LittleWhiteBox/modules/story-summary/data/config.js

import { extension_settings } from "../../../../../../extensions.js";
import { EXT_ID } from "../../../core/constants.js";
import { xbLog } from "../../../core/debug-core.js";
import { CommonSettingStorage } from "../../../core/server-storage.js";

const MODULE_ID = "summaryConfig";
const SUMMARY_CONFIG_KEY = "storySummaryPanelConfig";

const DEFAULT_FILTER_RULES = [
    { start: "<think>", end: "</think>" },
    { start: "<thinking>", end: "</thinking>" },
    { start: "```", end: "```" },
];

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.
[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>`;

export const DEFAULT_MEMORY_PROMPT_TEMPLATE = `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：
{$剧情记忆}
这些记忆是真实的，请自然地记住它们。`;

export const DEFAULT_SUMMARY_ASSISTANT_DOC_PROMPT = `
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.`;

export const DEFAULT_SUMMARY_ASSISTANT_ASK_SUMMARY_PROMPT = `
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)`;

export const DEFAULT_SUMMARY_ASSISTANT_ASK_CONTENT_PROMPT = `
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.`;

export const DEFAULT_SUMMARY_META_PROTOCOL_START_PROMPT = `
Summary Specialist:
ACKNOWLEDGED. Beginning structured JSON generation:
<meta_protocol>`;

export const DEFAULT_SUMMARY_USER_JSON_FORMAT_PROMPT = `
## 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>

## Placeholder Notes
- {$nextEventId} 会在运行时替换成实际起始事件编号，不要删除
- {$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`;

export const DEFAULT_SUMMARY_ASSISTANT_CHECK_PROMPT = `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":`;

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
</Chat_History>`;

export const DEFAULT_SUMMARY_ASSISTANT_PREFILL_PROMPT = '下面重新生成完整JSON。';
const DEFAULT_VECTOR_PROVIDER = "siliconflow";
const DEFAULT_L0_URL = "https://api.siliconflow.cn/v1";
const DEFAULT_OPENROUTER_URL = "https://openrouter.ai/api/v1";
const DEFAULT_L0_MODEL = "Qwen/Qwen3-8B";
const DEFAULT_EMBEDDING_MODEL = "BAAI/bge-m3";
const DEFAULT_RERANK_MODEL = "BAAI/bge-reranker-v2-m3";

export function getSettings() {
    const ext = (extension_settings[EXT_ID] ||= {});
    ext.storySummary ||= { enabled: true };
    return ext;
}

function normalizeOpenAiCompatApiConfig(src, defaults = {}) {
    const provider = String(src?.provider || defaults.provider || DEFAULT_VECTOR_PROVIDER).toLowerCase();
    const defaultUrl = provider === "openrouter"
        ? DEFAULT_OPENROUTER_URL
        : String(defaults.url || DEFAULT_L0_URL);
    return {
        provider,
        url: String(src?.url || defaultUrl || "").trim(),
        key: String(src?.key || defaults.key || "").trim(),
        model: String(src?.model || defaults.model || "").trim(),
        modelCache: Array.isArray(src?.modelCache) ? src.modelCache.filter(Boolean) : [],
    };
}

function normalizeVectorConfig(rawVector = null) {
    const legacyOnline = rawVector?.online || {};
    const sharedProvider = String(legacyOnline.provider || DEFAULT_VECTOR_PROVIDER).toLowerCase();
    const sharedUrl = String(legacyOnline.url || (sharedProvider === "openrouter" ? DEFAULT_OPENROUTER_URL : DEFAULT_L0_URL)).trim();
    const sharedKey = String(legacyOnline.key || "").trim();

    return {
        enabled: !!rawVector?.enabled,
        engine: "online",
        l0Concurrency: Math.max(1, Math.min(50, Number(rawVector?.l0Concurrency) || 10)),
        l0Api: normalizeOpenAiCompatApiConfig(rawVector?.l0Api, {
            provider: sharedProvider,
            url: sharedUrl,
            key: sharedKey,
            model: DEFAULT_L0_MODEL,
        }),
        embeddingApi: normalizeOpenAiCompatApiConfig(rawVector?.embeddingApi, {
            provider: DEFAULT_VECTOR_PROVIDER,
            url: DEFAULT_L0_URL,
            key: sharedKey,
            model: DEFAULT_EMBEDDING_MODEL,
        }),
        rerankApi: normalizeOpenAiCompatApiConfig(rawVector?.rerankApi, {
            provider: DEFAULT_VECTOR_PROVIDER,
            url: DEFAULT_L0_URL,
            key: sharedKey,
            model: DEFAULT_RERANK_MODEL,
        }),
    };
}

export function getSummaryPanelConfig() {
    const clampKeepVisibleCount = (value) => {
        const n = Number.parseInt(value, 10);
        if (!Number.isFinite(n)) return 6;
        return Math.max(0, Math.min(50, n));
    };

