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| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 | /** * Memory Extractor - Automatically extract memorable information from conversations * * Uses LLM to analyze completed conversations and extract: * - Facts the user shared * - User preferences discovered * - Lessons learned during problem-solving * - Pending tasks or commitments * * Also handles auto-updating USER.md with discovered preferences. * * Phase 1: Rule-based extraction (pattern matching). * Phase 4: LLM-powered semantic extraction with importance scoring. * * Reference: ZCLAW_AGENT_INTELLIGENCE_EVOLUTION.md §6.2.2 */ import { intelligenceClient, type MemoryType, } from './intelligence-client'; import { getLLMAdapter, llmExtract, type LLMServiceAdapter, type LLMProvider, } from './llm-service'; // === Types === export interface ExtractedItem { content: string; type: MemoryType; importance: number; tags: string[]; } export interface ExtractionResult { items: ExtractedItem[]; saved: number; skipped: number; userProfileUpdated: boolean; } export interface ConversationMessage { role: string; content: string; } export interface ExtractionConfig { useLLM: boolean; // Use LLM for semantic extraction (Phase 4) llmProvider?: LLMProvider; // Preferred LLM provider llmFallbackToRules: boolean; // Fall back to rules if LLM fails minMessagesForExtraction: number; // Minimum messages before extraction extractionCooldownMs: number; // Cooldown between extractions minImportanceThreshold: number; // Only save items with importance >= this } // === Extraction Prompt === const EXTRACTION_PROMPT = `请从以下对话中提取值得长期记住的信息。 只提取以下类型： - fact: 用户告知的事实（如"我的公司叫 XXX"、"我在做 YYY 项目"） - preference: 用户的偏好（如"我喜欢简洁的回答"、"请用中文"） - lesson: 本次对话的经验教训（如"调用 API 前需要先验证 token"） - task: 未完成的任务或承诺（如"下次帮我检查 XXX"） 评估规则： - importance 1-3: 临时性、不太重要的信息 - importance 4-6: 有一定参考价值的信息 - importance 7-9: 重要的持久信息 - importance 10: 极其关键的信息 输出**纯 JSON 数组**，每项包含 content, type, importance, tags[]。 如果没有值得记忆的内容，返回空数组 []。 不要输出任何其他内容，只输出 JSON。 对话内容： `; // === Default Config === export const DEFAULT_EXTRACTION_CONFIG: ExtractionConfig = { useLLM: true, // Enable LLM-powered semantic extraction by default llmFallbackToRules: true, minMessagesForExtraction: 2, // Lowered from 4 to capture memories earlier extractionCooldownMs: 30_000, minImportanceThreshold: 3, }; // === Memory Extractor === export class MemoryExtractor { private config: ExtractionConfig; private lastExtractionTime = 0; private llmAdapter: LLMServiceAdapter | null = null; constructor(config?: Partial<ExtractionConfig>) { this.config = { ...DEFAULT_EXTRACTION_CONFIG, ...config }; // Initialize LLM adapter if configured if (this.config.useLLM) { try { this.llmAdapter = getLLMAdapter(); } catch (error) { console.warn('[MemoryExtractor] Failed to initialize LLM adapter:', error); } } } /** * Extract memories from a conversation. * Uses LLM if configured, falls back to rule-based extraction. */ async extractFromConversation( messages: ConversationMessage[], agentId: string, conversationId?: string, options?: { forceLLM?: boolean } ): Promise<ExtractionResult> { // Cooldown check if (Date.now() - this.lastExtractionTime < this.config.extractionCooldownMs) { console.log('[MemoryExtractor] Skipping extraction: cooldown active'); return { items: [], saved: 0, skipped: 0, userProfileUpdated: false }; } // Minimum message threshold const chatMessages = messages.filter(m => m.role === 'user' || m.role === 'assistant'); console.log(`[MemoryExtractor] Checking extraction: ${chatMessages.length} messages (min: ${this.config.minMessagesForExtraction})`); if (chatMessages.length < this.config.minMessagesForExtraction) { console.log('[MemoryExtractor] Skipping extraction: not enough messages'); return { items: [], saved: 0, skipped: 0, userProfileUpdated: false }; } this.lastExtractionTime = Date.now(); // Try LLM extraction if enabled let extracted: ExtractedItem[]; if ((this.config.useLLM || options?.forceLLM) && this.llmAdapter?.isAvailable()) { try { console.log('[MemoryExtractor] Using LLM-powered semantic extraction'); extracted = await this.llmBasedExtraction(chatMessages); } catch (error) { console.error('[MemoryExtractor] LLM extraction failed:', error); if (!this.config.llmFallbackToRules) { throw error; } console.log('[MemoryExtractor] Falling back to rule-based extraction'); extracted = this.ruleBasedExtraction(chatMessages); } } else { // Rule-based extraction console.log('[MemoryExtractor] Using rule-based extraction'); extracted = this.ruleBasedExtraction(chatMessages); console.log(`[MemoryExtractor] Rule-based extracted ${extracted.length} items before filtering`); } // Filter by importance threshold extracted = extracted.filter(item => item.importance >= this.config.minImportanceThreshold); console.log(`[MemoryExtractor] After importance filtering (>= ${this.config.minImportanceThreshold}): ${extracted.length} items`); // Save to memory let saved = 0; let skipped = 0; for (const item of extracted) { try { await intelligenceClient.memory.store({ agent_id: agentId, memory_type: item.type, content: item.content, importance: item.importance, source: 'auto', tags: item.tags, conversation_id: conversationId, }); saved++; } catch { skipped++; } } // Auto-update USER.md with preferences let userProfileUpdated = false; const preferences = extracted.filter(e => e.type === 'preference' && e.importance >= 5); if (preferences.length > 0) { try { const prefSummary = preferences.map(p => `- ${p.content}`).join('\n'); await intelligenceClient.identity.appendUserProfile(agentId, `### 自动发现的偏好 (${new Date().toLocaleDateString('zh-CN')})\n${prefSummary}`); userProfileUpdated = true; } catch (err) { console.warn('[MemoryExtractor] Failed to update USER.md:', err); } } if (saved > 0) { console.log(`[MemoryExtractor] Extracted ${saved} memories from conversation (${skipped} skipped)`); } return { items: extracted, saved, skipped, userProfileUpdated }; } /** * LLM-powered semantic extraction. * Uses LLM to understand context and score importance semantically. */ private async llmBasedExtraction(messages: ConversationMessage[]): Promise<ExtractedItem[]> { const conversationText = messages .filter(m => m.role === 'user' || m.role === 'assistant') .map(m => `[${m.role === 'user' ? '用户' : '助手'}]: ${m.content}`) .join('\n\n'); // Use llmExtract helper from llm-service const llmResponse = await llmExtract(conversationText, this.llmAdapter!); // Parse the JSON response return this.parseExtractionResponse(llmResponse); } /** * Phase 1: Rule-based extraction using pattern matching. * Extracts common patterns from user messages. */ private ruleBasedExtraction(messages: ConversationMessage[]): ExtractedItem[] { const items: ExtractedItem[] = []; const userMessages = messages.filter(m => m.role === 'user').map(m => m.content); for (const msg of userMessages) { // Fact patterns this.extractFacts(msg, items); // Preference patterns this.extractPreferences(msg, items); // Task patterns this.extractTasks(msg, items); } // Lesson extraction from assistant messages (error corrections, solutions) const assistantMessages = messages.filter(m => m.role === 'assistant').map(m => m.content); this.extractLessons(userMessages, assistantMessages, items); return items; } private extractFacts(msg: string, items: ExtractedItem[]): void { // "我的/我们的 X 是/叫 Y" patterns const factPatterns = [ /我(?:的|们的|们)(\S{1,20})(?:是|叫|名叫|名字是)(.