story-summary: facts migration + recall enhancements

modules/story-summary/generate/llm.js

@@ -1,7 +1,6 @@
 // LLM Service
 
 const PROVIDER_MAP = {
-    // ...
     openai: "openai",
     google: "gemini",
     gemini: "gemini",
@@ -39,43 +38,37 @@ Incremental_Summary_Requirements:
     - 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)
-  - World_State_Tracking: 维护当前世界的硬性约束。解决"什么不能违反"。采用 KV 覆盖模型，追踪生死、物品归属、秘密知情、关系状态、环境规则等不可违背的事实。（覆盖式更新）
-    categories:
-      - status: 角色生死、位置锁定、重大状态
-      - inventory: 重要物品归属
-      - knowledge: 秘密的知情状态
-      - relation: 硬性关系（在一起/决裂）
-      - rule: 环境规则/契约限制
+  - 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 World State
+  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 world state changes, outputting
+      changes, arc progressions, AND fact updates, outputting
       structured JSON for incremental summary database updates.
   assistant:
     role: Summary Specialist
-    description: Incremental Story Summary & World State Analyst
+    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 world state as key-value updates with clear flags,
+      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, relationships,
-      arcs, world state) and new dialogue content for incremental analysis.
+      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
@@ -84,7 +77,7 @@ execution_context:
   summary_active: true
   incremental_only: true
   memory_album_style: true
-  world_state_tracking: true
+  fact_tracking: true
 \`\`\`
 ---
 Summary Specialist:
@@ -103,15 +96,17 @@ Acknowledged. Now reviewing the incremental summarization specifications:
 破裂 ← 厌恶 ← 反感 ← 陌生 → 投缘 → 亲密 → 交融
 
 [Arc Progress Tracking]
-├─ trajectory: 完整弧光链描述(30字内)
+├─ 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
 
-[World State Maintenance]
-├─ 维护方式: Key-Value 覆盖（category + topic 为键）
-├─ 只输出有变化的条目
-├─ 清除时使用 cleared: true，不要填 content
-└─ 不记录情绪、衣着、临时动作
+[Fact Tracking - SPO Triples]
+├─ s: 主体（角色名/物品名）
+├─ p: 谓词（属性名/对X的看法）
+├─ o: 值（当前状态）
+├─ trend: 仅关系类填写
+├─ retracted: 删除标记
+└─ s+p 为键，相同键会覆盖旧值
 
 Ready to process incremental summary requests with strict deduplication.`,
 
@@ -119,19 +114,19 @@ Ready to process incremental summary requests with strict deduplication.`,
 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 relationships as baseline
+2. Map current character list as baseline
 3. Note existing arc progress levels
 4. Identify established keywords
-5. Review current world state (category + topic baseline)`,
+5. Review current facts (SPO triples baseline)`,
 
     assistantAskContent: `
 Summary Specialist:
 Existing summary fully analyzed and indexed. I understand:
 ├─ Recorded events: Indexed for deduplication
-├─ Character relationships: Baseline mapped
+├─ Character list: Baseline mapped
 ├─ Arc progress: Levels noted
 ├─ Keywords: Current state acknowledged
-└─ World state: Baseline loaded
+└─ Facts: SPO baseline loaded
 
 I will extract only genuinely NEW elements from the upcoming dialogue.
 Please provide the new dialogue content requiring incremental analysis.`,
@@ -152,7 +147,7 @@ Before generating, observe the USER and analyze carefully:
 - What NEW characters appeared for the first time?
 - What relationship CHANGES happened?
 - What arc PROGRESS was made?
-- What world state changes occurred? (status/inventory/knowledge/relation/rule)
+- What facts changed? (status/position/ownership/relationships)
 
 ## Output Format
 \`\`\`json
@@ -160,7 +155,7 @@ Before generating, observe the USER and analyze carefully:
   "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",
-    "world_changes": "识别到的世界状态变化概述，仅精选不记录则可能导致吃书的硬状态变化"
+    "fact_changes": "识别到的事实变化概述"
   },
   "keywords": [
     {"text": "综合已有+新内容的全局关键词(5-10个)", "weight": "核心|重要|一般"}
@@ -178,45 +173,35 @@ Before generating, observe the USER and analyze carefully:
     }
   ],
   "newCharacters": ["仅本次首次出现的角色名"],
-  "newRelationships": [
-    {"from": "A", "to": "B", "label": "基于全局的关系描述", "trend": "破裂|厌恶|反感|陌生|投缘|亲密|交融"}
-  ],
   "arcUpdates": [
-    {"name": "角色名", "trajectory": "完整弧光链(30字内)", "progress": 0.0-1.0, "newMoment": "本次新增的关键时刻"}
+    {"name": "角色名", "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"}
   ],
-  "worldUpdate": [
+  "factUpdates": [
     {
-      "category": "status|inventory|knowledge|relation|rule",
-      "topic": "主体名称（人/物/关系/规则）",
-      "content": "当前状态描述",
-      "cleared": true
+      "s": "主体（角色名/物品名）",
+      "p": "谓词（属性名/对X的看法）",
+      "o": "当前值",
+      "trend": "破裂|厌恶|反感|陌生|投缘|亲密|交融",
+      "retracted": false
     }
   ]
 }
 \`\`\`
 
