ Content parsed
|
||||
├─ Deduplication engine: ✓ Active
|
||||
├─ Event classification: ✓ Ready
|
||||
├─ Fact tracking: ✓ Enabled
|
||||
└─ Output format: ✓ JSON specification loaded
|
||||
|
||||
[Material Verification]
|
||||
├─ Existing events: Indexed ({$existingEventCount} recorded)
|
||||
├─ Character baseline: Mapped
|
||||
├─ Arc progress baseline: Noted
|
||||
├─ Facts baseline: Loaded
|
||||
└─ Output specification: ✓ Defined in <meta_protocol>
|
||||
All checks passed. Beginning incremental extraction...
|
||||
{
|
||||
"mindful_prelude":`;
|
||||
|
||||
export const DEFAULT_SUMMARY_USER_CONFIRM_PROMPT = `怎么截断了！重新完整生成，只输出JSON，不要任何其他内容，3000字以内
|
||||
</Chat_History>`;
|
||||
|
||||
export const DEFAULT_SUMMARY_ASSISTANT_PREFILL_PROMPT = '下面重新生成完整JSON。';
|
||||
|
||||
export function getSettings() {
|
||||
const ext = (extension_settings[EXT_ID] ||= {});
|
||||
ext.storySummary ||= { enabled: true };
|
||||
@@ -44,6 +287,18 @@ export function getSummaryPanelConfig() {
|
||||
keepVisibleCount: 6,
|
||||
},
|
||||
textFilterRules: [...DEFAULT_FILTER_RULES],
|
||||
prompts: {
|
||||
summarySystemPrompt: DEFAULT_SUMMARY_SYSTEM_PROMPT,
|
||||
summaryAssistantDocPrompt: DEFAULT_SUMMARY_ASSISTANT_DOC_PROMPT,
|
||||
summaryAssistantAskSummaryPrompt: DEFAULT_SUMMARY_ASSISTANT_ASK_SUMMARY_PROMPT,
|
||||
summaryAssistantAskContentPrompt: DEFAULT_SUMMARY_ASSISTANT_ASK_CONTENT_PROMPT,
|
||||
summaryMetaProtocolStartPrompt: DEFAULT_SUMMARY_META_PROTOCOL_START_PROMPT,
|
||||
summaryUserJsonFormatPrompt: DEFAULT_SUMMARY_USER_JSON_FORMAT_PROMPT,
|
||||
summaryAssistantCheckPrompt: DEFAULT_SUMMARY_ASSISTANT_CHECK_PROMPT,
|
||||
summaryUserConfirmPrompt: DEFAULT_SUMMARY_USER_CONFIRM_PROMPT,
|
||||
summaryAssistantPrefillPrompt: DEFAULT_SUMMARY_ASSISTANT_PREFILL_PROMPT,
|
||||
memoryTemplate: DEFAULT_MEMORY_PROMPT_TEMPLATE,
|
||||
},
|
||||
vector: null,
|
||||
};
|
||||
|
||||
@@ -64,6 +319,7 @@ export function getSummaryPanelConfig() {
|
||||
trigger: { ...defaults.trigger, ...(parsed.trigger || {}) },
|
||||
ui: { ...defaults.ui, ...(parsed.ui || {}) },
|
||||
textFilterRules,
|
||||
prompts: { ...defaults.prompts, ...(parsed.prompts || {}) },
|
||||
vector: parsed.vector || null,
|
||||
};
|
||||
|
||||
|
||||
@@ -1,5 +1,18 @@
|
||||
// LLM Service
|
||||
|
||||
import {
|
||||
getSummaryPanelConfig,
|
||||
DEFAULT_SUMMARY_SYSTEM_PROMPT,
|
||||
DEFAULT_SUMMARY_ASSISTANT_DOC_PROMPT,
|
||||
DEFAULT_SUMMARY_ASSISTANT_ASK_SUMMARY_PROMPT,
|
||||
DEFAULT_SUMMARY_ASSISTANT_ASK_CONTENT_PROMPT,
|
||||
DEFAULT_SUMMARY_META_PROTOCOL_START_PROMPT,
|
||||
DEFAULT_SUMMARY_USER_JSON_FORMAT_PROMPT,
|
||||
DEFAULT_SUMMARY_ASSISTANT_CHECK_PROMPT,
|
||||
DEFAULT_SUMMARY_USER_CONFIRM_PROMPT,
|
||||
DEFAULT_SUMMARY_ASSISTANT_PREFILL_PROMPT,
|
||||
} from "../data/config.js";
|
||||
|
||||
const PROVIDER_MAP = {
|
||||
openai: "openai",
|
||||
google: "gemini",
|
||||
@@ -11,237 +24,18 @@ const PROVIDER_MAP = {
|
||||
custom: "custom",
|
||||
};
|
||||
|
||||
const JSON_PREFILL = '下面重新生成完整JSON。';
|
||||
const JSON_PREFILL = DEFAULT_SUMMARY_ASSISTANT_PREFILL_PROMPT;
|
||||
|
||||
const LLM_PROMPT_CONFIG = {
|
||||
topSystem: `Story Analyst: This task involves narrative comprehension and structured incremental summarization, representing creative story analysis at the intersection of plot tracking and character development. As a story analyst, you will conduct systematic evaluation of provided dialogue content to generate structured incremental summary data.
|
||||
[Read the settings for this task]
|
||||
<task_settings>
|
||||
Incremental_Summary_Requirements:
|
||||
- Incremental_Only: 只提取新对话中的新增要素，绝不重复已有总结
|
||||
- Event_Granularity: 记录有叙事价值的事件，而非剧情梗概
|
||||
- Memory_Album_Style: 形成有细节、有温度、有记忆点的回忆册
|
||||
- Event_Classification:
|
||||
type:
|
||||
- 相遇: 人物/事物初次接触
|
||||
- 冲突: 对抗、矛盾激化
|
||||
- 揭示: 真相、秘密、身份
|
||||
- 抉择: 关键决定
|
||||
- 羁绊: 关系加深或破裂
|
||||
- 转变: 角色/局势改变
|
||||
- 收束: 问题解决、和解
|
||||
- 日常: 生活片段
|
||||
weight:
|
||||
- 核心: 删掉故事就崩
|
||||
- 主线: 推动主要剧情
|
||||
- 转折: 改变某条线走向
|
||||
- 点睛: 有细节不影响主线
|
||||
- 氛围: 纯粹氛围片段
|
||||
- Causal_Chain: 为每个新事件标注直接前因事件ID（causedBy）。仅在因果关系明确（直接导致/明确动机/承接后果）时填写；不明确时填[]完全正常。0-2个，只填 evt-数字，指向已存在或本次新输出事件。
|
||||
- Character_Dynamics: 识别新角色，追踪关系趋势（破裂/厌恶/反感/陌生/投缘/亲密/交融）
|
||||
- Arc_Tracking: 更新角色弧光轨迹与成长进度(0.0-1.0)
|
||||
- Fact_Tracking: 维护 SPO 三元组知识图谱。追踪生死、物品归属、位置、关系等硬性事实。采用 KV 覆盖模型（s+p 为键）。
|
||||
</task_settings>
|
||||
---
|
||||
Story Analyst:
|
||||
[Responsibility Definition]
|
||||
\`\`\`yaml
|
||||
analysis_task:
|
||||
title: Incremental Story Summarization with Knowledge Graph
|
||||
Story Analyst:
|
||||
role: Antigravity
|
||||
task: >-
|
||||
To analyze provided dialogue content against existing summary state,
|
||||
extract only NEW plot elements, character developments, relationship
|
||||
changes, arc progressions, AND fact updates, outputting
|
||||
structured JSON for incremental summary database updates.
|
||||
assistant:
|
||||
role: Summary Specialist
|
||||
description: Incremental Story Summary & Knowledge Graph Analyst
|
||||
behavior: >-
|
||||
To compare new dialogue against existing summary, identify genuinely
|
||||
new events and character interactions, classify events by narrative
|
||||
type and weight, track character arc progression with percentage,
|
||||
maintain facts as SPO triples with clear semantics,
|
||||
and output structured JSON containing only incremental updates.
|
||||
Must strictly avoid repeating any existing summary content.
|
||||
user:
|
||||
role: Content Provider
|
||||
description: Supplies existing summary state and new dialogue
|
||||
behavior: >-
|
||||
To provide existing summary state (events, characters, arcs, facts)
|
||||
and new dialogue content for incremental analysis.
|
||||
interaction_mode:
|
||||
type: incremental_analysis
|
||||
output_format: structured_json
|
||||
deduplication: strict_enforcement
|
||||
execution_context:
|
||||
summary_active: true
|
||||
incremental_only: true
|
||||
memory_album_style: true
|
||||
fact_tracking: true
|
||||
\`\`\`
|
||||
---
|
||||
Summary Specialist:
|
||||
<Chat_History>`,
|
||||
|
||||
assistantDoc: `
|
||||
Summary Specialist:
|
||||
Acknowledged. Now reviewing the incremental summarization specifications:
|
||||
|
||||
[Event Classification System]
|
||||
├─ Types: 相遇|冲突|揭示|抉择|羁绊|转变|收束|日常
|
||||
├─ Weights: 核心|主线|转折|点睛|氛围
|
||||
└─ Each event needs: id, title, timeLabel, summary(含楼层), participants, type, weight
|
||||
|
||||
[Relationship Trend Scale]
|
||||
破裂 ← 厌恶 ← 反感 ← 陌生 → 投缘 → 亲密 → 交融
|
||||
|
||||
[Arc Progress Tracking]
|
||||
├─ trajectory: 当前阶段描述(15字内)
|
||||
├─ progress: 0.0 to 1.0
|
||||
└─ newMoment: 仅记录本次新增的关键时刻
|
||||
|
||||
[Fact Tracking - SPO / World Facts]
|
||||
We maintain a small "world state" as SPO triples.
|
||||
Each update is a JSON object: {s, p, o, isState, trend?, retracted?}
|
||||
|
||||
Core rules:
|
||||
1) Keyed by (s + p). If a new update has the same (s+p), it overwrites the previous value.
|
||||
2) Only output facts that are NEW or CHANGED in the new dialogue. Do NOT repeat unchanged facts.
|
||||
3) isState meaning:
|
||||
- isState: true -> core constraints that must stay stable and should NEVER be auto-deleted
|
||||
(identity, location, life/death, ownership, relationship status, binding rules)
|
||||
- isState: false -> non-core facts / soft memories that may be pruned by capacity limits later
|
||||
4) Relationship facts:
|
||||
- Use predicate format: "对X的看法" (X is the target person)
|
||||
- trend is required for relationship facts, one of:
|
||||
破裂 | 厌恶 | 反感 | 陌生 | 投缘 | 亲密 | 交融
|
||||
5) Retraction (deletion):
|
||||
- To delete a fact, output: {s, p, retracted: true}
|
||||
6) Predicate normalization:
|
||||
- Reuse existing predicates whenever possible, avoid inventing synonyms.