    const defaults = {
        api: { provider: "st", url: "", key: "", model: "", modelCache: [] },
        gen: { temperature: null, top_p: null, top_k: null, presence_penalty: null, frequency_penalty: null },
        trigger: {
            enabled: false,
            interval: 20,
            timing: "before_user",
            role: "system",
            useStream: true,
            maxPerRun: 100,
            wrapperHead: "",
            wrapperTail: "",
            forceInsertAtEnd: false,
        },
        ui: {
            hideSummarized: true,
            keepVisibleCount: 6,
        },
        textFilterRules: [...DEFAULT_FILTER_RULES],
        prompts: {
            summarySystemPrompt: DEFAULT_SUMMARY_SYSTEM_PROMPT,
            summaryAssistantDocPrompt: DEFAULT_SUMMARY_ASSISTANT_DOC_PROMPT,
            summaryAssistantAskSummaryPrompt: DEFAULT_SUMMARY_ASSISTANT_ASK_SUMMARY_PROMPT,
            summaryAssistantAskContentPrompt: DEFAULT_SUMMARY_ASSISTANT_ASK_CONTENT_PROMPT,
            summaryMetaProtocolStartPrompt: DEFAULT_SUMMARY_META_PROTOCOL_START_PROMPT,
            summaryUserJsonFormatPrompt: DEFAULT_SUMMARY_USER_JSON_FORMAT_PROMPT,
            summaryAssistantCheckPrompt: DEFAULT_SUMMARY_ASSISTANT_CHECK_PROMPT,
            summaryUserConfirmPrompt: DEFAULT_SUMMARY_USER_CONFIRM_PROMPT,
            summaryAssistantPrefillPrompt: DEFAULT_SUMMARY_ASSISTANT_PREFILL_PROMPT,
            memoryTemplate: DEFAULT_MEMORY_PROMPT_TEMPLATE,
        },
        vector: normalizeVectorConfig(),
    };

    try {
        const raw = localStorage.getItem("summary_panel_config");
        if (!raw) return defaults;
        const parsed = JSON.parse(raw);

        const textFilterRules = Array.isArray(parsed.textFilterRules)
            ? parsed.textFilterRules
            : (Array.isArray(parsed.vector?.textFilterRules)
                ? parsed.vector.textFilterRules
                : defaults.textFilterRules);

        const result = {
            api: { ...defaults.api, ...(parsed.api || {}) },
            gen: { ...defaults.gen, ...(parsed.gen || {}) },
            trigger: { ...defaults.trigger, ...(parsed.trigger || {}) },
            ui: { ...defaults.ui, ...(parsed.ui || {}) },
            textFilterRules,
            prompts: { ...defaults.prompts, ...(parsed.prompts || {}) },
            vector: normalizeVectorConfig(parsed.vector || null),
        };

        if (result.trigger.timing === "manual") result.trigger.enabled = false;
        if (result.trigger.useStream === undefined) result.trigger.useStream = true;
        result.ui.hideSummarized = !!result.ui.hideSummarized;
        result.ui.keepVisibleCount = clampKeepVisibleCount(result.ui.keepVisibleCount);

        return result;
    } catch {
        return defaults;
    }
}

export function saveSummaryPanelConfig(config) {
    try {
        localStorage.setItem("summary_panel_config", JSON.stringify(config));
        CommonSettingStorage.set(SUMMARY_CONFIG_KEY, config);
    } catch (e) {
        xbLog.error(MODULE_ID, "保存面板配置失败", e);
    }
}

export function getVectorConfig() {
    try {
        const raw = localStorage.getItem("summary_panel_config");
        if (!raw) return null;

        const parsed = JSON.parse(raw);
        return parsed.vector ? normalizeVectorConfig(parsed.vector) : normalizeVectorConfig();
    } catch {
        return null;
    }
}

export function getTextFilterRules() {
    const cfg = getSummaryPanelConfig();
    return Array.isArray(cfg?.textFilterRules)
        ? cfg.textFilterRules
        : DEFAULT_FILTER_RULES;
}

export function saveVectorConfig(vectorCfg) {
    try {
        const raw = localStorage.getItem("summary_panel_config") || "{}";
        const parsed = JSON.parse(raw);

        parsed.vector = normalizeVectorConfig(vectorCfg || null);

        localStorage.setItem("summary_panel_config", JSON.stringify(parsed));
        CommonSettingStorage.set(SUMMARY_CONFIG_KEY, parsed);
    } catch (e) {
        xbLog.error(MODULE_ID, "保存向量配置失败", e);
    }
}

export async function loadConfigFromServer() {
    try {
        const savedConfig = await CommonSettingStorage.get(SUMMARY_CONFIG_KEY, null);
        if (savedConfig) {
            localStorage.setItem("summary_panel_config", JSON.stringify(savedConfig));
            xbLog.info(MODULE_ID, "已从服务端加载面板配置");
            return savedConfig;
        }
    } catch (e) {
        xbLog.warn(MODULE_ID, "加载面板配置失败", e);
    }
    return null;
}