LittleWhiteBox/modules/story-summary/llm-service.js

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// Story Summary - LLM Service
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// ═══════════════════════════════════════════════════════════════════════════
// 常量
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const PROVIDER_MAP = {
    openai: "openai",
    google: "gemini",
    gemini: "gemini",
    claude: "claude",
    anthropic: "claude",
    deepseek: "deepseek",
    cohere: "cohere",
    custom: "custom",
};

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
  - 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)
</task_settings>
---
Story Analyst:
[Responsibility Definition]
\`\`\`yaml
analysis_task:
  title: Incremental Story Summarization
  Story Analyst:
    role: Antigravity
    task: >-
      To analyze provided dialogue content against existing summary state,
      extract only NEW plot elements, character developments, relationship
      changes, and arc progressions, outputting structured JSON for
      incremental summary database updates.
  assistant:
    role: Summary Specialist
    description: Incremental Story Summary 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,
      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) 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
\`\`\`
---
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: 完整弧光链描述(30字内)
├─ progress: 0.0 to 1.0
└─ newMoment: 仅记录本次新增的关键时刻

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 relationships as baseline
3. Note existing arc progress levels
4. Identify established keywords`,

    assistantAskContent: `
Summary Specialist:
Existing summary fully analyzed and indexed. I understand:
├─ Recorded events: Indexed for deduplication
├─ Character relationships: Baseline mapped
├─ Arc progress: Levels noted
└─ Keywords: Current state acknowledged

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?

## 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",
  },
  "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"}
  ],
  "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"],
      "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"
    }
  ],
  "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"}
  ]
}
\`\`\`

## 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
- 合法JSON，字符串值内部避免英文双引号
- Output single valid JSON only
</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
└─ Output format: ✓ JSON specification loaded

[Material Verification]
├─ Existing events: Indexed ({existingEventCount} recorded)
├─ Character baseline: Mapped
├─ Relationship baseline: Mapped
├─ Arc progress baseline: Noted
└─ 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
</Chat_History>`,

    assistantPrefill: `非常抱歉！现在重新完整生成JSON。`
};

// ═══════════════════════════════════════════════════════════════════════════
// 工具函数
// ═══════════════════════════════════════════════════════════════════════════

function b64UrlEncode(str) {
    const utf8 = new TextEncoder().encode(String(str));
    let bin = '';
    utf8.forEach(b => bin += String.fromCharCode(b));
    return btoa(bin).replace(/\+/g, '-').replace(/\//g, '_').replace(/=+$/, '');
}

function getStreamingModule() {
    const mod = window.xiaobaixStreamingGeneration;
    return mod?.xbgenrawCommand ? mod : null;
}

function waitForStreamingComplete(sessionId, streamingMod, timeout = 120000) {
    return new Promise((resolve, reject) => {
        const start = Date.now();
        const poll = () => {
            const { isStreaming, text } = streamingMod.getStatus(sessionId);
            if (!isStreaming) return resolve(text || '');
            if (Date.now() - start > timeout) return reject(new Error('生成超时'));
            setTimeout(poll, 300);
        };
        poll();
    });
}

// ═══════════════════════════════════════════════════════════════════════════
// 提示词构建
// ═══════════════════════════════════════════════════════════════════════════

function buildSummaryMessages(existingSummary, newHistoryText, historyRange, nextEventId, existingEventCount) {
    // 替换动态内容
    const jsonFormat = LLM_PROMPT_CONFIG.userJsonFormat
        .replace(/\{nextEventId\}/g, String(nextEventId));
    
    const checkContent = LLM_PROMPT_CONFIG.assistantCheck
        .replace(/\{existingEventCount\}/g, String(existingEventCount));

    // 顶部消息：系统设定 + 多轮对话引导
    const topMessages = [
        { 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${existingSummary}\n</已有总结状态>` },
        { role: 'assistant', content: LLM_PROMPT_CONFIG.assistantAskContent },
        { role: 'user', content: `<新对话内容>（${historyRange}）\n${newHistoryText}\n</新对话内容>` }
    ];