|
||||
|
||||
Ready to process incremental summary requests with strict deduplication.`,
|
||||
|
||||
assistantAskSummary: `
|
||||
Summary Specialist:
|
||||
Specifications internalized. Please provide the existing summary state so I can:
|
||||
1. Index all recorded events to avoid duplication
|
||||
2. Map current character list as baseline
|
||||
3. Note existing arc progress levels
|
||||
4. Identify established keywords
|
||||
5. Review current facts (SPO triples baseline)`,
|
||||
|
||||
assistantAskContent: `
|
||||
Summary Specialist:
|
||||
Existing summary fully analyzed and indexed. I understand:
|
||||
├─ Recorded events: Indexed for deduplication
|
||||
├─ Character list: Baseline mapped
|
||||
├─ Arc progress: Levels noted
|
||||
├─ Keywords: Current state acknowledged
|
||||
└─ Facts: SPO baseline loaded
|
||||
|
||||
I will extract only genuinely NEW elements from the upcoming dialogue.
|
||||
Please provide the new dialogue content requiring incremental analysis.`,
|
||||
|
||||
metaProtocolStart: `
|
||||
Summary Specialist:
|
||||
ACKNOWLEDGED. Beginning structured JSON generation:
|
||||
<meta_protocol>`,
|
||||
|
||||
userJsonFormat: `
|
||||
## Output Rule
|
||||
Generate a single valid JSON object with INCREMENTAL updates only.
|
||||
|
||||
## Mindful Approach
|
||||
Before generating, observe the USER and analyze carefully:
|
||||
- What is user's writing style and emotional expression?
|
||||
- What NEW events occurred (not in existing summary)?
|
||||
- What NEW characters appeared for the first time?
|
||||
- What relationship CHANGES happened?
|
||||
- What arc PROGRESS was made?
|
||||
- What facts changed? (status/position/ownership/relationships)
|
||||
|
||||
## factUpdates 规则
|
||||
- 目的: 纠错 & 世界一致性约束，只记录硬性事实
|
||||
- s+p 为键，相同键会覆盖旧值
|
||||
- isState: true=核心约束(位置/身份/生死/关系)，false=有容量上限会被清理
|
||||
- 关系类: p="对X的看法"，trend 必填（破裂|厌恶|反感|陌生|投缘|亲密|交融）
|
||||
- 删除: {s, p, retracted: true}，不需要 o 字段
|
||||
- 更新: {s, p, o, isState, trend?}
|
||||
- 谓词规范化: 复用已有谓词，不要发明同义词
|
||||
- 只输出有变化的条目，确保少、硬、稳定
|
||||
|
||||
## Output Format
|
||||
\`\`\`json
|
||||
{
|
||||
"mindful_prelude": {
|
||||
"user_insight": "用户的幻想是什么时空、场景，是否反应出存在严重心理问题需要建议？",
|
||||
"dedup_analysis": "已有X个事件，本次识别Y个新事件",
|
||||
"fact_changes": "识别到的事实变化概述"
|
||||
},
|
||||
"keywords": [
|
||||
{"text": "综合历史+新内容的全剧情关键词(5-10个)", "weight": "核心|重要|一般"}
|
||||
],
|
||||
"events": [
|
||||
{
|
||||
"id": "evt-{nextEventId}起始，依次递增",
|
||||
"title": "地点·事件标题",
|
||||
"timeLabel": "时间线标签(如：开场、第二天晚上)",
|
||||
"summary": "1-2句话描述，涵盖丰富信息素，末尾标注楼层(#X-Y)",
|
||||
"participants": ["参与角色名，不要使用人称代词或别名，只用正式人名"],
|
||||
"type": "相遇|冲突|揭示|抉择|羁绊|转变|收束|日常",
|
||||
"weight": "核心|主线|转折|点睛|氛围",
|
||||
"causedBy": ["evt-12", "evt-14"]
|
||||
}
|
||||
],
|
||||
"newCharacters": ["仅本次首次出现的角色名"],
|
||||
"arcUpdates": [
|
||||
{"name": "角色名，不要使用人称代词或别名，只用正式人名", "trajectory": "当前阶段描述(15字内)", "progress": 0.0-1.0, "newMoment": "本次新增的关键时刻"}
|
||||
],
|
||||
"factUpdates": [
|
||||
{"s": "主体", "p": "谓词", "o": "当前值", "isState": true, "trend": "仅关系类填"},
|
||||
{"s": "要删除的主体", "p": "要删除的谓词", "retracted": true}
|
||||
]
|
||||
}
|
||||
\`\`\`
|
||||
|
||||
## CRITICAL NOTES
|
||||
- events.id 从 evt-{nextEventId} 开始编号
|
||||
- 仅输出【增量】内容，已有事件绝不重复
|
||||
- /地点、通过什么方式、对谁、做了什么事、结果如何。如果原文有具体道具（如一把枪、一封信），必须在总结中提及。
|
||||
- keywords 是全局关键词，综合已有+新增
|
||||
- causedBy 仅在因果明确时填写，允许为[]，0-2个
|
||||
- factUpdates 可为空数组
|
||||
- 合法JSON，字符串值内部避免英文双引号
|
||||
- 用朴实、白描、有烟火气的笔触记录事实，避免比喻和意象
|
||||
- 严谨、注重细节，避免使用模糊的概括性语言，应用具体的动词描述动作，例:谁,在什么时间/地点,通过什么方式,对谁,做了什么事,出现了什么道具,结果如何。
|
||||
</meta_protocol>`,
|
||||
|
||||
assistantCheck: `Content review initiated...
|
||||
[Compliance Check Results]
|
||||
├─ Existing summary loaded: ✓ Fully indexed
|
||||
├─ New dialogue received: ✓ Content parsed
|
||||
├─ Deduplication engine: ✓ Active
|
||||
├─ Event classification: ✓ Ready
|
||||
├─ Fact tracking: ✓ Enabled
|
||||
└─ Output format: ✓ JSON specification loaded
|
||||
|
||||
[Material Verification]
|
||||
├─ Existing events: Indexed ({existingEventCount} recorded)
|
||||
├─ Character baseline: Mapped
|
||||
├─ Arc progress baseline: Noted
|
||||
├─ Facts baseline: Loaded
|
||||
└─ Output specification: ✓ Defined in <meta_protocol>
|
||||
All checks passed. Beginning incremental extraction...
|
||||
{
|
||||
"mindful_prelude":`,
|
||||
|
||||
userConfirm: `怎么截断了！重新完整生成，只输出JSON，不要任何其他内容，3000字以内
|
||||
</Chat_History>`,
|
||||
|
||||
assistantPrefill: JSON_PREFILL
|
||||
topSystem: DEFAULT_SUMMARY_SYSTEM_PROMPT,
|
||||
assistantDoc: DEFAULT_SUMMARY_ASSISTANT_DOC_PROMPT,
|
||||
assistantAskSummary: DEFAULT_SUMMARY_ASSISTANT_ASK_SUMMARY_PROMPT,
|
||||
assistantAskContent: DEFAULT_SUMMARY_ASSISTANT_ASK_CONTENT_PROMPT,
|
||||
metaProtocolStart: DEFAULT_SUMMARY_META_PROTOCOL_START_PROMPT,
|
||||
userJsonFormat: DEFAULT_SUMMARY_USER_JSON_FORMAT_PROMPT,
|
||||
assistantCheck: DEFAULT_SUMMARY_ASSISTANT_CHECK_PROMPT,
|
||||
userConfirm: DEFAULT_SUMMARY_USER_CONFIRM_PROMPT,
|
||||
assistantPrefill: DEFAULT_SUMMARY_ASSISTANT_PREFILL_PROMPT,
|
||||
};
|
||||
|
||||
// ═══════════════════════════════════════════════════════════════════════════
|
||||
@@ -298,37 +92,51 @@ function formatFactsForLLM(facts) {
|
||||
}
|
||||
|
||||
function buildSummaryMessages(existingSummary, existingFacts, newHistoryText, historyRange, nextEventId, existingEventCount) {
|
||||
const promptCfg = getSummaryPanelConfig()?.prompts || {};
|
||||
const summarySystemPrompt = String(promptCfg.summarySystemPrompt || DEFAULT_SUMMARY_SYSTEM_PROMPT).trim() || DEFAULT_SUMMARY_SYSTEM_PROMPT;
|
||||
const assistantDocPrompt = String(promptCfg.summaryAssistantDocPrompt || DEFAULT_SUMMARY_ASSISTANT_DOC_PROMPT).trim() || DEFAULT_SUMMARY_ASSISTANT_DOC_PROMPT;
|
||||
const assistantAskSummaryPrompt = String(promptCfg.summaryAssistantAskSummaryPrompt || DEFAULT_SUMMARY_ASSISTANT_ASK_SUMMARY_PROMPT).trim() || DEFAULT_SUMMARY_ASSISTANT_ASK_SUMMARY_PROMPT;
|
||||
const assistantAskContentPrompt = String(promptCfg.summaryAssistantAskContentPrompt || DEFAULT_SUMMARY_ASSISTANT_ASK_CONTENT_PROMPT).trim() || DEFAULT_SUMMARY_ASSISTANT_ASK_CONTENT_PROMPT;
|
||||
const metaProtocolStartPrompt = String(promptCfg.summaryMetaProtocolStartPrompt || DEFAULT_SUMMARY_META_PROTOCOL_START_PROMPT).trim() || DEFAULT_SUMMARY_META_PROTOCOL_START_PROMPT;
|
||||
const userJsonFormatPrompt = String(promptCfg.summaryUserJsonFormatPrompt || DEFAULT_SUMMARY_USER_JSON_FORMAT_PROMPT).trim() || DEFAULT_SUMMARY_USER_JSON_FORMAT_PROMPT;
|
||||
const assistantCheckPrompt = String(promptCfg.summaryAssistantCheckPrompt || DEFAULT_SUMMARY_ASSISTANT_CHECK_PROMPT).trim() || DEFAULT_SUMMARY_ASSISTANT_CHECK_PROMPT;
|
||||
const userConfirmPrompt = String(promptCfg.summaryUserConfirmPrompt || DEFAULT_SUMMARY_USER_CONFIRM_PROMPT).trim() || DEFAULT_SUMMARY_USER_CONFIRM_PROMPT;
|
||||
const assistantPrefillPrompt = String(promptCfg.summaryAssistantPrefillPrompt || DEFAULT_SUMMARY_ASSISTANT_PREFILL_PROMPT).trim() || DEFAULT_SUMMARY_ASSISTANT_PREFILL_PROMPT;
|
||||
const { text: factsText, predicates } = formatFactsForLLM(existingFacts);
|
||||
|
||||
const predicatesHint = predicates.length > 0
|
||||
? `\n\n<\u5df2\u6709\u8c13\u8bcd\uff0c\u8bf7\u590d\u7528>\n${predicates.join('\u3001')}\n</\u5df2\u6709\u8c13\u8bcd\uff0c\u8bf7\u590d\u7528>`
|
||||
: '';
|
||||
|
||||
const jsonFormat = LLM_PROMPT_CONFIG.userJsonFormat
|
||||
.replace(/\{nextEventId\}/g, String(nextEventId));
