Press n or j to go to the next uncovered block, b, p or k for the previous block.
| 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 | /**
* 主动学习引擎 - 从用户交互中学习并改进 Agent 行为
*
* 提供学习事件记录、模式提取和建议生成功能。
* Phase 1: 内存存储，Zustand 持久化
* Phase 2: SQLite + 向量化存储
*/
import {
type LearningEvent,
type LearningPattern,
type LearningSuggestion,
type LearningEventType,
type FeedbackSentiment,
} from '../types/active-learning';
// === 常量 ===
const MAX_EVENTS = 1000;
const PATTERN_CONFIDENCE_THRESHOLD = 0.7;
const SUGGESTION_COOLDOWN_HOURS = 2;
// === 生成 ID ===
function generateEventId(): string {
return `le-${Date.now()}-${Math.random().toString(36).slice(2)}`;
}
// === 分析反馈情感 ===
export function analyzeSentiment(text: string): FeedbackSentiment {
const positive = ['好的', '很棒', '谢谢', '完美', 'excellent', '喜欢', '爱了', 'good', 'great', 'nice', '满意'];
const negative = ['不好', '差', '糟糕', '错误', 'wrong', 'bad', '不喜欢', '讨厌', '问题', '失败', 'fail', 'error'];
const lowerText = text.toLowerCase();
if (positive.some(w => lowerText.includes(w.toLowerCase()))) return 'positive';
if (negative.some(w => lowerText.includes(w.toLowerCase()))) return 'negative';
return 'neutral';
}
// === 分析学习类型 ===
export function analyzeEventType(text: string): LearningEventType {
const lowerText = text.toLowerCase();
if (lowerText.includes('纠正') || lowerText.includes('不对') || lowerText.includes('修改')) {
return 'correction';
}
if (lowerText.includes('喜欢') || lowerText.includes('偏好') || lowerText.includes('风格')) {
return 'preference';
}
if (lowerText.includes('场景') || lowerText.includes('上下文') || lowerText.includes('情况')) {
return 'context';
}
if (lowerText.includes('总是') || lowerText.includes('经常') || lowerText.includes('习惯')) {
return 'behavior';
}
return 'implicit';
}
// === 推断偏好 ===
export function inferPreference(feedback: string, sentiment: FeedbackSentiment): string {
if (sentiment === 'positive') {
if (feedback.includes('简洁')) return '用户偏好简洁的回复';
if (feedback.includes('详细')) return '用户偏好详细的回复';
if (feedback.includes('快速')) return '用户偏好快速响应';
return '用户对当前回复风格满意';
}
if (sentiment === 'negative') {
if (feedback.includes('太长')) return '用户偏好更短的回复';
if (feedback.includes('太短')) return '用户偏好更详细的回复';
if (feedback.includes('不准确')) return '用户偏好更准确的信息';
return '用户对当前回复风格不满意';
}
return '用户反馈中性';
}
// === 学习引擎类 ===
export class ActiveLearningEngine {
private events: LearningEvent[] = [];
private patterns: LearningPattern[] = [];
// Reserved for future learning suggestions feature
private suggestions: LearningSuggestion[] = [];
private initialized: boolean = false;
constructor() {
this.initialized = true;
}
/** Get current suggestions (reserved for future use) */
getSuggestions(): LearningSuggestion[] {
return this.suggestions;
}
/** Check if engine is initialized */
isInitialized(): boolean {
return this.initialized;
}
/**
* 记录学习事件
*/
recordEvent(
event: Omit<LearningEvent, 'id' | 'timestamp' | 'acknowledged' | 'appliedCount'>
): LearningEvent {
// 检查重复事件
const existing = this.events.find(e =>
e.agentId === event.agentId &&
e.messageId === event.messageId &&
e.type === event.type
);
if (existing) {
// 更新现有事件
existing.observation += ' | ' + event.observation;
existing.confidence = (existing.confidence + event.confidence) / 2;
existing.appliedCount++;
return existing;
}
// 创建新事件
const newEvent: LearningEvent = {
...event,
id: generateEventId(),
timestamp: Date.now(),
acknowledged: false,
appliedCount: 0,
};
this.events.push(newEvent);
this.extractPatterns(newEvent);
// 保持事件数量限制
if (this.events.length > MAX_EVENTS) {
this.events = this.events.slice(-MAX_EVENTS);
}
return newEvent;
}
/**
* 从反馈中学习
*/
learnFromFeedback(
agentId: string,
messageId: string,
feedback: string,
context?: string
): LearningEvent {
const sentiment = analyzeSentiment(feedback);
const type = analyzeEventType(feedback);
return this.recordEvent({
type,
agentId,
messageId,
trigger: context || 'User feedback',
observation: feedback,
context,
inferredPreference: inferPreference(feedback, sentiment),
confidence: sentiment === 'positive' ? 0.8 : sentiment === 'negative' ? 0.5 : 0.3,
});
}
/**
