remove unused duplicate story-summary llm-service module
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@@ -1,378 +0,0 @@
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// ═══════════════════════════════════════════════════════════════════════════
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// Story Summary - LLM Service
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// ═══════════════════════════════════════════════════════════════════════════
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// ═══════════════════════════════════════════════════════════════════════════
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// 常量
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// ═══════════════════════════════════════════════════════════════════════════
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const PROVIDER_MAP = {
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openai: "openai",
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google: "gemini",
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gemini: "gemini",
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claude: "claude",
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anthropic: "claude",
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deepseek: "deepseek",
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cohere: "cohere",
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custom: "custom",
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};
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const LLM_PROMPT_CONFIG = {
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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.
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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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- 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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</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
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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, and arc progressions, outputting structured JSON for
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incremental summary database updates.
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assistant:
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role: Summary Specialist
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description: Incremental Story Summary 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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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, relationships,
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arcs) 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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\`\`\`
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---
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Summary Specialist:
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<Chat_History>`,
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assistantDoc: `
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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: 完整弧光链描述(30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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Ready to process incremental summary requests with strict deduplication.`,
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assistantAskSummary: `
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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 relationships as baseline
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3. Note existing arc progress levels
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4. Identify established keywords`,
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assistantAskContent: `
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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 relationships: Baseline mapped
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├─ Arc progress: Levels noted
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└─ Keywords: Current state acknowledged
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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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metaProtocolStart: `
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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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userJsonFormat: `
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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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## 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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},
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"keywords": [
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{"text": "综合已有+新内容的全局关键词(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"],
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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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}
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],
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"newCharacters": ["仅本次首次出现的角色名"],
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"newRelationships": [
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{"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"}
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],
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"arcUpdates": [
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{"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"}
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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
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- 仅输出【增量】内容，已有事件绝不重复
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- keywords 是全局关键词，综合已有+新增
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- 合法JSON，字符串值内部避免英文双引号
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- Output single valid JSON only
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</meta_protocol>`,
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assistantCheck: `Content review initiated...
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[Compliance Check Results]
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├─ Existing summary loaded: ✓ Fully indexed
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├─ New dialogue received: ✓ Content parsed
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├─ Deduplication engine: ✓ Active
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├─ Event classification: ✓ Ready
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└─ Output format: ✓ JSON specification loaded
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[Material Verification]
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├─ Existing events: Indexed ({existingEventCount} recorded)
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├─ Character baseline: Mapped
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├─ Relationship baseline: Mapped
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├─ Arc progress baseline: Noted
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└─ Output specification: ✓ Defined in <meta_protocol>
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All checks passed. Beginning incremental extraction...
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{
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"mindful_prelude":`,
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userConfirm: `怎么截断了！重新完整生成，只输出JSON，不要任何其他内容
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</Chat_History>`,
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assistantPrefill: `非常抱歉！现在重新完整生成JSON。`
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};
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// ═══════════════════════════════════════════════════════════════════════════
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// 工具函数
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// ═══════════════════════════════════════════════════════════════════════════
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function b64UrlEncode(str) {
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const utf8 = new TextEncoder().encode(String(str));
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let bin = '';
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utf8.forEach(b => bin += String.fromCharCode(b));
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return btoa(bin).replace(/\+/g, '-').replace(/\//g, '_').replace(/=+$/, '');
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}
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function getStreamingModule() {
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const mod = window.xiaobaixStreamingGeneration;
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return mod?.xbgenrawCommand ? mod : null;
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}
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function waitForStreamingComplete(sessionId, streamingMod, timeout = 120000) {
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return new Promise((resolve, reject) => {
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const start = Date.now();
