331 lines
10 KiB
JavaScript
331 lines
10 KiB
JavaScript
// ============================================================================
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// atom-extraction.js - L0 场景锚点提取（v2 - 场景摘要 + 图结构）
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//
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// 设计依据：
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// - BGE-M3 (BAAI, 2024): 自然语言段落检索精度最高 → semantic = 纯自然语言
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// - TransE (Bordes, 2013): s/t/r 三元组方向性 → edges 格式
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//
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// 每楼层 1-2 个场景锚点（非碎片原子），60-100 字场景摘要
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// ============================================================================
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import { callLLM, parseJson } from './llm-service.js';
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import { xbLog } from '../../../../core/debug-core.js';
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import { filterText } from '../utils/text-filter.js';
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const MODULE_ID = 'atom-extraction';
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const CONCURRENCY = 10;
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const RETRY_COUNT = 2;
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const RETRY_DELAY = 500;
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const DEFAULT_TIMEOUT = 20000;
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const STAGGER_DELAY = 80;
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let batchCancelled = false;
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export function cancelBatchExtraction() {
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batchCancelled = true;
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}
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export function isBatchCancelled() {
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return batchCancelled;
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}
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// ============================================================================
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// L0 提取 Prompt
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// ============================================================================
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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。
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输入格式：
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<round>
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<user name="用户名">...</user>
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<assistant>...</assistant>
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</round>
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只输出严格JSON：
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{"anchors":[
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{
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"scene": "60-100字完整场景描述",
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"edges": [{"s":"施事方","t":"受事方","r":"互动行为"}],
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"where": "地点"
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}
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]}
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## scene 写法
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- 纯自然语言，像旁白或日记，不要任何标签/标记/枚举值
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- 必须包含：角色名、动作、情感氛围、关键细节
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- 读者只看 scene 就能复原这一幕
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- 60-100字，信息密集但流畅
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## edges（关系三元组）
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- s=施事方 t=受事方 r=互动行为（10-15字）
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- s/t 必须是参与互动的角色正式名称，不用代词或别称
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- 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
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- 每个锚点 1-3 条
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## where
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- 场景地点，无明确地点时空字符串
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## 数量规则
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- 最多2个。1个够时不凑2个
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- 明显场景切换（地点/时间/对象变化）时才2个
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- 同一场景不拆分
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- 无角色互动时返回 {"anchors":[]}
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## 示例
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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
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输出：
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{"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","edges":[{"s":"艾拉","t":"古龙","r":"以圣剑刺穿心脏"}],"where":"火山口"}]}`;
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const JSON_PREFILL = '{"anchors":[';
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// ============================================================================
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// 睡眠工具
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// ============================================================================
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const sleep = (ms) => new Promise(r => setTimeout(r, ms));
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// ============================================================================
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// 清洗与构建
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// ============================================================================
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/**
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* 清洗 edges 三元组
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* @param {object[]} raw
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* @returns {object[]}
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*/
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function sanitizeEdges(raw) {
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if (!Array.isArray(raw)) return [];
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return raw
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.filter(e => e && typeof e === 'object')
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.map(e => ({
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s: String(e.s || '').trim(),
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t: String(e.t || '').trim(),
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r: String(e.r || '').trim().slice(0, 30),
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}))
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.filter(e => e.s && e.t && e.r)
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.slice(0, 3);
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}
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/**
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* 将解析后的 anchor 转换为 atom 存储对象
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*
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* semantic = scene（纯自然语言，直接用于 embedding）
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*
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* @param {object} anchor - LLM 输出的 anchor 对象
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* @param {number} aiFloor - AI 消息楼层号
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* @param {number} idx - 同楼层序号（0 或 1）
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* @returns {object|null} atom 对象
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*/
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function anchorToAtom(anchor, aiFloor, idx) {
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const scene = String(anchor.scene || '').trim();
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if (!scene) return null;
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// scene 过短（< 15 字）可能是噪音
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if (scene.length < 15) return null;
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const edges = sanitizeEdges(anchor.edges);
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const where = String(anchor.where || '').trim();
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return {
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atomId: `atom-${aiFloor}-${idx}`,
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floor: aiFloor,
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source: 'ai',
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// ═══ 检索层（embedding 的唯一入口） ═══
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semantic: scene,
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// ═══ 图结构层（扩散的 key） ═══
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edges,
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where,
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};
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}
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// ============================================================================
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// 单轮提取（带重试）
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// ============================================================================
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async function extractAtomsForRoundWithRetry(userMessage, aiMessage, aiFloor, options = {}) {
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const { timeout = DEFAULT_TIMEOUT } = options;
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if (!aiMessage?.mes?.trim()) return [];
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const parts = [];
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const userName = userMessage?.name || '用户';
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if (userMessage?.mes?.trim()) {
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const userText = filterText(userMessage.mes);
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parts.push(`<user name="${userName}">\n${userText}\n</user>`);
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}
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const aiText = filterText(aiMessage.mes);
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parts.push(`<assistant>\n${aiText}\n</assistant>`);
