409 lines
16 KiB
JavaScript
409 lines
16 KiB
JavaScript
import { extension_settings } from "../../../../../../extensions.js";
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import { EXT_ID } from "../../../core/constants.js";
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import { xbLog } from "../../../core/debug-core.js";
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import { CommonSettingStorage } from "../../../core/server-storage.js";
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const MODULE_ID = "summaryConfig";
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const SUMMARY_CONFIG_KEY = "storySummaryPanelConfig";
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const DEFAULT_FILTER_RULES = [
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{ start: "<think>", end: "</think>" },
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{ start: "<thinking>", end: "</thinking>" },
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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-{$nextEventId} 开始编号
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- 仅输出【增量】内容，已有事件绝不重复
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- /地点、通过什么方式、对谁、做了什么事、结果如何。如果原文有具体道具（如一把枪、一封信），必须在总结中提及。
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- keywords 是全局关键词，综合已有+新增
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- causedBy 仅在因果明确时填写，允许为[]，0-2个
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- factUpdates 可为空数组
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- 合法JSON，字符串值内部避免英文双引号
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- 用朴实、白描、有烟火气的笔触记录事实，避免比喻和意象
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- 严谨、注重细节，避免使用模糊的概括性语言，应用具体的动词描述动作，例:谁,在什么时间/地点,通过什么方式,对谁,做了什么事,出现了什么道具,结果如何。
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</meta_protocol>
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## Placeholder Notes
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- {$nextEventId} 会在运行时替换成实际起始事件编号，不要删除
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- {$existingEventCount}、{$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`;
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export const DEFAULT_SUMMARY_ASSISTANT_CHECK_PROMPT = `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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├─ Fact tracking: ✓ Enabled
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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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├─ Arc progress baseline: Noted
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├─ Facts baseline: Loaded
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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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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
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</Chat_History>`;
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export const DEFAULT_SUMMARY_ASSISTANT_PREFILL_PROMPT = '下面重新生成完整JSON。';
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export function getSettings() {
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const ext = (extension_settings[EXT_ID] ||= {});
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ext.storySummary ||= { enabled: true };
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return ext;
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}
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export function getSummaryPanelConfig() {
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const clampKeepVisibleCount = (value) => {
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const n = Number.parseInt(value, 10);
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if (!Number.isFinite(n)) return 6;
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return Math.max(0, Math.min(50, n));
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};
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const defaults = {
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api: { provider: "st", url: "", key: "", model: "", modelCache: [] },
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gen: { temperature: null, top_p: null, top_k: null, presence_penalty: null, frequency_penalty: null },
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trigger: {
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enabled: false,
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interval: 20,
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timing: "before_user",
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role: "system",
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useStream: true,
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maxPerRun: 100,
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wrapperHead: "",
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wrapperTail: "",
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forceInsertAtEnd: false,
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},
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ui: {
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hideSummarized: true,
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keepVisibleCount: 6,
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},
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textFilterRules: [...DEFAULT_FILTER_RULES],
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prompts: {
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summarySystemPrompt: DEFAULT_SUMMARY_SYSTEM_PROMPT,
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summaryAssistantDocPrompt: DEFAULT_SUMMARY_ASSISTANT_DOC_PROMPT,
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summaryAssistantAskSummaryPrompt: DEFAULT_SUMMARY_ASSISTANT_ASK_SUMMARY_PROMPT,
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summaryAssistantAskContentPrompt: DEFAULT_SUMMARY_ASSISTANT_ASK_CONTENT_PROMPT,
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summaryMetaProtocolStartPrompt: DEFAULT_SUMMARY_META_PROTOCOL_START_PROMPT,
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summaryUserJsonFormatPrompt: DEFAULT_SUMMARY_USER_JSON_FORMAT_PROMPT,
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summaryAssistantCheckPrompt: DEFAULT_SUMMARY_ASSISTANT_CHECK_PROMPT,
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summaryUserConfirmPrompt: DEFAULT_SUMMARY_USER_CONFIRM_PROMPT,
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summaryAssistantPrefillPrompt: DEFAULT_SUMMARY_ASSISTANT_PREFILL_PROMPT,
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memoryTemplate: DEFAULT_MEMORY_PROMPT_TEMPLATE,
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},
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vector: null,
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};
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try {
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const raw = localStorage.getItem("summary_panel_config");
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if (!raw) return defaults;