    // 底部消息：元协议 + 格式要求 + 合规检查 + 催促
    const bottomMessages = [
        { role: 'user', content: LLM_PROMPT_CONFIG.metaProtocolStart + '\n' + jsonFormat },
        { role: 'assistant', content: checkContent },
        { role: 'user', content: LLM_PROMPT_CONFIG.userConfirm }
    ];

    return {
        top64: b64UrlEncode(JSON.stringify(topMessages)),
        bottom64: b64UrlEncode(JSON.stringify(bottomMessages)),
        assistantPrefill: LLM_PROMPT_CONFIG.assistantPrefill
    };
}

// ═══════════════════════════════════════════════════════════════════════════
// JSON 解析
// ═══════════════════════════════════════════════════════════════════════════

export function parseSummaryJson(raw) {
    if (!raw) return null;
    
    let cleaned = String(raw).trim()
        .replace(/^```(?:json)?\s*/i, "")
        .replace(/\s*```$/i, "")
        .trim();

    // 直接解析
    try { 
        return JSON.parse(cleaned); 
    } catch {}

    // 提取 JSON 对象
    const start = cleaned.indexOf('{');
    const end = cleaned.lastIndexOf('}');
    if (start !== -1 && end > start) {
        let jsonStr = cleaned.slice(start, end + 1)
            .replace(/,(\s*[}\]])/g, '$1'); // 移除尾部逗号
        try { 
            return JSON.parse(jsonStr); 
        } catch {}
    }

    return null;
}

// ═══════════════════════════════════════════════════════════════════════════
// 主生成函数
// ═══════════════════════════════════════════════════════════════════════════

export async function generateSummary(options) {
    const {
        existingSummary,
        newHistoryText,
        historyRange,
        nextEventId,
        existingEventCount = 0,
        llmApi = {},
        genParams = {},
        useStream = true,
        timeout = 120000,
        sessionId = 'xb_summary'
    } = options;

    if (!newHistoryText?.trim()) {
        throw new Error('新对话内容为空');
    }

    const streamingMod = getStreamingModule();
    if (!streamingMod) {
        throw new Error('生成模块未加载');
    }

    const promptData = buildSummaryMessages(
        existingSummary, 
        newHistoryText, 
        historyRange, 
        nextEventId,
        existingEventCount
    );

    const args = {
        as: 'user',
        nonstream: useStream ? 'false' : 'true',
        top64: promptData.top64,
        bottom64: promptData.bottom64,
        bottomassistant: promptData.assistantPrefill,
        id: sessionId,
    };

    // API 配置（非酒馆主 API）
    if (llmApi.provider && llmApi.provider !== 'st') {
        const mappedApi = PROVIDER_MAP[String(llmApi.provider).toLowerCase()];
        if (mappedApi) {
            args.api = mappedApi;
            if (llmApi.url) args.apiurl = llmApi.url;
            if (llmApi.key) args.apipassword = llmApi.key;
            if (llmApi.model) args.model = llmApi.model;
        }
    }

    // 生成参数
    if (genParams.temperature != null) args.temperature = genParams.temperature;
    if (genParams.top_p != null) args.top_p = genParams.top_p;
    if (genParams.top_k != null) args.top_k = genParams.top_k;
    if (genParams.presence_penalty != null) args.presence_penalty = genParams.presence_penalty;
    if (genParams.frequency_penalty != null) args.frequency_penalty = genParams.frequency_penalty;

    // 调用生成
    let rawOutput;
    if (useStream) {
        const sid = await streamingMod.xbgenrawCommand(args, '');
        rawOutput = await waitForStreamingComplete(sid, streamingMod, timeout);
    } else {
        rawOutput = await streamingMod.xbgenrawCommand(args, '');
    }

    console.group('%c[Story-Summary] LLM输出', 'color: #7c3aed; font-weight: bold');
    console.log(rawOutput);
    console.groupEnd();

    return rawOutput;
}