* 提取学习模式
*/
private extractPatterns(event: LearningEvent): void {
// 1. 正面反馈 -> 偏好正面回复
if (event.observation.includes('谢谢') || event.observation.includes('好的')) {
this.addPattern({
type: 'preference',
pattern: 'positive_response_preference',
description: '用户偏好正面回复风格',
examples: [event.observation],
confidence: 0.8,
agentId: event.agentId,
});
}
// 2. 纠正 -> 需要更精确
if (event.type === 'correction') {
this.addPattern({
type: 'rule',
pattern: 'precision_preference',
description: '用户对精确性有更高要求',
examples: [event.observation],
confidence: 0.9,
agentId: event.agentId,
});
}
// 3. 上下文相关 -> 场景偏好
if (event.context) {
this.addPattern({
type: 'context',
pattern: 'context_aware',
description: 'Agent 需要关注上下文',
examples: [event.context],
confidence: 0.6,
agentId: event.agentId,
});
}
}
/**
* 添加学习模式
*/
private addPattern(pattern: Omit<LearningPattern, 'updatedAt'>): void {
const existing = this.patterns.find(p =>
p.type === pattern.type &&
p.pattern === pattern.pattern &&
p.agentId === pattern.agentId
);
if (existing) {
// 增强置信度
existing.confidence = Math.min(1, existing.confidence + pattern.confidence * 0.1);
existing.examples.push(pattern.examples[0]);
existing.updatedAt = Date.now();
} else {
this.patterns.push({
...pattern,
updatedAt: Date.now(),
});
}
}
/**
* 生成学习建议
*/
generateSuggestions(agentId: string): LearningSuggestion[] {
const suggestions: LearningSuggestion[] = [];
const now = Date.now();
// 获取该 Agent 的模式
const agentPatterns = this.patterns.filter(p => p.agentId === agentId);
for (const pattern of agentPatterns) {
// 检查冷却时间
const hoursSinceUpdate = (now - (pattern.updatedAt || now)) / (1000 * 60 * 60);
if (hoursSinceUpdate < SUGGESTION_COOLDOWN_HOURS) continue;
// 检查置信度阈值
if (pattern.confidence < PATTERN_CONFIDENCE_THRESHOLD) continue;
// 生成建议
suggestions.push({
id: `sug-${Date.now()}-${Math.random().toString(36).slice(2)}`,
agentId,
type: pattern.type,
pattern: pattern.pattern,
suggestion: this.generateSuggestionContent(pattern),
confidence: pattern.confidence,
createdAt: now,
expiresAt: new Date(now + 7 * 24 * 60 * 60 * 1000),
dismissed: false,
});
}
return suggestions;
}
/**
* 生成建议内容
*/
private generateSuggestionContent(pattern: LearningPattern): string {
const templates: Record<string, string> = {
positive_response_preference:
'用户似乎偏好正面回复。建议在回复时保持积极和确认的语气。',
precision_preference:
'用户对精确性有更高要求。建议在提供信息时更加详细和准确。',
context_aware:
'Agent 需要关注上下文。建议在回复时考虑对话的背景和历史。',
};
return templates[pattern.pattern] || `观察到模式: ${pattern.pattern}`;
}
/**
* 获取统计信息
*/
getStats(agentId: string) {
const agentEvents = this.events.filter(e => e.agentId === agentId);
const agentPatterns = this.patterns.filter(p => p.agentId === agentId);
const eventsByType: Record<LearningEventType, number> = {
preference: 0,
correction: 0,
context: 0,
feedback: 0,
behavior: 0,
implicit: 0,
};
for (const event of agentEvents) {
eventsByType[event.type]++;
}
return {
totalEvents: agentEvents.length,
eventsByType,
totalPatterns: agentPatterns.length,
avgConfidence: agentPatterns.length > 0
? agentPatterns.reduce((sum, p) => sum + p.confidence, 0) / agentPatterns.length
: 0,
};
}
/**
* 获取所有事件
*/
getEvents(agentId?: string): LearningEvent[] {
if (agentId) {
return this.events.filter(e => e.agentId === agentId);
}
return [...this.events];
}
/**
* 获取所有模式
*/
getPatterns(agentId?: string): LearningPattern[] {
if (agentId) {
return this.patterns.filter(p => p.agentId === agentId);
}
return [...this.patterns];
}
/**
* 确认事件
*/
acknowledgeEvent(eventId: string): void {
const event = this.events.find(e => e.id === eventId);
if (event) {
event.acknowledged = true;
}
}
/**
* 清除事件
*/
clearEvents(agentId: string): void {
this.events = this.events.filter(e => e.agentId !== agentId);
this.patterns = this.patterns.filter(p => p.agentId !== agentId);
}
}
// === 单例实例 ===
let engineInstance: ActiveLearningEngine | null = null;
export function getActiveLearningEngine(): ActiveLearningEngine {
if (!engineInstance) {
engineInstance = new ActiveLearningEngine();
}
return engineInstance;
}
export function resetActiveLearningEngine(): void {
engineInstance = null;
}
|