// ============================================================================
// atom-extraction.js - L0 场景锚点提取(v2 - 场景摘要 + 图结构)
//
// 设计依据:
// - BGE-M3 (BAAI, 2024): 自然语言段落检索精度最高 → semantic = 纯自然语言
// - Interpersonal Circumplex (Kiesler, 1983): 权力轴+情感轴 → dynamics 枚举
// - Labov Narrative Structure (1972): 叙事功能轴 → dynamics 枚举补充
// - TransE (Bordes, 2013): s/t/r 三元组方向性 → edges 格式
//
// 每楼层 1-2 个场景锚点(非碎片原子),60-100 字场景摘要
// ============================================================================
import { callLLM, parseJson } from './llm-service.js';
import { xbLog } from '../../../../core/debug-core.js';
import { filterText } from '../utils/text-filter.js';
const MODULE_ID = 'atom-extraction';
const CONCURRENCY = 10;
const RETRY_COUNT = 2;
const RETRY_DELAY = 500;
const DEFAULT_TIMEOUT = 20000;
const STAGGER_DELAY = 80;
let batchCancelled = false;
export function cancelBatchExtraction() {
batchCancelled = true;
}
export function isBatchCancelled() {
return batchCancelled;
}
// ============================================================================
// dynamics 封闭枚举(8 个标签,两轴四象限 + 叙事轴)
// ============================================================================
const VALID_DYNAMICS = new Set([
// 权力轴 (Interpersonal Circumplex: Dominance-Submission)
'支配', // 控制、命令、审视、威慑、主导
'让渡', // 顺从、服从、屈服、被动、配合
// 情感轴 (Interpersonal Circumplex: Hostility-Friendliness)
'亲密', // 温柔、关怀、依赖、信任、连接
'敌意', // 对抗、拒绝、攻击、嘲讽、排斥
// 叙事轴 (Labov Narrative Structure)
'揭示', // 真相、发现、告白、暴露、秘密
'决意', // 选择、承诺、放弃、宣言、转折
'张力', // 悬念、对峙、暗涌、不安、等待
'丧失', // 分离、死亡、破碎、遗憾、崩塌
]);
// ============================================================================
// L0 提取 Prompt
// ============================================================================
const SYSTEM_PROMPT = `你是场景摘要器。从一轮对话中提取1-2个场景锚点,用于语义检索和关系追踪。
输入格式:
...
...
只输出严格JSON:
{"anchors":[
{
"scene": "60-100字完整场景描述",
"who": ["角色名1","角色名2"],
"edges": [{"s":"施事方","t":"受事方","r":"互动行为"}],
"dynamics": ["标签"],
"where": "地点"
}
]}
## scene 写法
- 纯自然语言,像旁白或日记,不要任何标签/标记/枚举值
- 必须包含:角色名、动作、情感氛围、关键细节
- 读者只看 scene 就能复原这一幕
- 60-100字,信息密集但流畅
## who
- 参与互动的角色正式名称,不用代词或别称
- 只从正文内容中识别角色名,不要把标签名(如 user、assistant)当作角色
## edges(关系三元组)
- s=施事方 t=受事方 r=互动行为(10-15字)
- 每个锚点 1-3 条
## dynamics(封闭枚举,选0-2个)
权力轴:支配(控制/命令/审视) | 让渡(顺从/服从/屈服)
情感轴:亲密(温柔/信任/连接) | 敌意(对抗/拒绝/攻击)
叙事轴:揭示(真相/秘密) | 决意(选择/承诺) | 张力(对峙/不安) | 丧失(分离/破碎)
纯日常无明显模式时 dynamics 为 []
## where
- 场景地点,无明确地点时空字符串
## 数量规则
- 最多2个。1个够时不凑2个
- 明显场景切换(地点/时间/对象变化)时才2个
- 同一场景不拆分
- 无角色互动时返回 {"anchors":[]}
## 示例
输入:艾拉在火山口举起圣剑刺穿古龙心脏,龙血溅满她的铠甲,她跪倒在地痛哭
输出:
{"anchors":[{"scene":"火山口上艾拉举起圣剑刺穿古龙的心脏,龙血溅满铠甲,古龙轰然倒地,艾拉跪倒在滚烫的岩石上痛哭,完成了她不得不做的弑杀","who":["艾拉","古龙"],"edges":[{"s":"艾拉","t":"古龙","r":"以圣剑刺穿心脏"}],"dynamics":["决意","丧失"],"where":"火山口"}]}`;
const JSON_PREFILL = '{"anchors":[';
// ============================================================================
// 睡眠工具
// ============================================================================