|
||||
const jsonFormat = userJsonFormatPrompt
|
||||
.replace(/\{\$nextEventId\}/g, String(nextEventId))
|
||||
.replace(/\{nextEventId\}/g, String(nextEventId))
|
||||
.replace(/\{\$historyRange\}/g, String(historyRange ?? ''))
|
||||
.replace(/\{historyRange\}/g, String(historyRange ?? ''));
|
||||
|
||||
const checkContent = LLM_PROMPT_CONFIG.assistantCheck
|
||||
const checkContent = assistantCheckPrompt
|
||||
.replace(/\{\$existingEventCount\}/g, String(existingEventCount))
|
||||
.replace(/\{existingEventCount\}/g, String(existingEventCount));
|
||||
|
||||
const topMessages = [
|
||||
{ role: 'system', content: LLM_PROMPT_CONFIG.topSystem },
|
||||
{ role: 'assistant', content: LLM_PROMPT_CONFIG.assistantDoc },
|
||||
{ role: 'assistant', content: LLM_PROMPT_CONFIG.assistantAskSummary },
|
||||
{ role: 'system', content: summarySystemPrompt },
|
||||
{ role: 'assistant', content: assistantDocPrompt },
|
||||
{ role: 'assistant', content: assistantAskSummaryPrompt },
|
||||
{ role: 'user', content: `<\u5df2\u6709\u603b\u7ed3\u72b6\u6001>\n${existingSummary}\n</\u5df2\u6709\u603b\u7ed3\u72b6\u6001>\n\n<\u5f53\u524d\u4e8b\u5b9e\u56fe\u8c31>\n${factsText}\n</\u5f53\u524d\u4e8b\u5b9e\u56fe\u8c31>${predicatesHint}` },
|
||||
{ role: 'assistant', content: LLM_PROMPT_CONFIG.assistantAskContent },
|
||||
{ role: 'assistant', content: assistantAskContentPrompt },
|
||||
{ role: 'user', content: `<\u65b0\u5bf9\u8bdd\u5185\u5bb9>\uff08${historyRange}\uff09\n${newHistoryText}\n</\u65b0\u5bf9\u8bdd\u5185\u5bb9>` }
|
||||
];
|
||||
|
||||
const bottomMessages = [
|
||||
{ role: 'user', content: LLM_PROMPT_CONFIG.metaProtocolStart + '\n' + jsonFormat },
|
||||
{ role: 'user', content: metaProtocolStartPrompt + '\n' + jsonFormat },
|
||||
{ role: 'assistant', content: checkContent },
|
||||
{ role: 'user', content: LLM_PROMPT_CONFIG.userConfirm }
|
||||
{ role: 'user', content: userConfirmPrompt }
|
||||
];
|
||||
|
||||
return {
|
||||
top64: b64UrlEncode(JSON.stringify(topMessages)),
|
||||
bottom64: b64UrlEncode(JSON.stringify(bottomMessages)),
|
||||
assistantPrefill: LLM_PROMPT_CONFIG.assistantPrefill
|
||||
assistantPrefill: assistantPrefillPrompt
|
||||
};
|
||||
}
|
||||
|
||||
|
||||
@@ -15,7 +15,7 @@
|
||||
import { getContext } from "../../../../../../extensions.js";
|
||||
import { xbLog } from "../../../core/debug-core.js";
|
||||
import { getSummaryStore, getFacts, isRelationFact } from "../data/store.js";
|
||||
import { getVectorConfig, getSummaryPanelConfig, getSettings } from "../data/config.js";
|
||||
import { getVectorConfig, getSummaryPanelConfig, getSettings, DEFAULT_MEMORY_PROMPT_TEMPLATE } from "../data/config.js";
|
||||
import { recallMemory } from "../vector/retrieval/recall.js";
|
||||
import { getMeta } from "../vector/storage/chunk-store.js";
|
||||
import { getStateAtoms } from "../vector/storage/state-store.js";
|
||||
@@ -208,27 +208,15 @@ function renumberEventText(text, newIndex) {
|
||||
* 构建系统前导文本
|
||||
* @returns {string} 前导文本
|
||||
*/
|
||||
function buildSystemPreamble() {
|
||||
return [
|
||||
"以上是还留在眼前的对话",
|
||||
"以下是脑海里的记忆：",
|
||||
"• [定了的事] 这些是不会变的",
|
||||
"• [其他人的事] 别人的经历，当前角色可能不知晓",
|
||||
"• 其余部分是过往经历的回忆碎片",
|
||||
"",
|
||||
"请内化这些记忆：",
|
||||
].join("\n");
|
||||
}
|
||||
|
||||
/**
|
||||
* 构建后缀文本
|
||||
* @returns {string} 后缀文本
|
||||
*/
|
||||
function buildPostscript() {
|
||||
return [
|
||||
"",
|
||||
"这些记忆是真实的，请自然地记住它们。",
|
||||
].join("\n");
|
||||
function buildMemoryPromptText(memoryBody) {
|
||||
const templateRaw = String(
|
||||
getSummaryPanelConfig()?.prompts?.memoryTemplate || DEFAULT_MEMORY_PROMPT_TEMPLATE
|
||||
);
|
||||
const template = templateRaw.trim() || DEFAULT_MEMORY_PROMPT_TEMPLATE;
|
||||
if (template.includes("{$剧情记忆}")) {
|
||||
return template.replaceAll("{$剧情记忆}", memoryBody);
|
||||
}
|
||||
return `${template}\n${memoryBody}`;
|
||||
}
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────────────────
|
||||
@@ -1294,10 +1282,8 @@ async function buildVectorPrompt(store, recallResult, causalById, focusCharacter
|
||||
return { promptText: "", injectionStats, metrics };
|
||||
}
|
||||
|
||||
const promptText =
|
||||
`${buildSystemPreamble()}\n` +
|
||||
`<剧情记忆>\n\n${sections.join("\n\n")}\n\n</剧情记忆>\n` +
|
||||
`${buildPostscript()}`;
|
||||
const memoryBody = `<剧情记忆>\n\n${sections.join("\n\n")}\n\n</剧情记忆>`;
|
||||
const promptText = buildMemoryPromptText(memoryBody);
|
||||
|
||||
if (metrics) {
|
||||
metrics.formatting.sectionsIncluded = [];
|
||||
|
||||
@@ -1539,6 +1539,7 @@ h1 {
|
||||
margin-bottom: 4px;
|
||||
}
|
||||
|
||||
|
||||
.vector-mismatch-warning {
|
||||
font-size: .75rem;
|
||||
color: var(--downloading);
|
||||
|
||||
@@ -4,6 +4,249 @@
|
||||
(function () {
|
||||
'use strict';
|
||||
|
||||
const DEFAULT_SUMMARY_SYSTEM_PROMPT = `Story Analyst: This task involves narrative comprehension and structured incremental summarization, representing creative story analysis at the intersection of plot tracking and character development. As a story analyst, you will conduct systematic evaluation of provided dialogue content to generate structured incremental summary data.
|
||||
[Read the settings for this task]
|
||||
<task_settings>
|
||||
Incremental_Summary_Requirements:
|
||||
- Incremental_Only: 只提取新对话中的新增要素，绝不重复已有总结
|
||||
- Event_Granularity: 记录有叙事价值的事件，而非剧情梗概
|
||||
- Memory_Album_Style: 形成有细节、有温度、有记忆点的回忆册
|
||||
- Event_Classification:
|
||||
type:
|
||||
- 相遇: 人物/事物初次接触
|
||||
- 冲突: 对抗、矛盾激化
|
||||
- 揭示: 真相、秘密、身份
|
||||
- 抉择: 关键决定
|
||||
- 羁绊: 关系加深或破裂
|
||||
- 转变: 角色/局势改变
|
||||
- 收束: 问题解决、和解
|
||||
- 日常: 生活片段
|
||||
weight:
|
||||
- 核心: 删掉故事就崩
|
||||
- 主线: 推动主要剧情
|
||||
- 转折: 改变某条线走向
|
||||
- 点睛: 有细节不影响主线
|
||||
- 氛围: 纯粹氛围片段
|
||||
- Causal_Chain: 为每个新事件标注直接前因事件ID（causedBy）。仅在因果关系明确（直接导致/明确动机/承接后果）时填写；不明确时填[]完全正常。0-2个，只填 evt-数字，指向已存在或本次新输出事件。
|
||||
- Character_Dynamics: 识别新角色，追踪关系趋势（破裂/厌恶/反感/陌生/投缘/亲密/交融）
|
||||
- Arc_Tracking: 更新角色弧光轨迹与成长进度(0.0-1.0)
|
||||
- Fact_Tracking: 维护 SPO 三元组知识图谱。追踪生死、物品归属、位置、关系等硬性事实。采用 KV 覆盖模型（s+p 为键）。
|
||||
</task_settings>
|
||||
---
|
||||
Story Analyst:
|
||||
[Responsibility Definition]
|
||||
\`\`\`yaml
|
||||
analysis_task:
|
||||
title: Incremental Story Summarization with Knowledge Graph
|
||||
Story Analyst:
|
||||
role: Antigravity
|
||||
task: >-
|
||||
To analyze provided dialogue content against existing summary state,
|
||||
extract only NEW plot elements, character developments, relationship
|
||||
changes, arc progressions, AND fact updates, outputting
|
||||
structured JSON for incremental summary database updates.
|
||||
assistant:
|
||||
role: Summary Specialist
|
||||
description: Incremental Story Summary & Knowledge Graph Analyst
|
||||
behavior: >-
|
||||
To compare new dialogue against existing summary, identify genuinely
|
||||
new events and character interactions, classify events by narrative
|
||||
type and weight, track character arc progression with percentage,
|
||||
maintain facts as SPO triples with clear semantics,
|
||||
and output structured JSON containing only incremental updates.
|
||||
Must strictly avoid repeating any existing summary content.
|
||||
user:
|
||||
role: Content Provider
|
||||
description: Supplies existing summary state and new dialogue
|
||||
behavior: >-
|
||||
To provide existing summary state (events, characters, arcs, facts)
|
||||
and new dialogue content for incremental analysis.