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const poll = () => {
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const { isStreaming, text } = streamingMod.getStatus(sessionId);
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if (!isStreaming) return resolve(text || '');
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if (Date.now() - start > timeout) return reject(new Error('生成超时'));
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setTimeout(poll, 300);
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};
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poll();
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});
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}
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// ═══════════════════════════════════════════════════════════════════════════
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// 提示词构建
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// ═══════════════════════════════════════════════════════════════════════════
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function buildSummaryMessages(existingSummary, newHistoryText, historyRange, nextEventId, existingEventCount) {
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// 替换动态内容
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const jsonFormat = LLM_PROMPT_CONFIG.userJsonFormat
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.replace(/\{nextEventId\}/g, String(nextEventId));
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const checkContent = LLM_PROMPT_CONFIG.assistantCheck
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.replace(/\{existingEventCount\}/g, String(existingEventCount));
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// 顶部消息：系统设定 + 多轮对话引导
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const topMessages = [
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{ role: 'system', content: LLM_PROMPT_CONFIG.topSystem },
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{ role: 'assistant', content: LLM_PROMPT_CONFIG.assistantDoc },
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{ role: 'assistant', content: LLM_PROMPT_CONFIG.assistantAskSummary },
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{ role: 'user', content: `<已有总结状态>\n${existingSummary}\n</已有总结状态>` },
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{ role: 'assistant', content: LLM_PROMPT_CONFIG.assistantAskContent },
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{ role: 'user', content: `<新对话内容>（${historyRange}）\n${newHistoryText}\n</新对话内容>` }
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];
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// 底部消息：元协议 + 格式要求 + 合规检查 + 催促
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const bottomMessages = [
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{ role: 'user', content: LLM_PROMPT_CONFIG.metaProtocolStart + '\n' + jsonFormat },
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{ role: 'assistant', content: checkContent },
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{ role: 'user', content: LLM_PROMPT_CONFIG.userConfirm }
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];
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return {
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top64: b64UrlEncode(JSON.stringify(topMessages)),
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bottom64: b64UrlEncode(JSON.stringify(bottomMessages)),
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assistantPrefill: LLM_PROMPT_CONFIG.assistantPrefill
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};
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}
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// ═══════════════════════════════════════════════════════════════════════════
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// JSON 解析
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// ═══════════════════════════════════════════════════════════════════════════
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export function parseSummaryJson(raw) {
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if (!raw) return null;
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let cleaned = String(raw).trim()
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.replace(/^```(?:json)?\s*/i, "")
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.replace(/\s*```$/i, "")
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.trim();
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// 直接解析
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try {
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return JSON.parse(cleaned);
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} catch {}
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// 提取 JSON 对象
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const start = cleaned.indexOf('{');
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const end = cleaned.lastIndexOf('}');
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if (start !== -1 && end > start) {
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let jsonStr = cleaned.slice(start, end + 1)
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.replace(/,(\s*[}\]])/g, '$1'); // 移除尾部逗号
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try {
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return JSON.parse(jsonStr);
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} catch {}
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}
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return null;
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}
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// ═══════════════════════════════════════════════════════════════════════════
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// 主生成函数
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// ═══════════════════════════════════════════════════════════════════════════
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export async function generateSummary(options) {
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const {
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existingSummary,
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newHistoryText,
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historyRange,
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nextEventId,
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existingEventCount = 0,
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llmApi = {},
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genParams = {},
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useStream = true,
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timeout = 120000,
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sessionId = 'xb_summary'
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} = options;
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if (!newHistoryText?.trim()) {
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throw new Error('新对话内容为空');
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}
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const streamingMod = getStreamingModule();
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if (!streamingMod) {
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throw new Error('生成模块未加载');
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}
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const promptData = buildSummaryMessages(
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existingSummary,
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newHistoryText,
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historyRange,
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nextEventId,
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existingEventCount
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);
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const args = {
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as: 'user',
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nonstream: useStream ? 'false' : 'true',
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top64: promptData.top64,
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bottom64: promptData.bottom64,
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bottomassistant: promptData.assistantPrefill,
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id: sessionId,
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};
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// API 配置（非酒馆主 API）
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if (llmApi.provider && llmApi.provider !== 'st') {
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const mappedApi = PROVIDER_MAP[String(llmApi.provider).toLowerCase()];
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if (mappedApi) {
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args.api = mappedApi;
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if (llmApi.url) args.apiurl = llmApi.url;
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if (llmApi.key) args.apipassword = llmApi.key;
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if (llmApi.model) args.model = llmApi.model;
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|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// 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;
|
|
||||||
}
|
|
||||||
Reference in New Issue
Block a user