450 lines
18 KiB
JavaScript
450 lines
18 KiB
JavaScript
// LLM Service
|
||
|
||
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
|
||
- 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)
|
||
- World_State_Tracking: 维护当前世界的硬性约束。解决"什么不能违反"。采用 KV 覆盖模型，追踪生死、物品归属、秘密知情、关系状态、环境规则等不可违背的事实。（覆盖式更新）
|
||
categories:
|
||
- status: 角色生死、位置锁定、重大状态
|
||
- inventory: 重要物品归属
|
||
- knowledge: 秘密的知情状态
|
||
- relation: 硬性关系（在一起/决裂）
|
||
- rule: 环境规则/契约限制
|
||
</task_settings>
|
||
---
|
||
Story Analyst:
|
||
[Responsibility Definition]
|
||
\`\`\`yaml
|
||
analysis_task:
|
||
title: Incremental Story Summarization with World State
|
||
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 world state changes, outputting
|
||
structured JSON for incremental summary database updates.
|
||
assistant:
|
||
role: Summary Specialist
|
||
description: Incremental Story Summary & World State 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 world state as key-value updates with clear flags,
|
||
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, world state) 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
|
||
world_state_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: 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
|
||
|
||
[World State Maintenance]
|
||
├─ 维护方式: Key-Value 覆盖（category + topic 为键）
|
||
├─ 只输出有变化的条目
|
||
├─ 清除时使用 cleared: true，不要填 content
|
||
└─ 不记录情绪、衣着、临时动作
|
||
|
||
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
|
||
5. Review current world state (category + topic baseline)`,
|
||
|
||
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
|
||
└─ World state: 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 world state changes occurred? (status/inventory/knowledge/relation/rule)
|
||
|
||
## 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",
|
||
"world_changes": "识别到的世界状态变化概述，仅精选不记录则可能导致吃书的硬状态变化"
|
||
},
|
||
"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",
|
||
"causedBy": ["evt-12", "evt-14"]
|
||
}
|
||
],
|
||
"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"}
|
||
],
|
||
"worldUpdate": [
|
||
{
|
||
"category": "status|inventory|knowledge|relation|rule",
|
||
"topic": "主体名称（人/物/关系/规则）",
|
||
"content": "当前状态描述",
|
||
"cleared": true
|
||
}
|
||
]
|
||
}
|
||
\`\`\`
|
||
|
||
## Field Guidelines
|
||
|
||
### worldUpdate（世界状态·硬约束KV表）
|
||
- category 固定 5 选 1：status / inventory / knowledge / relation / rule
|
||
- topic 命名规范：
|
||
- status：「角色名::状态类型」如 张三::生死、李四::位置、王五::伤势
|
||
- knowledge：「角色名::知情事项」如 张三::知道某秘密、李四::知道真相
|
||
- relation：「角色A::与角色B关系」如 张三::与李四关系
|
||
- inventory：物品名称，如 钥匙、信物、武器
|
||
- rule：规则/契约名称，如 门禁时间、魔法契约、禁令
|
||
- content：当前状态的简短描述
|
||
- cleared: true 表示该条目已失效需删除（不填 content）
|
||
- status/knowledge/relation 的 topic 必须包含「::」分隔符
|
||
- 硬约束才记录，避免叙事化，确保少、硬、稳定、可覆盖
|
||
- 动态清理：若发现已有条目中存在不适合作为硬约束的内容（如衣着打扮、临时情绪、琐碎动作），本次输出中用 cleared: true 删除
|
||
|
||
## 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
|
||
- causedBy 仅在因果明确时填写，允许为[]，0-2个，详见上方 Causal_Chain 规则
|
||
- worldUpdate 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，避免比喻和意象
|
||
</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
|
||
├─ World state tracking: ✓ Enabled
|
||
└─ Output format: ✓ JSON specification loaded
|
||
|
||
[Material Verification]
|
||
├─ Existing events: Indexed ({existingEventCount} recorded)
|
||
├─ Character baseline: Mapped
|
||
├─ Relationship baseline: Mapped
|
||
├─ Arc progress baseline: Noted
|
||
├─ World state: 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
|
||
</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 formatWorldForLLM(worldList) {
|
||
if (!worldList?.length) {
|
||
return '（空白，尚无世界状态记录）';
|
||
}
|
||
|
||
