feat: Agent 记忆系统替代预设功能，全功能协调运作
核心改造： - 取消 Agent 预设（保留兼容），新增 AI Agent 记忆系统 - 自动提取 → 存储 → 检索 → 注入上下文的完整记忆闭环 - 记忆类型：事实/偏好/规则/事件，支持重要性评分和标签 新增文件： - services/memory-manager.ts: 记忆管理核心 - 关键词+标签+重要性加权检索 - 会话结束自动提取（LLM 驱动） - 去重检测、使用频率追踪 - components/memory-panel.ts: 记忆管理面板 UI 修改文件： - types.d.ts: 新增 MemoryEntry/MemorySearchResult 类型 - db/chat-db.ts: 升级 v2，新增 memories 存储 - input-area.ts: 对话流集成记忆检索+自动提取 - agent-engine.ts: Agent Loop 注入记忆上下文 - settings-modal.ts: 记忆开关设置 - main.ts: 记忆系统初始化 - index.html: 记忆设置面板 HTML - style.css: 记忆面板完整样式 功能协调： - 对话流: 记忆检索 → RAG 检索 → 系统提示词组合 → 流式对话 - Agent Loop: 记忆检索 → 系统提示词注入 → 工具调用循环 - 会话结束: 自动提取关键信息 → 存入记忆库 - 跨会话: 记忆持久化 IndexedDB，新对话自动召回相关记忆
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/**
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* MemoryManager - Agent 记忆系统
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* 自动提取 → 存储 → 检索 → 注入上下文
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*
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* 记忆类型：
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* - fact: 用户告诉我的事实（项目信息、个人背景等）
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* - preference: 用户偏好（语言、风格、格式习惯）
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* - rule: 应遵守的规则（命名规范、输出格式要求）
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* - episode: 重要事件（完成了什么、得到了什么结论）
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*/
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import { state, KEYS } from '../state/state.js';
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import { generateId } from '../utils/utils.js';
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import type { MemoryEntry, MemorySearchResult, MemoryExtractionResult, ChatDB, OllamaAPI } from '../types.js';
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const TYPE_ICONS: Record<string, string> = {
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fact: '📌',
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preference: '⚙️',
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rule: '📏',
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episode: '📝'
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};
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const TYPE_NAMES: Record<string, string> = {
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fact: '事实',
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preference: '偏好',
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rule: '规则',
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episode: '事件'
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};
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let memoryCache: MemoryEntry[] = [];
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let memoryEnabled = true;
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export async function initMemoryManager(): Promise<void> {
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const db = state.get<ChatDB | null>(KEYS.DB);
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if (!db) return;
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memoryEnabled = await db.getSetting('memoryEnabled', true);
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state.set('memoryEnabled', memoryEnabled);
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memoryCache = await db.getAllMemories();
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state.set('memoryEntries', memoryCache);
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console.log(`[Memory] 初始化完成，加载 ${memoryCache.length} 条记忆`);
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}
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export function isMemoryEnabled(): boolean {
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return memoryEnabled;
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}
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export function setMemoryEnabled(enabled: boolean): void {
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memoryEnabled = enabled;
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state.set('memoryEnabled', enabled);
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const db = state.get<ChatDB | null>(KEYS.DB);
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if (db) db.saveSetting('memoryEnabled', enabled);
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}
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export function getMemoryCache(): MemoryEntry[] {
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return memoryCache;
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}
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export function getTypeIcon(type: string): string {
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return TYPE_ICONS[type] || '📌';
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}
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export function getTypeName(type: string): string {
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return TYPE_NAMES[type] || type;
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}
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// ── 记忆检索（关键词 + 标签 + 重要性加权）──
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export function searchMemories(query: string, limit = 8): MemorySearchResult[] {
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if (!memoryEnabled || memoryCache.length === 0) return [];
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const queryLower = query.toLowerCase();
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const queryWords = queryLower.split(/[\s,，。！？、；：""''（）\[\]{}<>`~@#$%^&*+=|\\/.]+/).filter(w => w.length > 1);
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const scored = memoryCache.map(entry => {
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let score = 0;
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const contentLower = entry.content.toLowerCase();
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const tagsLower = entry.tags.map(t => t.toLowerCase());
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// 完全匹配内容
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if (contentLower.includes(queryLower)) score += 50;
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// 标签匹配
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for (const tag of tagsLower) {
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if (queryLower.includes(tag) || tag.includes(queryLower)) score += 30;
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for (const word of queryWords) {
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if (tag.includes(word)) score += 15;
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}
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}
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// 关键词匹配
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for (const word of queryWords) {
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if (contentLower.includes(word)) score += 10;
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}
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// 重要性加权
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score *= (0.5 + entry.importance / 20);
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// 最近使用加权（7 天内使用过 +20%）
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const daysSinceUse = (Date.now() - entry.lastUsedAt) / (1000 * 60 * 60 * 24);
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if (daysSinceUse < 7) score *= 1.2;
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// 使用频率加权
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score *= (1 + Math.min(entry.useCount, 10) / 50);
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return { ...entry, score };