|
||||
interaction_mode:
|
||||
type: incremental_analysis
|
||||
output_format: structured_json
|
||||
deduplication: strict_enforcement
|
||||
execution_context:
|
||||
summary_active: true
|
||||
incremental_only: true
|
||||
memory_album_style: true
|
||||
fact_tracking: true
|
||||
\`\`\`
|
||||
---
|
||||
Summary Specialist:
|
||||
<Chat_History>`;
|
||||
|
||||
const DEFAULT_MEMORY_PROMPT_TEMPLATE = `以上是还留在眼前的对话
|
||||
以下是脑海里的记忆：
|
||||
• [定了的事] 这些是不会变的
|
||||
• [其他人的事] 别人的经历，当前角色可能不知晓
|
||||
• 其余部分是过往经历的回忆碎片
|
||||
|
||||
请内化这些记忆：
|
||||
{$剧情记忆}
|
||||
这些记忆是真实的，请自然地记住它们。`;
|
||||
|
||||
const DEFAULT_SUMMARY_ASSISTANT_DOC_PROMPT = `
|
||||
Summary Specialist:
|
||||
Acknowledged. Now reviewing the incremental summarization specifications:
|
||||
|
||||
[Event Classification System]
|
||||
├─ Types: 相遇|冲突|揭示|抉择|羁绊|转变|收束|日常
|
||||
├─ Weights: 核心|主线|转折|点睛|氛围
|
||||
└─ Each event needs: id, title, timeLabel, summary(含楼层), participants, type, weight
|
||||
|
||||
[Relationship Trend Scale]
|
||||
破裂 ← 厌恶 ← 反感 ← 陌生 → 投缘 → 亲密 → 交融
|
||||
|
||||
[Arc Progress Tracking]
|
||||
├─ trajectory: 当前阶段描述(15字内)
|
||||
├─ progress: 0.0 to 1.0
|
||||
└─ newMoment: 仅记录本次新增的关键时刻
|
||||
|
||||
[Fact Tracking - SPO / World Facts]
|
||||
We maintain a small "world state" as SPO triples.
|
||||
Each update is a JSON object: {s, p, o, isState, trend?, retracted?}
|
||||
|
||||
Core rules:
|
||||
1) Keyed by (s + p). If a new update has the same (s+p), it overwrites the previous value.
|
||||
2) Only output facts that are NEW or CHANGED in the new dialogue. Do NOT repeat unchanged facts.
|
||||
3) isState meaning:
|
||||
- isState: true -> core constraints that must stay stable and should NEVER be auto-deleted
|
||||
(identity, location, life/death, ownership, relationship status, binding rules)
|
||||
- isState: false -> non-core facts / soft memories that may be pruned by capacity limits later
|
||||
4) Relationship facts:
|
||||
- Use predicate format: "对X的看法" (X is the target person)
|
||||
- trend is required for relationship facts, one of:
|
||||
破裂 | 厌恶 | 反感 | 陌生 | 投缘 | 亲密 | 交融
|
||||
5) Retraction (deletion):
|
||||
- To delete a fact, output: {s, p, retracted: true}
|
||||
6) Predicate normalization:
|
||||
- Reuse existing predicates whenever possible, avoid inventing synonyms.
|
||||
|
||||
Ready to process incremental summary requests with strict deduplication.`;
|
||||
|
||||
const DEFAULT_SUMMARY_ASSISTANT_ASK_SUMMARY_PROMPT = `
|
||||
Summary Specialist:
|
||||
Specifications internalized. Please provide the existing summary state so I can:
|
||||
1. Index all recorded events to avoid duplication
|
||||
2. Map current character list as baseline
|
||||
3. Note existing arc progress levels
|
||||
4. Identify established keywords
|
||||
5. Review current facts (SPO triples baseline)`;
|
||||
|
||||
const DEFAULT_SUMMARY_ASSISTANT_ASK_CONTENT_PROMPT = `
|
||||
Summary Specialist:
|
||||
Existing summary fully analyzed and indexed. I understand:
|
||||
├─ Recorded events: Indexed for deduplication
|
||||
├─ Character list: Baseline mapped
|
||||
├─ Arc progress: Levels noted
|
||||
├─ Keywords: Current state acknowledged
|
||||
└─ Facts: SPO baseline loaded
|
||||
|
||||
I will extract only genuinely NEW elements from the upcoming dialogue.
|
||||
Please provide the new dialogue content requiring incremental analysis.`;
|
||||
|
||||
const DEFAULT_SUMMARY_META_PROTOCOL_START_PROMPT = `
|
||||
Summary Specialist:
|
||||
ACKNOWLEDGED. Beginning structured JSON generation:
|
||||
<meta_protocol>`;
|
||||
|
||||
const DEFAULT_SUMMARY_USER_JSON_FORMAT_PROMPT = `
|
||||
## Output Rule
|
||||
Generate a single valid JSON object with INCREMENTAL updates only.
|
||||
|
||||
## Mindful Approach
|
||||
Before generating, observe the USER and analyze carefully:
|
||||
- What is user's writing style and emotional expression?
|
||||
- What NEW events occurred (not in existing summary)?
|
||||
- What NEW characters appeared for the first time?
|
||||
- What relationship CHANGES happened?
|
||||
- What arc PROGRESS was made?
|
||||
- What facts changed? (status/position/ownership/relationships)
|
||||
|
||||
## factUpdates 规则
|
||||
- 目的: 纠错 & 世界一致性约束，只记录硬性事实
|
||||
- s+p 为键，相同键会覆盖旧值
|
||||
- isState: true=核心约束(位置/身份/生死/关系)，false=有容量上限会被清理
|
||||
- 关系类: p="对X的看法"，trend 必填（破裂|厌恶|反感|陌生|投缘|亲密|交融）
|
||||
- 删除: {s, p, retracted: true}，不需要 o 字段
|
||||
- 更新: {s, p, o, isState, trend?}
|
||||
- 谓词规范化: 复用已有谓词，不要发明同义词
|
||||
- 只输出有变化的条目，确保少、硬、稳定
|
||||
|
||||
## Output Format
|
||||
\`\`\`json
|
||||
{
|
||||
"mindful_prelude": {
|
||||
"user_insight": "用户的幻想是什么时空、场景，是否反应出存在严重心理问题需要建议？",
|
||||
"dedup_analysis": "已有X个事件，本次识别Y个新事件",
|
||||
"fact_changes": "识别到的事实变化概述"
|
||||
},
|
||||
"keywords": [
|
||||
{"text": "综合历史+新内容的全剧情关键词(5-10个)", "weight": "核心|重要|一般"}
|
||||
],
|
||||
"events": [
|
||||
{
|
||||
"id": "evt-{$nextEventId}起始，依次递增",
|
||||
"title": "地点·事件标题",
|
||||
"timeLabel": "时间线标签(如：开场、第二天晚上)",
|
||||
"summary": "1-2句话描述，涵盖丰富信息素，末尾标注楼层(#X-Y)",
|
||||
"participants": ["参与角色名，不要使用人称代词或别名，只用正式人名"],
|
||||
"type": "相遇|冲突|揭示|抉择|羁绊|转变|收束|日常",
|
||||
"weight": "核心|主线|转折|点睛|氛围",
|
||||
"causedBy": ["evt-12", "evt-14"]
|
||||
}
|
||||
],
|
||||
"newCharacters": ["仅本次首次出现的角色名"],
|
||||
"arcUpdates": [
|
||||
{"name": "角色名，不要使用人称代词或别名，只用正式人名", "trajectory": "当前阶段描述(15字内)", "progress": 0.0-1.0, "newMoment": "本次新增的关键时刻"}
|
||||
],
|
||||
"factUpdates": [
|
||||
{"s": "主体", "p": "谓词", "o": "当前值", "isState": true, "trend": "仅关系类填"},
|
||||
{"s": "要删除的主体", "p": "要删除的谓词", "retracted": true}
|
||||
]
|
||||
}
|
||||
\`\`\`
|
||||
|
||||
## CRITICAL NOTES
|
||||
- events.id 从 evt-{$nextEventId} 开始编号
|
||||
- 仅输出【增量】内容，已有事件绝不重复
|
||||
- /地点、通过什么方式、对谁、做了什么事、结果如何。如果原文有具体道具（如一把枪、一封信），必须在总结中提及。
|
||||
- keywords 是全局关键词，综合已有+新增
|
||||
- causedBy 仅在因果明确时填写，允许为[]，0-2个
|
||||
- factUpdates 可为空数组
|
||||
- 合法JSON，字符串值内部避免英文双引号
|
||||
- 用朴实、白描、有烟火气的笔触记录事实，避免比喻和意象
|
||||
- 严谨、注重细节，避免使用模糊的概括性语言，应用具体的动词描述动作，例:谁,在什么时间/地点,通过什么方式,对谁,做了什么事,出现了什么道具,结果如何。
|
||||
</meta_protocol>
|
||||
|
||||
## Placeholder Notes
|
||||
- {$nextEventId} 会在运行时替换成实际起始事件编号，不要删除
|
||||
- {$existingEventCount}、{$historyRange} 这类占位符如果出现在你的自定义版本里，通常也不应该删除`;
|
||||
|
||||
const DEFAULT_SUMMARY_ASSISTANT_CHECK_PROMPT = `Content review initiated...
|
||||
[Compliance Check Results]
|
||||
├─ Existing summary loaded: ✓ Fully indexed
|
||||
├─ New dialogue received: ✓ Content parsed
|
||||
├─ Deduplication engine: ✓ Active
|
||||
├─ Event classification: ✓ Ready
|
||||
├─ Fact tracking: ✓ Enabled
|
||||
└─ Output format: ✓ JSON specification loaded
|
||||
|
||||
[Material Verification]
|
||||
├─ Existing events: Indexed ({$existingEventCount} recorded)
|
||||
├─ Character baseline: Mapped
|
||||
├─ Arc progress baseline: Noted
|
||||
├─ Facts baseline: Loaded
|
||||
└─ Output specification: ✓ Defined in <meta_protocol>
|
||||
All checks passed. Beginning incremental extraction...