const grouped = { status: [], inventory: [], knowledge: [], relation: [], rule: [] };
|
||
const labels = {
|
||
status: '状态（生死/位置锁定）',
|
||
inventory: '物品归属',
|
||
knowledge: '秘密/认知',
|
||
relation: '关系状态',
|
||
rule: '规则/约束'
|
||
};
|
||
|
||
worldList.forEach(w => {
|
||
if (grouped[w.category]) {
|
||
grouped[w.category].push(w);
|
||
}
|
||
});
|
||
|
||
const parts = [];
|
||
for (const [cat, items] of Object.entries(grouped)) {
|
||
if (items.length > 0) {
|
||
const lines = items.map(w => ` - ${w.topic}: ${w.content}`).join('\n');
|
||
parts.push(`【${labels[cat]}】\n${lines}`);
|
||
}
|
||
}
|
||
|
||
return parts.join('\n\n') || '（空白，尚无世界状态记录）';
|
||
}
|
||
|
||
function buildSummaryMessages(existingSummary, existingWorld, newHistoryText, historyRange, nextEventId, existingEventCount) {
|
||
const worldStateText = formatWorldForLLM(existingWorld);
|
||
|
||
const jsonFormat = LLM_PROMPT_CONFIG.userJsonFormat
|
||
.replace(/\{nextEventId\}/g, String(nextEventId));
|
||
|
||
const checkContent = LLM_PROMPT_CONFIG.assistantCheck
|
||
.replace(/\{existingEventCount\}/g, String(existingEventCount));
|
||
|
||
const topMessages = [
|
||
{ role: 'system', content: LLM_PROMPT_CONFIG.topSystem },
|
||
{ role: 'assistant', content: LLM_PROMPT_CONFIG.assistantDoc },
|
||
{ role: 'assistant', content: LLM_PROMPT_CONFIG.assistantAskSummary },
|
||
{ role: 'user', content: `<已有总结状态>\n${existingSummary}\n</已有总结状态>\n\n<当前世界状态>\n${worldStateText}\n</当前世界状态>` },
|
||
{ role: 'assistant', content: LLM_PROMPT_CONFIG.assistantAskContent },
|
||
{ role: 'user', content: `<新对话内容>（${historyRange}）\n${newHistoryText}\n</新对话内容>` }
|
||
];
|
||
|
||
const bottomMessages = [
|
||
{ role: 'user', content: LLM_PROMPT_CONFIG.metaProtocolStart + '\n' + jsonFormat },
|
||
{ role: 'assistant', content: checkContent },
|
||
{ role: 'user', content: LLM_PROMPT_CONFIG.userConfirm }
|
||
];
|
||
|
||
return {
|
||
top64: b64UrlEncode(JSON.stringify(topMessages)),
|
||
bottom64: b64UrlEncode(JSON.stringify(bottomMessages)),
|
||
assistantPrefill: LLM_PROMPT_CONFIG.assistantPrefill
|
||
};
|
||
}
|
||
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
// JSON 解析
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
export function parseSummaryJson(raw) {
|
||
if (!raw) return null;
|
||
|
||
let cleaned = String(raw).trim()
|
||
.replace(/^```(?:json)?\s*/i, "")
|
||
.replace(/\s*```$/i, "")
|
||
.trim();
|
||
|
||
try {
|
||
return JSON.parse(cleaned);
|
||
} catch { }
|
||
|
||
const start = cleaned.indexOf('{');
|
||
const end = cleaned.lastIndexOf('}');
|
||
if (start !== -1 && end > start) {
|
||
let jsonStr = cleaned.slice(start, end + 1)
|
||
.replace(/,(\s*[}\]])/g, '$1');
|
||
try {
|
||
return JSON.parse(jsonStr);
|
||
} catch { }
|
||
}
|
||
|
||
return null;
|
||
}
|
||
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
// 主生成函数
|
||
// ═══════════════════════════════════════════════════════════════════════════
|
||
|
||
export async function generateSummary(options) {
|
||
const {
|
||
existingSummary,
|
||
existingWorld,
|
||
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,
|
||
existingWorld,
|
||
newHistoryText,
|
||
historyRange,
|
||
nextEventId,
|
||
existingEventCount
|
||
);
|
||
|
||
const args = {
|
||
as: 'user',
|
||
nonstream: useStream ? 'false' : 'true',
|
||
top64: promptData.top64,
|
||
bottom64: promptData.bottom64,
|
||
bottomassistant: promptData.assistantPrefill,
|
||
id: sessionId,
|
||
};
|
||
|
||
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;
|
||
}
|
||
}
|
||
|
||
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;
|
||
|
||
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;
|
||
}
|