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});
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return scored
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.filter(s => s.score > 0)
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.sort((a, b) => b.score - a.score)
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.slice(0, limit);
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}
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// ── 记忆添加 ──
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export async function addMemory(data: {
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type: MemoryEntry['type'];
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content: string;
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importance?: number;
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tags?: string[];
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source?: string;
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sessionId?: string;
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}): Promise<MemoryEntry> {
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const db = state.get<ChatDB | null>(KEYS.DB);
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// 检查重复（内容相似度 > 80% 则跳过）
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const existing = memoryCache.find(e => {
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if (e.type !== data.type) return false;
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const similarity = simpleSimilarity(e.content, data.content);
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return similarity > 0.8;
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});
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if (existing) return existing;
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const entry: MemoryEntry = {
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id: `mem_${generateId()}`,
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type: data.type,
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content: data.content.trim(),
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importance: data.importance ?? 5,
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tags: data.tags || extractTags(data.content),
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source: data.source,
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sessionId: data.sessionId,
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createdAt: Date.now(),
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updatedAt: Date.now(),
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lastUsedAt: Date.now(),
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useCount: 0
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};
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memoryCache.push(entry);
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state.set('memoryEntries', [...memoryCache]);
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if (db) await db.saveMemory(entry);
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return entry;
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}
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// ── 记忆更新 ──
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export async function updateMemory(id: string, updates: Partial<MemoryEntry>): Promise<void> {
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const idx = memoryCache.findIndex(e => e.id === id);
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if (idx === -1) return;
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memoryCache[idx] = { ...memoryCache[idx], ...updates, updatedAt: Date.now() };
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state.set('memoryEntries', [...memoryCache]);
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const db = state.get<ChatDB | null>(KEYS.DB);
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if (db) await db.saveMemory(memoryCache[idx]);
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}
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// ── 记忆删除 ──
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export async function deleteMemory(id: string): Promise<void> {
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memoryCache = memoryCache.filter(e => e.id !== id);
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state.set('memoryEntries', [...memoryCache]);
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const db = state.get<ChatDB | null>(KEYS.DB);
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if (db) await db.deleteMemory(id);
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}
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// ── 清空所有记忆 ──
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export async function clearAllMemories(): Promise<void> {
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memoryCache = [];
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state.set('memoryEntries', []);
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const db = state.get<ChatDB | null>(KEYS.DB);
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if (db) await db.clearAllMemories();
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}
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// ── 标记记忆被使用 ──
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export async function markMemoryUsed(id: string): Promise<void> {
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const entry = memoryCache.find(e => e.id === id);
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if (!entry) return;
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entry.useCount++;
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entry.lastUsedAt = Date.now();
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const db = state.get<ChatDB | null>(KEYS.DB);
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if (db) await db.saveMemory(entry);
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}
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// ── 构建记忆上下文（注入 system prompt）──
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export function buildMemoryContext(memories: MemorySearchResult[]): string {
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if (memories.length === 0) return '';
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const grouped: Record<string, MemorySearchResult[]> = {};
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for (const m of memories) {
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if (!grouped[m.type]) grouped[m.type] = [];
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grouped[m.type].push(m);
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}
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let context = '【关于用户的重要信息】\n';
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context += '以下是你之前记住的关于用户的信息，在回答时请参考：\n\n';
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for (const [type, items] of Object.entries(grouped)) {
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const typeName = TYPE_NAMES[type] || type;
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context += `— ${typeName} —\n`;
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for (const item of items) {
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context += ` • ${item.content}\n`;
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}
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context += '\n';
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}
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context += '请自然地利用这些信息，不要生硬地列举。如果信息与当前对话无关，忽略即可。';
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return context;
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}