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const input = `<round>\n${parts.join('\n')}\n</round>`;
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for (let attempt = 0; attempt <= RETRY_COUNT; attempt++) {
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if (batchCancelled) return [];
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try {
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const response = await callLLM([
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{ role: 'system', content: SYSTEM_PROMPT },
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{ role: 'user', content: input },
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{ role: 'assistant', content: JSON_PREFILL },
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], {
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temperature: 0.3,
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max_tokens: 600,
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timeout,
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});
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const rawText = String(response || '');
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if (!rawText.trim()) {
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if (attempt < RETRY_COUNT) {
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await sleep(RETRY_DELAY);
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continue;
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}
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return null;
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}
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const fullJson = JSON_PREFILL + rawText;
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let parsed;
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try {
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parsed = parseJson(fullJson);
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} catch (e) {
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xbLog.warn(MODULE_ID, `floor ${aiFloor} JSON解析失败 (attempt ${attempt})`);
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if (attempt < RETRY_COUNT) {
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await sleep(RETRY_DELAY);
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continue;
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}
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return null;
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}
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// 兼容：优先 anchors，回退 atoms
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const rawAnchors = parsed?.anchors;
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if (!rawAnchors || !Array.isArray(rawAnchors)) {
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if (attempt < RETRY_COUNT) {
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await sleep(RETRY_DELAY);
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continue;
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}
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return null;
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}
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// 转换为 atom 存储格式（最多 2 个）
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const atoms = rawAnchors
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.slice(0, 2)
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.map((a, idx) => anchorToAtom(a, aiFloor, idx))
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.filter(Boolean);
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return atoms;
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} catch (e) {
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if (batchCancelled) return null;
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if (attempt < RETRY_COUNT) {
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await sleep(RETRY_DELAY * (attempt + 1));
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continue;
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}
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xbLog.error(MODULE_ID, `floor ${aiFloor} 失败`, e);
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return null;
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}
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}
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return null;
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}
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export async function extractAtomsForRound(userMessage, aiMessage, aiFloor, options = {}) {
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return extractAtomsForRoundWithRetry(userMessage, aiMessage, aiFloor, options);
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}
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// ============================================================================
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// 批量提取
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// ============================================================================
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export async function batchExtractAtoms(chat, onProgress) {
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if (!chat?.length) return [];
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batchCancelled = false;
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const pairs = [];
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for (let i = 0; i < chat.length; i++) {
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if (!chat[i].is_user) {
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const userMsg = (i > 0 && chat[i - 1]?.is_user) ? chat[i - 1] : null;
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pairs.push({ userMsg, aiMsg: chat[i], aiFloor: i });
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}
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}
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if (!pairs.length) return [];
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const allAtoms = [];
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let completed = 0;
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let failed = 0;
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for (let i = 0; i < pairs.length; i += CONCURRENCY) {
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if (batchCancelled) break;
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const batch = pairs.slice(i, i + CONCURRENCY);
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if (i === 0) {
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const promises = batch.map((pair, idx) => (async () => {
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await sleep(idx * STAGGER_DELAY);
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if (batchCancelled) return;
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try {
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const atoms = await extractAtomsForRoundWithRetry(
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pair.userMsg,
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pair.aiMsg,
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pair.aiFloor,
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{ timeout: DEFAULT_TIMEOUT }
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);
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if (atoms?.length) {
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allAtoms.push(...atoms);
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} else if (atoms === null) {
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failed++;
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}
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} catch {
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failed++;
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}
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completed++;
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onProgress?.(completed, pairs.length, failed);
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})());
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await Promise.all(promises);
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} else {
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const promises = batch.map(pair =>
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extractAtomsForRoundWithRetry(
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pair.userMsg,
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pair.aiMsg,
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pair.aiFloor,
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{ timeout: DEFAULT_TIMEOUT }
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)
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.then(atoms => {
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if (batchCancelled) return;
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if (atoms?.length) {
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allAtoms.push(...atoms);
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} else if (atoms === null) {
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failed++;
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}
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completed++;
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onProgress?.(completed, pairs.length, failed);
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})
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.catch(() => {
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if (batchCancelled) return;
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failed++;
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completed++;
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onProgress?.(completed, pairs.length, failed);
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})
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);
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await Promise.all(promises);
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}
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if (i + CONCURRENCY < pairs.length && !batchCancelled) {
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await sleep(30);
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}
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}
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xbLog.info(MODULE_ID, `批量提取完成: ${allAtoms.length} atoms, ${failed} 失败`);
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return allAtoms;
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}
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