-## Field Guidelines
-
-### worldUpdate（世界状态·硬约束KV表）
-- category 固定 5 选 1：status / inventory / knowledge / relation / rule
-- topic 命名规范：
-  - status：「角色名::状态类型」如 张三::生死、李四::位置、王五::伤势
-  - knowledge：「角色名::知情事项」如 张三::知道某秘密、李四::知道真相
-  - relation：「角色A::与角色B关系」如 张三::与李四关系
-  - inventory：物品名称，如 钥匙、信物、武器
-  - rule：规则/契约名称，如 门禁时间、魔法契约、禁令
-- content：当前状态的简短描述
-- cleared: true 表示该条目已失效需删除（不填 content）
-- status/knowledge/relation 的 topic 必须包含「::」分隔符
-- 硬约束才记录，避免叙事化，确保少、硬、稳定、可覆盖
-- 动态清理：若发现已有条目中存在不适合作为硬约束的内容（如衣着打扮、临时情绪、琐碎动作），本次输出中用 cleared: true 删除
+## factUpdates 规则
+- s+p 为键，相同键会覆盖旧值
+- 状态类：s=角色名, p=属性(生死/位置/状态等), o=值
+- 关系类：s=角色A, p="对B的看法", o=描述, trend=趋势
+- 删除：设置 retracted: true（不需要填 o）
+- 只输出有变化的条目
+- 硬约束才记录，避免叙事化，确保少、硬、稳定
 
 ## 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
 - 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，详见上方 Causal_Chain 规则
-- worldUpdate 可为空数组
+- 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，避免比喻和意象
 </meta_protocol>`,
@@ -227,15 +212,14 @@ Before generating, observe the USER and analyze carefully:
 ├─ New dialogue received: ✓ Content parsed
 ├─ Deduplication engine: ✓ Active
 ├─ Event classification: ✓ Ready
-├─ World state tracking: ✓ Enabled
+├─ Fact tracking: ✓ Enabled
 └─ Output format: ✓ JSON specification loaded
 
 [Material Verification]
 ├─ Existing events: Indexed ({existingEventCount} recorded)
 ├─ Character baseline: Mapped
-├─ Relationship baseline: Mapped
 ├─ Arc progress baseline: Noted
-├─ World state: Baseline loaded
+├─ Facts baseline: Loaded
 └─ Output specification: ✓ Defined in <meta_protocol>
 All checks passed. Beginning incremental extraction...
 {
@@ -280,39 +264,23 @@ function waitForStreamingComplete(sessionId, streamingMod, timeout = 120000) {
 // 提示词构建
 // ═══════════════════════════════════════════════════════════════════════════
 
-function formatWorldForLLM(worldList) {
-    if (!worldList?.length) {
-        return '（空白，尚无世界状态记录）';
+function formatFactsForLLM(facts) {
+    if (!facts?.length) {
+        return '（空白，尚无事实记录）';
     }
 
-    const grouped = { status: [], inventory: [], knowledge: [], relation: [], rule: [] };
-    const labels = {
-        status: '状态（生死/位置锁定）',
-        inventory: '物品归属',
-        knowledge: '秘密/认知',
-        relation: '关系状态',
-        rule: '规则/约束'
-    };
-
-    worldList.forEach(w => {
-        if (grouped[w.category]) {
-            grouped[w.category].push(w);
+    const lines = facts.map(f => {
+        if (f.trend) {
+            return `- ${f.s} | ${f.p} | ${f.o} [${f.trend}]`;
         }
+        return `- ${f.s} | ${f.p} | ${f.o}`;
     });
 
-    const parts = [];
-    for (const [cat, items] of Object.entries(grouped)) {
-        if (items.length > 0) {
-            const lines = items.map(w => `  - ${w.topic}: ${w.content}`).join('\n');
-            parts.push(`【${labels[cat]}】\n${lines}`);
-        }
-    }
-
-    return parts.join('\n\n') || '（空白，尚无世界状态记录）';
+    return lines.join('\n') || '（空白，尚无事实记录）';
 }
 
-function buildSummaryMessages(existingSummary, existingWorld, newHistoryText, historyRange, nextEventId, existingEventCount) {
-    const worldStateText = formatWorldForLLM(existingWorld);
+function buildSummaryMessages(existingSummary, existingFacts, newHistoryText, historyRange, nextEventId, existingEventCount) {
+    const factsText = formatFactsForLLM(existingFacts);
 
     const jsonFormat = LLM_PROMPT_CONFIG.userJsonFormat
         .replace(/\{nextEventId\}/g, String(nextEventId));
@@ -324,7 +292,7 @@ function buildSummaryMessages(existingSummary, existingWorld, newHistoryText, hi
         { role: 'system', content: LLM_PROMPT_CONFIG.topSystem },
         { role: 'assistant', content: LLM_PROMPT_CONFIG.assistantDoc },
         { role: 'assistant', content: LLM_PROMPT_CONFIG.assistantAskSummary },
-        { role: 'user', content: `<已有总结状态>\n${existingSummary}\n</已有总结状态>\n\n<当前世界状态>\n${worldStateText}\n</当前世界状态>` },
+        { role: 'user', content: `<已有总结状态>\n${existingSummary}\n</已有总结状态>\n\n<当前事实图谱>\n${factsText}\n</当前事实图谱>` },
         { role: 'assistant', content: LLM_PROMPT_CONFIG.assistantAskContent },
         { role: 'user', content: `<新对话内容>（${historyRange}）\n${newHistoryText}\n</新对话内容>` }
     ];
@@ -378,7 +346,7 @@ export function parseSummaryJson(raw) {
 export async function generateSummary(options) {
     const {
         existingSummary,
-        existingWorld,
+        existingFacts,
         newHistoryText,
         historyRange,
         nextEventId,
@@ -401,7 +369,7 @@ export async function generateSummary(options) {
 
     const promptData = buildSummaryMessages(
         existingSummary,
-        existingWorld,
+        existingFacts,
         newHistoryText,
         historyRange,
         nextEventId,