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https://github.com/Youzini-afk/ST-Bionic-Memory-Ecology.git
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203 lines
7.4 KiB
JavaScript
203 lines
7.4 KiB
JavaScript
// ST-BME: 记忆进化引擎（A-MEM 启发）
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// 新节点写入后触发，回溯更新相关旧节点的 context/tags/links
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import { getActiveNodes, getNode, createEdge, addEdge } from './graph.js';
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import { callLLMForJSON } from './llm.js';
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import {
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buildNodeVectorText,
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findSimilarNodesByText,
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validateVectorConfig,
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} from './vector-index.js';
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/**
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* 进化系统提示词
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* 参考 A-MEM process_memory() 的进化决策 Prompt
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*/
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const EVOLUTION_SYSTEM_PROMPT = `你是一个记忆进化分析器。当新的记忆加入知识图谱时，你需要分析它与现有记忆的关系。
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你的任务：
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1. 判断新记忆是否揭示了与旧记忆相关的新信息
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2. 如果是，决定如何更新旧记忆的描述和分类
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3. 建立新旧记忆之间的有意义连接
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输出严格 JSON：
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{
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"should_evolve": true/false,
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"reason": "进化理由（简述）",
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"suggested_connections": ["需要建立链接的旧记忆ID列表"],
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"neighbor_updates": [
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{
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"nodeId": "需更新的旧节点ID",
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"newContext": "基于新信息修正后的描述（如不需修改则为null）",
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"newTags": ["更新后的分类标签，如不需修改则为null"]
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}
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]
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}
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进化规则：
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- 仅当新信息确实改变了对旧记忆的理解时才触发进化
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- 例如：揭露卧底身份 → 修正该角色之前事件中的动机描述
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- 例如：发现地点的隐藏特性 → 更新地点节点的描述
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- 不要对无关记忆强行建立联系
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- neighbor_updates 中每条必须有实际意义的修改`;
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/**
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* 记忆进化主函数
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*
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* @param {object} params
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* @param {object} params.graph - 当前图状态
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* @param {string[]} params.newNodeIds - 本次新创建的节点 ID 列表
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* @param {object} params.embeddingConfig - Embedding API 配置
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* @param {object} [params.options]
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* @returns {Promise<{evolved: number, connections: number, updates: number}>}
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*/
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export async function evolveMemories({
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graph,
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newNodeIds,
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embeddingConfig,
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options = {},
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}) {
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const neighborCount = options.neighborCount ?? 5;
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const stats = { evolved: 0, connections: 0, updates: 0 };
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if (!newNodeIds || newNodeIds.length === 0) return stats;
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if (!validateVectorConfig(embeddingConfig).valid) {
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console.log('[ST-BME] 记忆进化跳过：向量配置不可用');
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return stats;
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}
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const activeNodes = getActiveNodes(graph);
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if (activeNodes.length < 2) return stats; // 至少需要 2 个节点才有进化意义
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for (const newId of newNodeIds) {
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const newNode = getNode(graph, newId);
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if (!newNode) continue;
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const queryText = buildNodeVectorText(newNode);
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if (!queryText) continue;
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const candidates = activeNodes.filter(n => n.id !== newId);
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if (candidates.length === 0) continue;
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const neighbors = await findSimilarNodesByText(
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graph,
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queryText,
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embeddingConfig,
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neighborCount,
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candidates,
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);
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if (neighbors.length === 0) continue;
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// 构建 LLM 上下文
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const neighborsContext = neighbors.map(n => {
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const node = getNode(graph, n.nodeId);
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if (!node) return null;
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const fieldsStr = Object.entries(node.fields)
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.map(([k, v]) => `${k}: ${v}`)
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.join(', ');
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return `[${node.id}] 类型=${node.type}, ${fieldsStr}, 分类=${(node.clusters || []).join('/')}`;
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}).filter(Boolean).join('\n');
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const newNodeFieldsStr = Object.entries(newNode.fields)
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.map(([k, v]) => `${k}: ${v}`)
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.join(', ');
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const userPrompt = [
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'## 新加入的记忆',
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`[${newNode.id}] 类型=${newNode.type}, ${newNodeFieldsStr}`,
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'',
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'## 最近邻的已有记忆',
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neighborsContext,
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'',
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`共 ${neighbors.length} 条近邻记忆。请分析新记忆是否揭示了关于旧记忆的新信息。`,
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].join('\n');
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try {
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const decision = await callLLMForJSON({
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systemPrompt: EVOLUTION_SYSTEM_PROMPT,
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userPrompt,
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maxRetries: 1,
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});
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if (!decision || !decision.should_evolve) continue;
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stats.evolved++;
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console.log(`[ST-BME] 记忆进化触发: ${decision.reason || '(无理由)'}`);
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// 1. 建立链接（strengthen）
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if (decision.suggested_connections && Array.isArray(decision.suggested_connections)) {
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for (const targetId of decision.suggested_connections) {
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// 验证目标节点存在
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if (!getNode(graph, targetId)) continue;
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const edge = createEdge({
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fromId: newId,
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toId: targetId,
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relation: 'related',
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strength: 0.7,
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});
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if (addEdge(graph, edge)) {
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stats.connections++;
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}
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}
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}
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// 2. 反向更新旧节点（update_neighbor）
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if (decision.neighbor_updates && Array.isArray(decision.neighbor_updates)) {
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for (const update of decision.neighbor_updates) {
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if (!update.nodeId) continue;
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const oldNode = getNode(graph, update.nodeId);
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if (!oldNode) continue;
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let changed = false;
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// 更新 context/state 字段
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if (update.newContext && typeof update.newContext === 'string') {
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// 根据节点类型选择更新哪个字段
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if (oldNode.fields.state !== undefined) {
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oldNode.fields.state = update.newContext;
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changed = true;
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} else if (oldNode.fields.summary !== undefined) {
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oldNode.fields.summary = update.newContext;
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changed = true;
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} else if (oldNode.fields.core_note !== undefined) {
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oldNode.fields.core_note = update.newContext;
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changed = true;
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}
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}
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// 更新分类标签
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if (update.newTags && Array.isArray(update.newTags)) {
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oldNode.clusters = update.newTags;
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changed = true;
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}
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if (changed) {
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// 标记需要重新生成 embedding
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oldNode.embedding = null;
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// 记录进化历史
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if (!oldNode._evolutionHistory) oldNode._evolutionHistory = [];
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oldNode._evolutionHistory.push({
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triggeredBy: newId,
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timestamp: Date.now(),
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reason: decision.reason || '',
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});
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stats.updates++;
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}
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}
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}
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} catch (e) {
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console.error(`[ST-BME] 记忆进化失败 (${newId}):`, e);
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}
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}
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if (stats.evolved > 0) {
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console.log(
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`[ST-BME] 记忆进化完成: ${stats.evolved} 次进化, ` +
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`${stats.connections} 条新链接, ${stats.updates} 个节点回溯更新`,
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);
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}
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return stats;
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}
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