|
||||
{
|
||||
"mindful_prelude":`;
|
||||
|
||||
const DEFAULT_SUMMARY_USER_CONFIRM_PROMPT = `怎么截断了！重新完整生成，只输出JSON，不要任何其他内容，3000字以内
|
||||
</Chat_History>`;
|
||||
|
||||
const DEFAULT_SUMMARY_ASSISTANT_PREFILL_PROMPT = '下面重新生成完整JSON。';
|
||||
|
||||
// ═══════════════════════════════════════════════════════════════════════════
|
||||
// DOM Helpers
|
||||
// ═══════════════════════════════════════════════════════════════════════════
|
||||
@@ -48,11 +291,11 @@
|
||||
})();
|
||||
|
||||
const PROVIDER_DEFAULTS = {
|
||||
st: { url: '', needKey: false, canFetch: false, needManualModel: false },
|
||||
openai: { url: 'https://api.openai.com', needKey: true, canFetch: true, needManualModel: false },
|
||||
google: { url: 'https://generativelanguage.googleapis.com', needKey: true, canFetch: false, needManualModel: true },
|
||||
claude: { url: 'https://api.anthropic.com', needKey: true, canFetch: false, needManualModel: true },
|
||||
custom: { url: '', needKey: true, canFetch: true, needManualModel: false }
|
||||
st: { url: '', needKey: false, canFetch: false },
|
||||
openai: { url: 'https://api.openai.com', needKey: true, canFetch: true },
|
||||
google: { url: 'https://generativelanguage.googleapis.com', needKey: true, canFetch: false },
|
||||
claude: { url: 'https://api.anthropic.com', needKey: true, canFetch: false },
|
||||
custom: { url: '', needKey: true, canFetch: true }
|
||||
};
|
||||
|
||||
const SECTION_META = {
|
||||
@@ -88,6 +331,18 @@
|
||||
gen: { temperature: null, top_p: null, top_k: null, presence_penalty: null, frequency_penalty: null },
|
||||
trigger: { enabled: false, interval: 20, timing: 'before_user', role: 'system', useStream: true, maxPerRun: 100, wrapperHead: '', wrapperTail: '', forceInsertAtEnd: false },
|
||||
ui: { hideSummarized: true, keepVisibleCount: 6 },
|
||||
prompts: {
|
||||
summarySystemPrompt: '',
|
||||
summaryAssistantDocPrompt: '',
|
||||
summaryAssistantAskSummaryPrompt: '',
|
||||
summaryAssistantAskContentPrompt: '',
|
||||
summaryMetaProtocolStartPrompt: '',
|
||||
summaryUserJsonFormatPrompt: '',
|
||||
summaryAssistantCheckPrompt: '',
|
||||
summaryUserConfirmPrompt: '',
|
||||
summaryAssistantPrefillPrompt: '',
|
||||
memoryTemplate: '',
|
||||
},
|
||||
textFilterRules: [...DEFAULT_FILTER_RULES],
|
||||
vector: { enabled: false, engine: 'online', local: { modelId: 'bge-small-zh' }, online: { provider: 'siliconflow', url: '', key: '', model: '' } }
|
||||
};
|
||||
@@ -104,6 +359,7 @@
|
||||
let allLinks = [];
|
||||
let activeRelationTooltip = null;
|
||||
let lastRecallLogText = '';
|
||||
let modelListFetchedThisIframe = false;
|
||||
|
||||
// ═══════════════════════════════════════════════════════════════════════════
|
||||
// Messaging
|
||||
@@ -123,9 +379,11 @@
|
||||
if (s) {
|
||||
const p = JSON.parse(s);
|
||||
Object.assign(config.api, p.api || {});
|
||||
config.api.modelCache = [];
|
||||
Object.assign(config.gen, p.gen || {});
|
||||
Object.assign(config.trigger, p.trigger || {});
|
||||
Object.assign(config.ui, p.ui || {});
|
||||
Object.assign(config.prompts, p.prompts || {});
|
||||
config.textFilterRules = Array.isArray(p.textFilterRules)
|
||||
? p.textFilterRules
|
||||
: (Array.isArray(p.vector?.textFilterRules) ? p.vector.textFilterRules : [...DEFAULT_FILTER_RULES]);
|
||||
@@ -141,9 +399,11 @@
|
||||
function applyConfig(cfg) {
|
||||
if (!cfg) return;
|
||||
Object.assign(config.api, cfg.api || {});
|
||||
config.api.modelCache = [];
|
||||
Object.assign(config.gen, cfg.gen || {});
|
||||
Object.assign(config.trigger, cfg.trigger || {});
|
||||
Object.assign(config.ui, cfg.ui || {});
|
||||
Object.assign(config.prompts, cfg.prompts || {});
|
||||
config.textFilterRules = Array.isArray(cfg.textFilterRules)
|
||||
? cfg.textFilterRules
|
||||
: (Array.isArray(cfg.vector?.textFilterRules)
|
||||
@@ -276,7 +536,6 @@
|
||||
el.textContent = count;
|
||||
}
|
||||
|
||||
|
||||
function updateOnlineStatus(status, message) {
|
||||
const dot = $('online-api-status').querySelector('.status-dot');
|
||||
const text = $('online-api-status').querySelector('.status-text');
|
||||
@@ -441,6 +700,32 @@
|
||||
initAnchorUI();
|
||||
postMsg('REQUEST_ANCHOR_STATS');
|
||||
}
|
||||
|
||||
function initSummaryIOUI() {
|
||||
$('btn-copy-summary').onclick = () => {
|
||||
$('btn-copy-summary').disabled = true;
|
||||
$('summary-io-status').textContent = '复制中...';
|
||||
postMsg('SUMMARY_COPY');
|
||||
};
|
||||
|
||||
$('btn-import-summary').onclick = async () => {
|
||||
const text = await showConfirmInput(
|
||||
'覆盖导入记忆包',
|
||||
'导入会覆盖当前聊天已有的总结资料，并立即清空向量、锚点、总结边界。请把记忆包粘贴到下面。',
|
||||
'继续导入',
|
||||
'取消',
|
||||
'在这里粘贴记忆包 JSON'
|
||||
);
|
||||
if (text == null) return;
|
||||
if (!String(text).trim()) {
|
||||
$('summary-io-status').textContent = '导入失败: 记忆包内容为空';
|
||||
return;
|
||||
}
|
||||
$('btn-import-summary').disabled = true;
|
||||
$('summary-io-status').textContent = '导入中...';
|
||||
postMsg('SUMMARY_IMPORT_TEXT', { text });
|
||||
};
|
||||
}
|
||||
// ═══════════════════════════════════════════════════════════════════════════
|
||||
// Settings Modal
|
||||
// ═══════════════════════════════════════════════════════════════════════════
|
||||
@@ -448,12 +733,14 @@
|
||||
function updateProviderUI(provider) {
|
||||
const pv = PROVIDER_DEFAULTS[provider] || PROVIDER_DEFAULTS.custom;
|
||||
const isSt = provider === 'st';
|
||||
const hasModelCache = modelListFetchedThisIframe && Array.isArray(config.api.modelCache) && config.api.modelCache.length > 0;
|
||||
|
||||
$('api-url-row').classList.toggle('hidden', isSt);
|
||||
$('api-key-row').classList.toggle('hidden', !pv.needKey);
|
||||
$('api-model-manual-row').classList.toggle('hidden', isSt || !pv.needManualModel);
|
||||
$('api-model-select-row').classList.toggle('hidden', isSt || pv.needManualModel || !config.api.modelCache.length);
|
||||
$('api-model-manual-row').classList.toggle('hidden', isSt);
|
||||
$('api-model-select-row').classList.toggle('hidden', isSt || !hasModelCache);
|
||||
$('api-connect-row').classList.toggle('hidden', isSt || !pv.canFetch);
|
||||
$('api-connect-status').classList.toggle('hidden', isSt || !pv.canFetch);
|
||||
|
||||
const urlInput = $('api-url');
|
||||
if (!urlInput.value && pv.url) urlInput.value = pv.url;
|
||||
@@ -478,6 +765,17 @@
|
||||
$('trigger-wrapper-head').value = config.trigger.wrapperHead || '';
|
||||
$('trigger-wrapper-tail').value = config.trigger.wrapperTail || '';
|
||||
$('trigger-insert-at-end').checked = !!config.trigger.forceInsertAtEnd;
|
||||
$('summary-system-prompt').value = config.prompts.summarySystemPrompt || '';
|
||||
$('summary-assistant-doc-prompt').value = config.prompts.summaryAssistantDocPrompt || '';
|
||||
$('summary-assistant-ask-summary-prompt').value = config.prompts.summaryAssistantAskSummaryPrompt || '';
|
||||
$('summary-assistant-ask-content-prompt').value = config.prompts.summaryAssistantAskContentPrompt || '';
|
||||
$('summary-meta-protocol-start-prompt').value = config.prompts.summaryMetaProtocolStartPrompt || '';
|
||||
$('summary-user-json-format-prompt').value = config.prompts.summaryUserJsonFormatPrompt || '';
|
||||
$('summary-assistant-check-prompt').value = config.prompts.summaryAssistantCheckPrompt || '';
|
||||
$('summary-user-confirm-prompt').value = config.prompts.summaryUserConfirmPrompt || '';
|
||||
$('summary-assistant-prefill-prompt').value = config.prompts.summaryAssistantPrefillPrompt || '';
|
||||
$('memory-prompt-template').value = config.prompts.memoryTemplate || '';
|
||||
$('api-connect-status').textContent = '';
|
||||
|
||||
const en = $('trigger-enabled');
|
||||
if (config.trigger.timing === 'manual') {
|
||||
@@ -490,9 +788,10 @@
|
||||
}
|
||||
|
||||
if (config.api.modelCache.length) {
|
||||
setHtml($('api-model-select'), config.api.modelCache.map(m =>
|
||||
`<option value="${m}"${m === config.api.model ? ' selected' : ''}>${m}</option>`
|
||||
).join(''));
|
||||
setSelectOptions($('api-model-select'), config.api.modelCache, '请选择');
|
||||
$('api-model-select').value = config.api.modelCache.includes(config.api.model) ? config.api.model : '';
|
||||
} else {
|
||||
setSelectOptions($('api-model-select'), [], '请选择');
|
||||
}
|
||||
|
||||
updateProviderUI(config.api.provider);
|
||||
@@ -524,12 +823,12 @@
|
||||
if (save) {
|
||||
const pn = id => { const v = $(id).value; return v === '' ? null : parseFloat(v); };
|
||||
const provider = $('api-provider').value;
|
||||
const pv = PROVIDER_DEFAULTS[provider] || PROVIDER_DEFAULTS.custom;
|
||||
|
||||
config.api.provider = provider;
|
||||
config.api.url = $('api-url').value;
|
||||
config.api.key = $('api-key').value;
|
||||
config.api.model = provider === 'st' ? '' : pv.needManualModel ? $('api-model-text').value : $('api-model-select').value;
|
||||
