// ============================================================================ // 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 // - 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; } // ============================================================================ // 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", "edges": [{"s":"施事方","t":"受事方","r":"互动行为"}], "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 ## edges(关系三元组) - s=施事方 t=受事方 r=互动行为(建议 6-12 字,最多 20 字) - s/t 必须是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 - r 使用动作模板短语:“动作+对象/结果”(例:“提出交易条件”、“拒绝对方请求”、“当众揭露秘密”、“安抚对方情绪”) - r 不要写人名,不要复述整句,不要写心理描写或评价词 - 每个锚点 1-3 条 ## 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","edges":[{"s":"艾拉","t":"古龙","r":"以圣剑刺穿心脏"}],"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 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); } /** * 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 edges = sanitizeEdges(anchor.edges); 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) ═══ edges, 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; 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; }