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const parsed = JSON.parse(raw);
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const textFilterRules = Array.isArray(parsed.textFilterRules)
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? parsed.textFilterRules
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: (Array.isArray(parsed.vector?.textFilterRules)
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? parsed.vector.textFilterRules
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: defaults.textFilterRules);
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const result = {
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api: { ...defaults.api, ...(parsed.api || {}) },
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gen: { ...defaults.gen, ...(parsed.gen || {}) },
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trigger: { ...defaults.trigger, ...(parsed.trigger || {}) },
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ui: { ...defaults.ui, ...(parsed.ui || {}) },
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textFilterRules,
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prompts: { ...defaults.prompts, ...(parsed.prompts || {}) },
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vector: parsed.vector || null,
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};
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if (result.trigger.timing === "manual") result.trigger.enabled = false;
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if (result.trigger.useStream === undefined) result.trigger.useStream = true;
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result.ui.hideSummarized = !!result.ui.hideSummarized;
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result.ui.keepVisibleCount = clampKeepVisibleCount(result.ui.keepVisibleCount);
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return result;
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} catch {
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return defaults;
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}
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}
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export function saveSummaryPanelConfig(config) {
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try {
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localStorage.setItem("summary_panel_config", JSON.stringify(config));
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CommonSettingStorage.set(SUMMARY_CONFIG_KEY, config);
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} catch (e) {
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xbLog.error(MODULE_ID, "保存面板配置失败", e);
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}
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}
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export function getVectorConfig() {
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try {
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const raw = localStorage.getItem("summary_panel_config");
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if (!raw) return null;
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const parsed = JSON.parse(raw);
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const cfg = parsed.vector || null;
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if (!cfg) return null;
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// Keep vector side normalized to online + siliconflow.
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cfg.engine = "online";
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cfg.online = cfg.online || {};
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cfg.online.provider = "siliconflow";
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cfg.online.model = "BAAI/bge-m3";
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return cfg;
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} catch {
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return null;
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}
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}
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export function getTextFilterRules() {
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const cfg = getSummaryPanelConfig();
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return Array.isArray(cfg?.textFilterRules)
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? cfg.textFilterRules
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: DEFAULT_FILTER_RULES;
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}
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export function saveVectorConfig(vectorCfg) {
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try {
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const raw = localStorage.getItem("summary_panel_config") || "{}";
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const parsed = JSON.parse(raw);
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parsed.vector = {
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enabled: !!vectorCfg?.enabled,
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engine: "online",
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online: {
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provider: "siliconflow",
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key: vectorCfg?.online?.key || "",
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model: "BAAI/bge-m3",
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},
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};
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localStorage.setItem("summary_panel_config", JSON.stringify(parsed));
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CommonSettingStorage.set(SUMMARY_CONFIG_KEY, parsed);
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} catch (e) {
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xbLog.error(MODULE_ID, "保存向量配置失败", e);
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}
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}
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export async function loadConfigFromServer() {
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try {
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const savedConfig = await CommonSettingStorage.get(SUMMARY_CONFIG_KEY, null);
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if (savedConfig) {
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localStorage.setItem("summary_panel_config", JSON.stringify(savedConfig));
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xbLog.info(MODULE_ID, "已从服务端加载面板配置");
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return savedConfig;
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}
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} catch (e) {
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xbLog.warn(MODULE_ID, "加载面板配置失败", e);
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}
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||
return null;
|
||
}
|