{2,50})/g, /(?:公司|团队|项目|产品)(?:名|名称)?(?:是|叫)(.{2,30})/g, /我(?:在|正在)(?:做|开发|使用|学习)(.{2,40})/g, /我(?:是|做)(.{2,30})(?:的|工作)/g, ]; for (const pattern of factPatterns) { const matches = msg.matchAll(pattern); for (const match of matches) { const content = match[0].trim(); if (content.length > 5 && content.length < 100) { items.push({ content, type: 'fact', importance: 6, tags: ['auto-extracted'], }); } } } } private extractPreferences(msg: string, items: ExtractedItem[]): void { const prefPatterns = [ /(?:我喜欢|我偏好|我习惯|请用|请使用|默认用|我更愿意)(.{2,50})/g, /(?:不要|别|不用)(.{2,30})(?:了|吧)?/g, /(?:以后|下次|每次)(?:都)?(.{2,40})/g, /(?:用中文|用英文|简洁|详细|简短)(?:一点|回复|回答)?/g, ]; for (const pattern of prefPatterns) { const matches = msg.matchAll(pattern); for (const match of matches) { const content = match[0].trim(); if (content.length > 3 && content.length < 80) { items.push({ content: `用户偏好: ${content}`, type: 'preference', importance: 5, tags: ['auto-extracted', 'preference'], }); } } } } private extractTasks(msg: string, items: ExtractedItem[]): void { const taskPatterns = [ /(?:帮我|帮忙|记得|别忘了|下次|以后|待办)(.{5,60})/g, /(?:TODO|todo|FIXME|fixme)[:\s]*(.{5,60})/g, ]; for (const pattern of taskPatterns) { const matches = msg.matchAll(pattern); for (const match of matches) { const content = match[0].trim(); if (content.length > 5 && content.length < 100) { items.push({ content, type: 'task', importance: 7, tags: ['auto-extracted', 'task'], }); } } } } private extractLessons( _userMessages: string[], assistantMessages: string[], items: ExtractedItem[] ): void { // Look for error resolution patterns in assistant messages for (const msg of assistantMessages) { // "问题是/原因是/根因是" patterns const lessonPatterns = [ /(?:问题是|原因是|根因是|解决方法是|关键是)(.{10,100})/g, /(?:需要注意|要注意|注意事项)[：:](.{10,80})/g, ]; for (const pattern of lessonPatterns) { const matches = msg.matchAll(pattern); for (const match of matches) { const content = match[0].trim(); if (content.length > 10 && content.length < 150) { items.push({ content, type: 'lesson', importance: 6, tags: ['auto-extracted', 'lesson'], }); } } } } } /** * Build the LLM extraction prompt for a conversation. * For Phase 2: send this to LLM and parse the JSON response. */ buildExtractionPrompt(messages: ConversationMessage[]): string { const conversationText = messages .filter(m => m.role === 'user' || m.role === 'assistant') .map(m => `[${m.role === 'user' ? '用户' : '助手'}]: ${m.content}`) .join('\n\n'); return EXTRACTION_PROMPT + conversationText; } /** * Parse LLM extraction response. * For Phase 2: parse the JSON array from LLM response. */ parseExtractionResponse(response: string): ExtractedItem[] { try { // Find JSON array in response const jsonMatch = response.match(/\[[\s\S]*\]/); if (!jsonMatch) return []; const parsed = JSON.parse(jsonMatch[0]); if (!Array.isArray(parsed)) return []; return parsed .filter((item: Record<string, unknown>) => item.content && item.type && item.importance !== undefined ) .map((item: Record<string, unknown>) => ({ content: String(item.content), type: item.type as MemoryType, importance: Math.max(1, Math.min(10, Number(item.importance))), tags: Array.isArray(item.tags) ? item.tags.map(String) : [], })); } catch { console.warn('[MemoryExtractor] Failed to parse LLM extraction response'); return []; } } } // === Singleton === let _instance: MemoryExtractor | null = null; export function getMemoryExtractor(): MemoryExtractor { if (!_instance) { _instance = new MemoryExtractor(); } return _instance; } export function resetMemoryExtractor(): void { _instance = null; } |