const sleep = (ms) => new Promise(r => setTimeout(r, ms));
// ============================================================================
// 清洗与构建
// ============================================================================
/**
* 清洗 dynamics 标签,只保留合法枚举值
* @param {string[]} raw
* @returns {string[]}
*/
function sanitizeDynamics(raw) {
if (!Array.isArray(raw)) return [];
return raw
.map(d => String(d || '').trim())
.filter(d => VALID_DYNAMICS.has(d))
.slice(0, 2);
}
/**
* 清洗 edges 三元组
* @param {object[]} raw
* @returns {object[]}
*/
function sanitizeEdges(raw) {
if (!Array.isArray(raw)) return [];
return raw
.filter(e => e && typeof e === 'object')
.map(e => ({
s: String(e.s || '').trim(),
t: String(e.t || '').trim(),
r: String(e.r || '').trim().slice(0, 30),
}))
.filter(e => e.s && e.t && e.r)
.slice(0, 3);
}
/**
* 清洗 who 列表
* @param {string[]} raw
* @returns {string[]}
*/
function sanitizeWho(raw) {
if (!Array.isArray(raw)) return [];
const seen = new Set();
return raw
.map(w => String(w || '').trim())
.filter(w => {
if (!w || w.length < 1 || seen.has(w)) return false;
seen.add(w);
return true;
})
.slice(0, 6);
}
/**
* 将解析后的 anchor 转换为 atom 存储对象
*
* semantic = scene(纯自然语言,直接用于 embedding)
*
* @param {object} anchor - LLM 输出的 anchor 对象
* @param {number} aiFloor - AI 消息楼层号
* @param {number} idx - 同楼层序号(0 或 1)
* @returns {object|null} atom 对象
*/
function anchorToAtom(anchor, aiFloor, idx) {
const scene = String(anchor.scene || '').trim();
if (!scene) return null;
// scene 过短(< 15 字)可能是噪音
if (scene.length < 15) return null;
const who = sanitizeWho(anchor.who);
const edges = sanitizeEdges(anchor.edges);
const dynamics = sanitizeDynamics(anchor.dynamics);
const where = String(anchor.where || '').trim();
return {
atomId: `atom-${aiFloor}-${idx}`,
floor: aiFloor,
source: 'ai',
// ═══ 检索层(embedding 的唯一入口) ═══
semantic: scene,
// ═══ 图结构层(扩散的 key) ═══
who,
edges,
dynamics,
where,
};
}
// ============================================================================
// 单轮提取(带重试)
// ============================================================================
async function extractAtomsForRoundWithRetry(userMessage, aiMessage, aiFloor, options = {}) {
const { timeout = DEFAULT_TIMEOUT } = options;
if (!aiMessage?.mes?.trim()) return [];
const parts = [];
const userName = userMessage?.name || '用户';
if (userMessage?.mes?.trim()) {
const userText = filterText(userMessage.mes);
parts.push(`\n${userText}\n`);
}
const aiText = filterText(aiMessage.mes);
parts.push(`\n${aiText}\n`);
const input = `\n${parts.join('\n')}\n`;
for (let attempt = 0; attempt <= RETRY_COUNT; attempt++) {
if (batchCancelled) return [];
try {
const response = await callLLM([
{ role: 'system', content: SYSTEM_PROMPT },
{ role: 'user', content: input },
{ role: 'assistant', content: JSON_PREFILL },
], {
temperature: 0.3,
max_tokens: 600,
timeout,
});
const rawText = String(response || '');
if (!rawText.trim()) {