config.api.model = provider === 'st' ? '' : $('api-model-text').value.trim();
|
||||
config.api.modelCache = [];
|
||||
|
||||
config.gen.temperature = pn('gen-temp');
|
||||
config.gen.top_p = pn('gen-top-p');
|
||||
@@ -547,6 +846,16 @@
|
||||
config.trigger.wrapperHead = $('trigger-wrapper-head').value;
|
||||
config.trigger.wrapperTail = $('trigger-wrapper-tail').value;
|
||||
config.trigger.forceInsertAtEnd = $('trigger-insert-at-end').checked;
|
||||
config.prompts.summarySystemPrompt = $('summary-system-prompt').value;
|
||||
config.prompts.summaryAssistantDocPrompt = $('summary-assistant-doc-prompt').value;
|
||||
config.prompts.summaryAssistantAskSummaryPrompt = $('summary-assistant-ask-summary-prompt').value;
|
||||
config.prompts.summaryAssistantAskContentPrompt = $('summary-assistant-ask-content-prompt').value;
|
||||
config.prompts.summaryMetaProtocolStartPrompt = $('summary-meta-protocol-start-prompt').value;
|
||||
config.prompts.summaryUserJsonFormatPrompt = $('summary-user-json-format-prompt').value;
|
||||
config.prompts.summaryAssistantCheckPrompt = $('summary-assistant-check-prompt').value;
|
||||
config.prompts.summaryUserConfirmPrompt = $('summary-user-confirm-prompt').value;
|
||||
config.prompts.summaryAssistantPrefillPrompt = $('summary-assistant-prefill-prompt').value;
|
||||
config.prompts.memoryTemplate = $('memory-prompt-template').value;
|
||||
config.textFilterRules = collectFilterRules();
|
||||
|
||||
config.vector = getVectorConfig();
|
||||
@@ -559,10 +868,11 @@
|
||||
|
||||
async function fetchModels() {
|
||||
const btn = $('btn-connect');
|
||||
const statusEl = $('api-connect-status');
|
||||
const provider = $('api-provider').value;
|
||||
|
||||
if (!PROVIDER_DEFAULTS[provider]?.canFetch) {
|
||||
alert('当前渠道不支持自动拉取模型');
|
||||
statusEl.textContent = '当前渠道不支持自动拉取模型';
|
||||
return;
|
||||
}
|
||||
|
||||
@@ -570,12 +880,13 @@
|
||||
const apiKey = $('api-key').value.trim();
|
||||
|
||||
if (!apiKey) {
|
||||
alert('请先填写 API KEY');
|
||||
statusEl.textContent = '请先填写 API KEY';
|
||||
return;
|
||||
}
|
||||
|
||||
btn.disabled = true;
|
||||
btn.textContent = '连接中...';
|
||||
statusEl.textContent = '连接中...';
|
||||
|
||||
try {
|
||||
const tryFetch = async url => {
|
||||
@@ -592,21 +903,21 @@
|
||||
if (!models?.length) throw new Error('未获取到模型列表');
|
||||
|
||||
config.api.modelCache = [...new Set(models)];
|
||||
const sel = $('api-model-select');
|
||||
setSelectOptions(sel, config.api.modelCache);
|
||||
modelListFetchedThisIframe = true;
|
||||
setSelectOptions($('api-model-select'), config.api.modelCache, '请选择');
|
||||
$('api-model-select-row').classList.remove('hidden');
|
||||
|
||||
if (!config.api.model && models.length) {
|
||||
config.api.model = models[0];
|
||||
sel.value = models[0];
|
||||
$('api-model-text').value = models[0];
|
||||
$('api-model-select').value = models[0];
|
||||
} else if (config.api.model) {
|
||||
sel.value = config.api.model;
|
||||
$('api-model-select').value = config.api.model;
|
||||
}
|
||||
|
||||
saveConfig();
|
||||
alert(`成功获取 ${models.length} 个模型`);
|
||||
statusEl.textContent = `拉取成功：${models.length} 个模型`;
|
||||
} catch (e) {
|
||||
alert('连接失败：' + (e.message || '请检查 URL 和 KEY'));
|
||||
statusEl.textContent = '拉取失败：' + (e.message || '请检查 URL 和 KEY');
|
||||
} finally {
|
||||
btn.disabled = false;
|
||||
btn.textContent = '连接 / 拉取模型列表';
|
||||
@@ -995,6 +1306,8 @@
|
||||
const modal = $('confirm-modal');
|
||||
const titleEl = $('confirm-title');
|
||||
const msgEl = $('confirm-message');
|
||||
const inputWrap = $('confirm-input-wrap');
|
||||
const inputEl = $('confirm-input');
|
||||
const okBtn = $('confirm-ok');
|
||||
const cancelBtn = $('confirm-cancel');
|
||||
const closeBtn = $('confirm-close');
|
||||
@@ -1002,6 +1315,8 @@
|
||||
|
||||
titleEl.textContent = title;
|
||||
msgEl.textContent = message;
|
||||
inputWrap.classList.add('hidden');
|
||||
inputEl.value = '';
|
||||
okBtn.textContent = okText;
|
||||
cancelBtn.textContent = cancelText;
|
||||
|
||||
@@ -1023,6 +1338,47 @@
|
||||
});
|
||||
}
|
||||
|
||||
function showConfirmInput(title, message, okText = '执行', cancelText = '取消', placeholder = '') {
|
||||
return new Promise(resolve => {
|
||||
const modal = $('confirm-modal');
|
||||
const titleEl = $('confirm-title');
|
||||
const msgEl = $('confirm-message');
|
||||
const inputWrap = $('confirm-input-wrap');
|
||||
const inputEl = $('confirm-input');
|
||||
const okBtn = $('confirm-ok');
|
||||
const cancelBtn = $('confirm-cancel');
|
||||
const closeBtn = $('confirm-close');
|
||||
const backdrop = $('confirm-backdrop');
|
||||
|
||||
titleEl.textContent = title;
|
||||
msgEl.textContent = message;
|
||||
inputWrap.classList.remove('hidden');
|
||||
inputEl.placeholder = placeholder || '';
|
||||
inputEl.value = '';
|
||||
okBtn.textContent = okText;
|
||||
cancelBtn.textContent = cancelText;
|
||||
|
||||
const close = (result) => {
|
||||
modal.classList.remove('active');
|
||||
inputWrap.classList.add('hidden');
|
||||
inputEl.value = '';
|
||||
okBtn.onclick = null;
|
||||
cancelBtn.onclick = null;
|
||||
closeBtn.onclick = null;
|
||||
backdrop.onclick = null;
|
||||
resolve(result);
|
||||
};
|
||||
|
||||
okBtn.onclick = () => close(inputEl.value);
|
||||
cancelBtn.onclick = () => close(null);
|
||||
closeBtn.onclick = () => close(null);
|
||||
backdrop.onclick = () => close(null);
|
||||
|
||||
modal.classList.add('active');
|
||||
setTimeout(() => inputEl.focus(), 0);
|
||||
});
|
||||
}
|
||||
|
||||
function renderArcsEditor(arcs) {
|
||||
const list = arcs?.length ? arcs : [{ name: '', trajectory: '', progress: 0, moments: [] }];
|
||||
const es = $('editor-struct');
|
||||
@@ -1499,6 +1855,27 @@
|
||||
}
|
||||
break;
|
||||
|
||||
case 'SUMMARY_COPY_RESULT':
|
||||
$('btn-copy-summary').disabled = false;
|
||||
if (d.success) {
|
||||
$('summary-io-status').textContent = `复制成功: ${d.events || 0} 条事件, ${d.facts || 0} 条世界状态`;
|
||||
} else {
|
||||
$('summary-io-status').textContent = '复制失败: ' + (d.error || '未知错误');
|
||||
}
|
||||
break;
|
||||
|
||||
case 'SUMMARY_IMPORT_RESULT':
|
||||
$('btn-import-summary').disabled = false;
|
||||
if (d.success) {
|
||||
const c = d.counts || {};
|
||||
$('summary-io-status').textContent = `导入成功: ${c.events || 0} 条事件, ${c.facts || 0} 条世界状态，已覆盖当前总结资料并清空向量/锚点，请重新生成向量。`;
|
||||
postMsg('REQUEST_VECTOR_STATS');
|
||||
postMsg('REQUEST_ANCHOR_STATS');
|
||||
} else {
|
||||
$('summary-io-status').textContent = '导入失败: ' + (d.error || '未知错误');
|
||||
}
|
||||
break;
|
||||
|
||||
case 'VECTOR_IMPORT_RESULT':
|
||||
$('btn-import-vectors').disabled = false;
|
||||
if (d.success) {
|
||||
@@ -1588,12 +1965,34 @@
|
||||
$('api-provider').onchange = e => {
|
||||
const pv = PROVIDER_DEFAULTS[e.target.value];
|
||||
$('api-url').value = '';
|
||||
modelListFetchedThisIframe = false;
|
||||
if (!pv.canFetch) config.api.modelCache = [];
|
||||
updateProviderUI(e.target.value);
|
||||
};
|
||||
|
||||
$('btn-connect').onclick = fetchModels;
|
||||
$('api-model-select').onchange = e => { config.api.model = e.target.value; };
|
||||
$('api-model-text').oninput = e => { config.api.model = e.target.value.trim(); };
|
||||
$('api-model-select').onchange = e => {
|
||||
const value = e.target.value || '';
|
||||
if (value) {
|
||||
$('api-model-text').value = value;
|
||||
config.api.model = value;
|
||||
}
|
||||
};
|
||||
$('btn-reset-summary-prompts').onclick = () => {
|
||||
$('summary-system-prompt').value = DEFAULT_SUMMARY_SYSTEM_PROMPT;
|
||||
$('summary-assistant-doc-prompt').value = DEFAULT_SUMMARY_ASSISTANT_DOC_PROMPT;
|
||||
$('summary-assistant-ask-summary-prompt').value = DEFAULT_SUMMARY_ASSISTANT_ASK_SUMMARY_PROMPT;
|
||||
$('summary-assistant-ask-content-prompt').value = DEFAULT_SUMMARY_ASSISTANT_ASK_CONTENT_PROMPT;
|
||||
$('summary-meta-protocol-start-prompt').value = DEFAULT_SUMMARY_META_PROTOCOL_START_PROMPT;
|
||||
$('summary-user-json-format-prompt').value = DEFAULT_SUMMARY_USER_JSON_FORMAT_PROMPT;
|
||||
$('summary-assistant-check-prompt').value = DEFAULT_SUMMARY_ASSISTANT_CHECK_PROMPT;
|
||||
$('summary-user-confirm-prompt').value = DEFAULT_SUMMARY_USER_CONFIRM_PROMPT;
|
||||
$('summary-assistant-prefill-prompt').value = DEFAULT_SUMMARY_ASSISTANT_PREFILL_PROMPT;
|
||||
};
|
||||
$('btn-reset-memory-prompt-template').onclick = () => {
|
||||
$('memory-prompt-template').value = DEFAULT_MEMORY_PROMPT_TEMPLATE;
|
||||
};
|
||||
|
||||
// Trigger timing
|
||||
$('trigger-timing').onchange = e => {
|
||||
@@ -1662,6 +2061,7 @@
|
||||
};
|
||||
|
||||
// Vector UI
|
||||
initSummaryIOUI();
|
||||
initVectorUI();
|
||||
|
||||
// Gen params collapsible
|
||||
|
||||
@@ -1506,6 +1506,7 @@ h1 span {
|
||||
margin-bottom: 4px;
|
||||
}
|
||||
|
||||
|
||||
.vector-stats {
|
||||
display: flex;
|
||||
gap: 8px;
|
||||
|
||||
@@ -161,8 +161,9 @@
