上傳檔案到「modules/story-summary/generate」

2026-03-31 14:25:43 +00:00
parent 689ebb4ea5
commit 4bdfc14469
2 changed files with 59 additions and 265 deletions
--- a/modules/story-summary/generate/llm.js
+++ b/modules/story-summary/generate/llm.js
@@ -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
    };
 }

--- a/modules/story-summary/generate/prompt.js
+++ b/modules/story-summary/generate/prompt.js
@@ -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 = [];