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// ── 自动提取记忆（会话结束时调用）──
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export async function extractMemoriesFromConversation(
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messages: Array<{ role: string; content: string }>,
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sessionTitle?: string
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): Promise<number> {
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if (!memoryEnabled) return 0;
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if (messages.length < 3) return 0; // 至少一轮完整对话
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const api = state.get<OllamaAPI>(KEYS.API);
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const model = state.get<string>('_defaultModel', '');
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if (!api || !model) return 0;
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// 取最近 20 条消息作为提取素材
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const recentMessages = messages.slice(-20);
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const conversationText = recentMessages
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.filter(m => m.role === 'user' || m.role === 'assistant')
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.map(m => `[${m.role === 'user' ? '用户' : 'AI'}]: ${m.content}`)
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.join('\n\n');
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const extractPrompt = `你是一个记忆提取系统。请分析以下对话，提取值得长期记住的关键信息。
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对话内容：
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${conversationText.slice(0, 4000)}
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请以 JSON 格式返回提取结果（不要输出其他内容）：
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\`\`\`json
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{
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"entries": [
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{
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"type": "fact",
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"content": "用户正在开发一个 Electron 桌面应用",
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"importance": 7,
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"tags": ["项目", "Electron", "开发"]
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}
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]
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}
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\`\`\`
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记忆类型说明：
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- fact: 关于用户的事实（项目、身份、背景、习惯）
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- preference: 用户偏好（语言风格、输出格式、技术栈偏好）
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- rule: 用户要求遵守的规则（编码规范、输出要求）
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- episode: 重要事件（完成的任务、达成的结论）
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提取规则：
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1. 只提取真正有价值、值得跨会话记住的信息
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2. 每条记忆精炼简洁，不超过 50 字
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3. importance 1-10，5 为普通，8+ 为必须记住
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4. tags 2-5 个关键词，用于检索匹配
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5. 最多提取 5 条，宁缺毋滥
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6. 如果对话中没有值得记住的信息，返回 {"entries": []}`;
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try {
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const response = await api.chat({
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model,
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messages: [{ role: 'user', content: extractPrompt }],
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stream: false,
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think: false,
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options: { num_ctx: 8192, temperature: 0.1 }
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});
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const content = (response as { message?: { content?: string } })?.message?.content || '';
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const jsonMatch = content.match(/```json\s*([\s\S]*?)\s*```/) || content.match(/\{[\s\S]*"entries"[\s\S]*\}/);
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if (!jsonMatch) return 0;
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const parsed: MemoryExtractionResult = JSON.parse(jsonMatch[1] || jsonMatch[0]);
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if (!parsed.entries?.length) return 0;
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let count = 0;
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const currentSession = state.get(KEYS.CURRENT_SESSION);
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for (const entry of parsed.entries) {
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if (!entry.content || entry.content.length < 5) continue;
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await addMemory({
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type: entry.type || 'fact',
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content: entry.content,
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importance: Math.min(10, Math.max(1, entry.importance || 5)),
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tags: entry.tags || [],
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source: sessionTitle || '自动提取',
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sessionId: currentSession?.id
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});
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count++;
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}
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return count;
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} catch (err) {
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console.warn('[Memory] 自动提取失败:', err);
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return 0;
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}
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}
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// ── 工具函数 ──
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function extractTags(text: string): string[] {
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const words = text
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.replace(/[^\w\u4e00-\u9fff\s]/g, ' ')
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.split(/\s+/)
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.filter(w => w.length > 1);
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return [...new Set(words)].slice(0, 5);
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}
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function simpleSimilarity(a: string, b: string): number {
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const aWords = new Set(a.toLowerCase().split(/\s+/));
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const bWords = new Set(b.toLowerCase().split(/\s+/));
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let intersection = 0;
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for (const w of aWords) {
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if (bWords.has(w)) intersection++;
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}
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const union = aWords.size + bWords.size - intersection;
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return union === 0 ? 0 : intersection / union;
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}
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