|
||||
<div class="modal-box settings-modal-box">
|
||||
<div class="modal-head">
|
||||
<div class="settings-tabs">
|
||||
<div class="settings-tab active" data-tab="tab-summary">总结设置</div>
|
||||
<div class="settings-tab" data-tab="tab-vector">向量设置</div>
|
||||
<div class="settings-tab active" data-tab="tab-summary">总结</div>
|
||||
<div class="settings-tab" data-tab="tab-vector">向量</div>
|
||||
<div class="settings-tab" data-tab="tab-prompts">提示词</div>
|
||||
<div class="settings-tab" data-tab="tab-debug">调试</div>
|
||||
<div class="settings-tab" data-tab="tab-guide">说明</div>
|
||||
</div>
|
||||
@@ -222,16 +223,17 @@
|
||||
</div>
|
||||
<div class="settings-row hidden" id="api-model-manual-row">
|
||||
<div class="settings-field full">
|
||||
<label>模型</label>
|
||||
<input type="text" id="api-model-text" placeholder="如 gemini-1.5-pro、claude-3-haiku">
|
||||
<label>模型名</label>
|
||||
<input type="text" id="api-model-text" placeholder="可手动填写，如 cursor/google/gemini-3-flash">
|
||||
</div>
|
||||
</div>
|
||||
<div class="settings-row hidden" id="api-model-select-row">
|
||||
<div class="settings-field full">
|
||||
<label>可用模型</label>
|
||||
<label>已拉取模型</label>
|
||||
<select id="api-model-select">
|
||||
<option value="">请先拉取模型列表</option>
|
||||
<option value="">请选择</option>
|
||||
</select>
|
||||
<div class="settings-hint">选择后会回填到上面的模型名输入框。原生下拉更稳，不依赖额外样式。</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="settings-btn-row hidden" id="api-connect-row"
|
||||
@@ -243,6 +245,7 @@
|
||||
<span>流式</span>
|
||||
</label>
|
||||
</div>
|
||||
<div class="settings-hint hidden" id="api-connect-status"></div>
|
||||
|
||||
<!-- Collapsible Gen Params -->
|
||||
<div class="settings-collapse">
|
||||
@@ -383,6 +386,15 @@
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="settings-section" style="padding: 0; margin-top: 16px;">
|
||||
<div class="settings-section-title">导出与导入</div>
|
||||
<div class="settings-btn-row" style="margin-top: 8px;">
|
||||
<button class="btn btn-sm" id="btn-copy-summary" style="flex:1">复制记忆包</button>
|
||||
<button class="btn btn-sm" id="btn-import-summary" style="flex:1">粘贴导入记忆包</button>
|
||||
</div>
|
||||
<div class="settings-hint" id="summary-io-status">复制会把记忆包放进剪贴板；导入会覆盖当前聊天的总结资料，并自动清空向量与总结边界。</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -581,6 +593,75 @@
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="tab-pane" id="tab-prompts">
|
||||
<div class="settings-section">
|
||||
<div class="settings-btn-row" style="margin: 0 0 12px 0; align-items: center;">
|
||||
<div class="settings-section-title" style="margin: 0;">增量总结提示词</div>
|
||||
<button class="btn btn-sm" id="btn-reset-summary-prompts" style="margin-left:auto;">恢复默认</button>
|
||||
</div>
|
||||
<div class="settings-hint" style="margin-bottom: 12px;">这里展示的是一次完整增量总结的各段提示词。像 <code>{$nextEventId}</code>、<code>{$existingEventCount}</code> 这样的占位符会在运行时自动替换，不要删除。</div>
|
||||
<div class="settings-row">
|
||||
<div class="settings-field full">
|
||||
<textarea class="editor-ta" id="summary-system-prompt" style="min-height: 300px;" placeholder="assistant"></textarea>
|
||||
</div>
|
||||
</div>
|
||||
<div class="settings-row">
|
||||
<div class="settings-field full">
|
||||
<textarea class="editor-ta" id="summary-assistant-doc-prompt" style="min-height: 220px;" placeholder="assistant"></textarea>
|
||||
</div>
|
||||
</div>
|
||||
<div class="settings-row">
|
||||
<div class="settings-field full">
|
||||
<textarea class="editor-ta" id="summary-assistant-ask-summary-prompt" style="min-height: 120px;" placeholder="user"></textarea>
|
||||
</div>
|
||||
</div>
|
||||
<div class="settings-row">
|
||||
<div class="settings-field full">
|
||||
<textarea class="editor-ta" id="summary-assistant-ask-content-prompt" style="min-height: 160px;" placeholder="assistant"></textarea>
|
||||
</div>
|
||||
</div>
|
||||
<div class="settings-row">
|
||||
<div class="settings-field full">
|
||||
<label>{插入聊天历史记录}</label>
|
||||
<textarea class="editor-ta" id="summary-meta-protocol-start-prompt" style="min-height: 120px;" placeholder="user"></textarea>
|
||||
</div>
|
||||
</div>
|
||||
<div class="settings-row">
|
||||
<div class="settings-field full">
|
||||
<textarea class="editor-ta" id="summary-user-json-format-prompt" style="min-height: 320px;" placeholder="user"></textarea>
|
||||
</div>
|
||||
</div>
|
||||
<div class="settings-row">
|
||||
<div class="settings-field full">
|
||||
<textarea class="editor-ta" id="summary-assistant-check-prompt" style="min-height: 180px;" placeholder="assistant"></textarea>
|
||||
</div>
|
||||
</div>
|
||||
<div class="settings-row">
|
||||
<div class="settings-field full">
|
||||
<textarea class="editor-ta" id="summary-user-confirm-prompt" style="min-height: 100px;" placeholder="user"></textarea>
|
||||
</div>
|
||||
</div>
|
||||
<div class="settings-row">
|
||||
<div class="settings-field full">
|
||||
<textarea class="editor-ta" id="summary-assistant-prefill-prompt" style="min-height: 80px;" placeholder="assistant"></textarea>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="settings-section">
|
||||
<div class="settings-btn-row" style="margin: 0 0 12px 0; align-items: center;">
|
||||
<div class="settings-section-title" style="margin: 0;">记忆注入提示词</div>
|
||||
<button class="btn btn-sm" id="btn-reset-memory-prompt-template" style="margin-left:auto;">恢复默认</button>
|
||||
</div>
|
||||
<div class="settings-row">
|
||||
<div class="settings-field full">
|
||||
<textarea class="editor-ta" id="memory-prompt-template" style="min-height: 220px;" placeholder="聊天注入模板"></textarea>
|
||||
<div class="settings-hint">必须保留 <code>{$剧情记忆}</code> 这个占位符，运行时会替换成实际记忆内容。</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Tab 3: Debug -->
|
||||
<div class="tab-pane" id="tab-debug">
|
||||
<div class="debug-log-header">
|
||||
@@ -859,6 +940,9 @@
|
||||
</div>
|
||||
<div class="modal-body">
|
||||
<div id="confirm-message" style="margin: 10px 0; line-height: 1.6; color: var(--fg);">内容</div>
|
||||
<div id="confirm-input-wrap" class="hidden" style="margin-top: 12px;">
|
||||
<textarea class="editor-ta" id="confirm-input" style="min-height: 220px;" placeholder="在这里粘贴记忆包"></textarea>
|
||||
</div>
|
||||
</div>
|
||||
<div class="modal-foot">
|
||||
<button class="btn" id="confirm-cancel">取消</button>
|
||||
|
||||
@@ -944,10 +944,8 @@ function initButtonsForAll() {
|
||||
|
||||
async function sendSavedConfigToFrame() {
|
||||
try {
|
||||
const savedConfig = await CommonSettingStorage.get(SUMMARY_CONFIG_KEY, null);
|
||||
if (savedConfig) {
|
||||
postToFrame({ type: "LOAD_PANEL_CONFIG", config: savedConfig });
|
||||
}
|
||||
const savedConfig = getSummaryPanelConfig();
|
||||
postToFrame({ type: "LOAD_PANEL_CONFIG", config: savedConfig });
|
||||
} catch (e) {
|
||||
xbLog.warn(MODULE_ID, "加载面板配置失败", e);
|
||||
}
|
||||
@@ -1031,6 +1029,270 @@ function buildFramePayload(store) {
|
||||
};
|
||||
}
|
||||
|
||||
async function copyTextToClipboard(text) {
|
||||
const value = String(text ?? "");
|
||||
if (!value) {
|
||||
throw new Error("没有可复制的内容");
|
||||
}
|
||||
|
||||
if (navigator.clipboard?.writeText) {
|
||||
await navigator.clipboard.writeText(value);
|
||||
return;
|
||||
}
|
||||
|
||||
const ta = document.createElement("textarea");
|
||||
ta.value = value;
|
||||
ta.setAttribute("readonly", "");
|
||||
ta.style.position = "fixed";
|
||||
ta.style.left = "-9999px";
|
||||
document.body.appendChild(ta);
|
||||
ta.select();
|
||||
ta.setSelectionRange(0, ta.value.length);
|
||||
const ok = document.execCommand?.("copy");
|
||||
ta.remove();
|
||||
if (!ok) {
|
||||
throw new Error("浏览器不支持自动复制");
|
||||
}
|
||||
}
|
||||
|
||||
function stripFloorMarker(summary) {
|
||||
return String(summary || "")
|
||||
.replace(/\s*\(#\d+(?:-\d+)?\)\s*$/, "")
|
||||
.trim();
|
||||
}
|
||||
|
||||
function normalizeInternalFact(item) {
|
||||
const fact = item && typeof item === "object" ? item : {};
|
||||
const base = {
|
||||
id: String(fact?.id || "").trim(),
|
||||
s: String(fact?.s ?? "").trim(),
|
||||
p: String(fact?.p ?? "").trim(),
|
||||
o: String(fact?.o ?? "").trim(),
|
||||
};
|
||||
|
||||
const stateValue = fact?._isState ?? fact?.isState;
|
||||
if (stateValue != null) {
|
||||
base._isState = !!stateValue;
|
||||
}
|
||||
|
||||
const trendValue = String(fact?.trend ?? "").trim();
|
||||
if (trendValue) {
|
||||
base.trend = trendValue;
|
||||
}
|
||||
|
||||
return base;
|
||||
}
|
||||
|
||||
function normalizePortableFact(item) {
|
||||
const fact = item && typeof item === "object" ? item : {};
|
||||
const base = {
|
||||
id: String(fact?.id || "").trim(),
|
||||
s: String(fact?.人物名字 ?? "").trim(),
|
||||
p: String(fact?.种类 ?? "").trim(),
|
||||
o: String(fact?.描述 ?? "").trim(),
|
||||
};
|
||||
|
||||
const stateValue = fact?._isState ?? fact?.isState ?? fact?.核心事实;