if (attempt < RETRY_COUNT) {
await sleep(RETRY_DELAY);
continue;
}
return null;
}
const fullJson = JSON_PREFILL + rawText;
let parsed;
try {
parsed = parseJson(fullJson);
} catch (e) {
xbLog.warn(MODULE_ID, `floor ${aiFloor} JSON解析失败 (attempt ${attempt})`);
if (attempt < RETRY_COUNT) {
await sleep(RETRY_DELAY);
continue;
}
return null;
}
// 兼容:优先 anchors,回退 atoms
const rawAnchors = parsed?.anchors || parsed?.atoms;
if (!rawAnchors || !Array.isArray(rawAnchors)) {
if (attempt < RETRY_COUNT) {
await sleep(RETRY_DELAY);
continue;
}
return null;
}
// 转换为 atom 存储格式(最多 2 个)
const atoms = rawAnchors
.slice(0, 2)
.map((a, idx) => anchorToAtom(a, aiFloor, idx))
.filter(Boolean);
return atoms;
} catch (e) {
if (batchCancelled) return null;
if (attempt < RETRY_COUNT) {
await sleep(RETRY_DELAY * (attempt + 1));
continue;
}
xbLog.error(MODULE_ID, `floor ${aiFloor} 失败`, e);
return null;
}
}
return null;
}
export async function extractAtomsForRound(userMessage, aiMessage, aiFloor, options = {}) {
return extractAtomsForRoundWithRetry(userMessage, aiMessage, aiFloor, options);
}
// ============================================================================
// 批量提取
// ============================================================================
export async function batchExtractAtoms(chat, onProgress) {
if (!chat?.length) return [];
batchCancelled = false;
const pairs = [];
for (let i = 0; i < chat.length; i++) {
if (!chat[i].is_user) {
const userMsg = (i > 0 && chat[i - 1]?.is_user) ? chat[i - 1] : null;
pairs.push({ userMsg, aiMsg: chat[i], aiFloor: i });
}
}
if (!pairs.length) return [];
const allAtoms = [];
let completed = 0;
let failed = 0;
for (let i = 0; i < pairs.length; i += CONCURRENCY) {
if (batchCancelled) break;
const batch = pairs.slice(i, i + CONCURRENCY);
if (i === 0) {
const promises = batch.map((pair, idx) => (async () => {
await sleep(idx * STAGGER_DELAY);
if (batchCancelled) return;
try {
const atoms = await extractAtomsForRoundWithRetry(
pair.userMsg,
pair.aiMsg,
pair.aiFloor,
{ timeout: DEFAULT_TIMEOUT }
);
if (atoms?.length) {
allAtoms.push(...atoms);
} else if (atoms === null) {
failed++;
}
} catch {
failed++;
}
completed++;
onProgress?.(completed, pairs.length, failed);
})());
await Promise.all(promises);
} else {
const promises = batch.map(pair =>
extractAtomsForRoundWithRetry(
pair.userMsg,
pair.aiMsg,
pair.aiFloor,
{ timeout: DEFAULT_TIMEOUT }
)
.then(atoms => {
if (batchCancelled) return;
if (atoms?.length) {
allAtoms.push(...atoms);
} else if (atoms === null) {
failed++;
}
completed++;
onProgress?.(completed, pairs.length, failed);
})
.catch(() => {
if (batchCancelled) return;
failed++;
completed++;
onProgress?.(completed, pairs.length, failed);
})
);
await Promise.all(promises);
}
if (i + CONCURRENCY < pairs.length && !batchCancelled) {
await sleep(30);
}
}
xbLog.info(MODULE_ID, `批量提取完成: ${allAtoms.length} atoms, ${failed} 失败`);
return allAtoms;
}