|
||||
if (stateValue != null) {
|
||||
base._isState = !!stateValue;
|
||||
}
|
||||
|
||||
const trendValue = String(fact?.trend ?? fact?.趋势 ?? "").trim();
|
||||
if (trendValue) {
|
||||
base.trend = trendValue;
|
||||
}
|
||||
|
||||
return base;
|
||||
}
|
||||
|
||||
function serializePortableFact(fact) {
|
||||
const out = {
|
||||
人物名字: String(fact?.s || "").trim(),
|
||||
种类: String(fact?.p || "").trim(),
|
||||
描述: String(fact?.o || "").trim(),
|
||||
};
|
||||
|
||||
if (fact?._isState != null) {
|
||||
out.核心事实 = !!fact._isState;
|
||||
}
|
||||
|
||||
if (fact?.trend) {
|
||||
out.趋势 = String(fact.trend).trim();
|
||||
}
|
||||
|
||||
return out;
|
||||
}
|
||||
|
||||
function cloneSummaryJsonForPortability(json) {
|
||||
const src = json && typeof json === "object" ? json : {};
|
||||
const characters = src.characters && typeof src.characters === "object" ? src.characters : {};
|
||||
return {
|
||||
keywords: Array.isArray(src.keywords)
|
||||
? src.keywords.map((item) => ({
|
||||
text: String(item?.text || "").trim(),
|
||||
weight: String(item?.weight || "").trim(),
|
||||
})).filter((item) => item.text)
|
||||
: [],
|
||||
events: Array.isArray(src.events)
|
||||
? src.events.map((item) => ({
|
||||
id: String(item?.id || "").trim(),
|
||||
title: String(item?.title || "").trim(),
|
||||
timeLabel: String(item?.timeLabel || "").trim(),
|
||||
summary: stripFloorMarker(item?.summary),
|
||||
participants: Array.isArray(item?.participants)
|
||||
? item.participants.map((name) => String(name || "").trim()).filter(Boolean)
|
||||
: [],
|
||||
type: String(item?.type || "").trim(),
|
||||
weight: String(item?.weight || "").trim(),
|
||||
causedBy: Array.isArray(item?.causedBy)
|
||||
? item.causedBy.map((id) => String(id || "").trim()).filter(Boolean)
|
||||
: [],
|
||||
})).filter((item) => item.id || item.title || item.summary)
|
||||
: [],
|
||||
characters: {
|
||||
main: Array.isArray(characters.main)
|
||||
? characters.main
|
||||
.map((item) => typeof item === "string"
|
||||
? { name: String(item).trim() }
|
||||
: { name: String(item?.name || "").trim() })
|
||||
.filter((item) => item.name)
|
||||
: (Array.isArray(characters)
|
||||
? characters
|
||||
.map((item) => typeof item === "string"
|
||||
? { name: String(item).trim() }
|
||||
: { name: String(item?.name || "").trim() })
|
||||
.filter((item) => item.name)
|
||||
: []),
|
||||
},
|
||||
arcs: Array.isArray(src.arcs)
|
||||
? src.arcs.map((item) => ({
|
||||
name: String(item?.name || "").trim(),
|
||||
trajectory: String(item?.trajectory || "").trim(),
|
||||
progress: Number.isFinite(Number(item?.progress)) ? Number(item.progress) : 0,
|
||||
moments: Array.isArray(item?.moments)
|
||||
? item.moments
|
||||
.map((moment) => typeof moment === "string"
|
||||
? { text: String(moment).trim() }
|
||||
: { text: String(moment?.text || "").trim() })
|
||||
.filter((moment) => moment.text)
|
||||
: [],
|
||||
})).filter((item) => item.name)
|
||||
: [],
|
||||
facts: Array.isArray(src.facts)
|
||||
? src.facts.map(normalizeInternalFact).filter((item) => item.s && item.p && item.o)
|
||||
: [],
|
||||
};
|
||||
}
|
||||
|
||||
function extractSummaryImportJson(raw) {
|
||||
if (!raw || typeof raw !== "object") {
|
||||
throw new Error("文件内容不是有效 JSON 对象");
|
||||
}
|
||||
|
||||
const candidate =
|
||||
(raw.type === "LittleWhiteBoxStorySummaryMemory" && raw.data && typeof raw.data === "object" ? raw.data : null) ||
|
||||
(raw.storySummary?.json && typeof raw.storySummary.json === "object" ? raw.storySummary.json : null) ||
|
||||
(raw.json && typeof raw.json === "object" ? raw.json : null) ||
|
||||
raw;
|
||||
|
||||
const hasSummaryShape =
|
||||
Array.isArray(candidate.keywords) ||
|
||||
Array.isArray(candidate.events) ||
|
||||
Array.isArray(candidate.arcs) ||
|
||||
Array.isArray(candidate.facts) ||
|
||||
(candidate.characters && typeof candidate.characters === "object");
|
||||
|
||||
if (!hasSummaryShape) {
|
||||
throw new Error("未识别到可导入的总结数据");
|
||||
}
|
||||
|
||||
const json = cloneSummaryJsonForPortability(candidate);
|
||||
json.facts = Array.isArray(candidate.facts)
|
||||
? candidate.facts.map(normalizePortableFact).filter((item) => item.s && item.p && item.o)
|
||||
: [];
|
||||
return json;
|
||||
}
|
||||
|
||||
function buildSummaryExportPackage(store) {
|
||||
const json = cloneSummaryJsonForPortability(store?.json || {});
|
||||
const data = {
|
||||
...json,
|
||||
facts: json.facts.map(serializePortableFact),
|
||||
};
|
||||
return {
|
||||
type: "LittleWhiteBoxStorySummaryMemory",
|
||||
version: 1,
|
||||
exportedAt: new Date().toISOString(),
|
||||
data,
|
||||
counts: {
|
||||
keywords: json.keywords.length,
|
||||
events: json.events.length,
|
||||
characters: json.characters.main.length,
|
||||
arcs: json.arcs.length,
|
||||
facts: json.facts.length,
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
async function importSummaryMemoryPackage(rawText) {
|
||||
if (!String(rawText || "").trim()) {
|
||||
throw new Error("记忆包内容为空");
|
||||
}
|
||||
let parsed;
|
||||
try {
|
||||
parsed = JSON.parse(String(rawText));
|
||||
} catch {
|
||||
throw new Error("JSON 解析失败");
|
||||
}
|
||||
|
||||
const importedJson = extractSummaryImportJson(parsed);
|
||||
const { chatId, chat } = getContext();
|
||||
if (!chatId) {
|
||||
throw new Error("当前没有打开聊天");
|
||||
}
|
||||
|
||||
await clearAllAtomsAndVectors(chatId);
|
||||
await clearAllChunks(chatId);
|
||||
await clearEventVectors(chatId);
|
||||
await clearStateVectors(chatId);
|
||||
await updateMeta(chatId, { lastChunkFloor: -1, fingerprint: null });
|
||||
|
||||
invalidateLexicalIndex();
|
||||
|
||||
const store = getSummaryStore();
|
||||
if (!store) {
|
||||
throw new Error("无法读取当前聊天的总结存储");
|
||||
}
|
||||
|
||||
store.json = importedJson;
|
||||
store.lastSummarizedMesId = -1;
|
||||
store.summaryHistory = [];
|
||||
store.updatedAt = Date.now();
|
||||
saveSummaryStore();
|
||||
|
||||
_lastBuiltPromptText = "";
|
||||
|
||||
refreshEntityLexiconAndWarmup();
|
||||
scheduleLexicalWarmup();
|
||||
|
||||
await clearHideState();
|
||||
const totalFloors = Array.isArray(chat) ? chat.length : 0;
|
||||
await sendFrameBaseData(store, totalFloors);
|
||||
sendFrameFullData(store, totalFloors);
|
||||
await sendAnchorStatsToFrame();
|
||||
await sendVectorStatsToFrame();
|
||||
|
||||
return {
|
||||
counts: {
|
||||
keywords: importedJson.keywords.length,
|
||||
events: importedJson.events.length,
|
||||
characters: importedJson.characters.main.length,
|
||||
arcs: importedJson.arcs.length,
|
||||
facts: importedJson.facts.length,
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
// Compatibility export for ena-planner.
|
||||
// Returns a compact plain-text snapshot of story-summary memory.
|
||||
export function getStorySummaryForEna() {
|
||||
@@ -1426,6 +1688,43 @@ async function handleFrameMessage(event) {
|
||||
})();
|
||||
break;
|
||||
|
||||
case "SUMMARY_COPY":
|
||||
(async () => {
|
||||
try {
|
||||
const store = getSummaryStore();
|
||||
const payload = buildSummaryExportPackage(store);
|
||||
await copyTextToClipboard(JSON.stringify(payload, null, 2));
|
||||
postToFrame({
|
||||
type: "SUMMARY_COPY_RESULT",
|
||||
success: true,
|
||||
events: payload.counts.events,
|
||||
facts: payload.counts.facts,
|
||||
});
|
||||
} catch (e) {
|
||||
postToFrame({ type: "SUMMARY_COPY_RESULT", success: false, error: e.message });
|
||||
}
|
||||
})();
|
||||
break;
|
||||
|
||||
case "SUMMARY_IMPORT_TEXT":
|
||||
if (guard.isAnyRunning('summary', 'vector', 'anchor')) {
|
||||
postToFrame({ type: "SUMMARY_IMPORT_RESULT", success: false, error: "请等待当前总结/向量任务结束" });
|
||||
break;
|
||||
}
|
||||
(async () => {
|
||||
try {
|
||||
const result = await importSummaryMemoryPackage(data.text || "");
|
||||
postToFrame({
|
||||
type: "SUMMARY_IMPORT_RESULT",
|
||||
success: true,
|
||||
counts: result.counts,
|
||||
});
|
||||
} catch (e) {
|
||||
postToFrame({ type: "SUMMARY_IMPORT_RESULT", success: false, error: e.message });
|
||||
}
|
||||
})();
|
||||
break;
|
||||
|
||||
case "VECTOR_IMPORT_PICK":
|
||||
// 在 parent 创建 file picker，避免 iframe 传大文件
|
||||
(async () => {
|
||||
|
||||
@@ -177,7 +177,9 @@ class StreamingGeneration {
|
||||
const provider = String(opts.api || '').toLowerCase();
|
||||
const reverseProxyConfigured = String(opts.apiurl || '').trim().length > 0;
|
||||
const pwd = String(opts.apipassword || '').trim();
|
||||
if (!reverseProxyConfigured && pwd) {
|
||||
if (pwd && provider === 'custom') {
|
||||
await writeSecret(SECRET_KEYS.CUSTOM, pwd, 'xbgen-inline');
|
||||
} else if (!reverseProxyConfigured && pwd) {
|
||||
const providerToSecretKey = {
|
||||
openai: SECRET_KEYS.OPENAI,
|
||||
gemini: SECRET_KEYS.MAKERSUITE,
|
||||
|
||||
Reference in New Issue
Block a user