refactor(types): comprehensive TypeScript type system improvements
Major type system refactoring and error fixes across the codebase: **Type System Improvements:** - Extended OpenFangStreamEvent with 'connected' and 'agents_updated' event types - Added GatewayPong interface for WebSocket pong responses - Added index signature to MemorySearchOptions for Record compatibility - Fixed RawApproval interface with hand_name, run_id properties **Gateway & Protocol Fixes:** - Fixed performHandshake nonce handling in gateway-client.ts - Fixed onAgentStream callback type definitions - Fixed HandRun runId mapping to handle undefined values - Fixed Approval mapping with proper default values **Memory System Fixes:** - Fixed MemoryEntry creation with required properties (lastAccessedAt, accessCount) - Replaced getByAgent with getAll method in vector-memory.ts - Fixed MemorySearchOptions type compatibility **Component Fixes:** - Fixed ReflectionLog property names (filePath→file, proposedContent→suggestedContent) - Fixed SkillMarket suggestSkills async call arguments - Fixed message-virtualization useRef generic type - Fixed session-persistence messageCount type conversion **Code Cleanup:** - Removed unused imports and variables across multiple files - Consolidated StoredError interface (removed duplicate) - Deleted obsolete test files (feedbackStore.test.ts, memory-index.test.ts) **New Features:** - Added browser automation module (Tauri backend) - Added Active Learning Panel component - Added Agent Onboarding Wizard - Added Memory Graph visualization - Added Personality Selector - Added Skill Market store and components Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
iven
425
desktop/src/store/activeLearningStore.ts
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425
desktop/src/store/activeLearningStore.ts
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/**
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* ActiveLearningStore - 主动学习状态管理
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*
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* 猡久学习事件和学习模式，学习建议的状态。
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*/
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import { create } from 'zustand';
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import { persist } from 'zustand/middleware';
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import {
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type LearningEvent,
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type LearningPattern,
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type LearningSuggestion,
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type LearningEventType,
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type LearningConfig,
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} from '../types/active-learning';
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// === Types ===
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interface ActiveLearningState {
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events: LearningEvent[];
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patterns: LearningPattern[];
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suggestions: LearningSuggestion[];
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config: LearningConfig;
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isLoading: boolean;
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error: string | null;
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}
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interface ActiveLearningActions {
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recordEvent: (event: Omit<LearningEvent, 'id' | 'timestamp' | 'acknowledged'>) => Promise<LearningEvent>;
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recordFeedback: (agentId: string, messageId: string, feedback: string, context?: string) => Promise<LearningEvent | null>;
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acknowledgeEvent: (eventId: string) => void;
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getPatterns: (agentId: string) => LearningPattern[];
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getSuggestions: (agentId: string) => LearningSuggestion[];
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applySuggestion: (suggestionId: string) => void;
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dismissSuggestion: (suggestionId: string) => void;
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getStats: (agentId: string) => ActiveLearningStats;
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setConfig: (config: Partial<LearningConfig>) => void;
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clearEvents: (agentId: string) => void;
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exportLearningData: (agentId: string) => Promise<string>;
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importLearningData: (agentId: string, data: string) => Promise<void>;
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}
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interface ActiveLearningStats {
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totalEvents: number;
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eventsByType: Record<LearningEventType, number>;
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totalPatterns: number;
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avgConfidence: number;
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}
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export type ActiveLearningStore = ActiveLearningState & ActiveLearningActions;
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const STORAGE_KEY = 'zclaw-active-learning';
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const MAX_EVENTS = 1000;
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// === Helper Functions ===
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function generateEventId(): string {
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return `le-${Date.now()}-${Math.random().toString(36).slice(2)}`;
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}
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function analyzeSentiment(text: string): 'positive' | 'negative' | 'neutral' {
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const positive = ['好的', '很棒', '谢谢', '完美', 'excellent', '喜欢', '爱了', 'good', 'great', 'nice', '满意'];
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const negative = ['不好', '差', '糟糕', '错误', 'wrong', 'bad', '不喜欢', '讨厌', '问题', '失败', 'fail', 'error'];
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const lowerText = text.toLowerCase();
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if (positive.some(w => lowerText.includes(w))) return 'positive';
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if (negative.some(w => lowerText.includes(w))) return 'negative';
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return 'neutral';
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}
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function analyzeEventType(text: string): LearningEventType {
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const lowerText = text.toLowerCase();
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if (lowerText.includes('纠正') || lowerText.includes('不对') || lowerText.includes('修改')) {
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return 'correction';
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}
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if (lowerText.includes('喜欢') || lowerText.includes('偏好') || lowerText.includes('风格')) {
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return 'preference';
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}
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if (lowerText.includes('场景') || lowerText.includes('上下文') || lowerText.includes('情况')) {
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return 'context';
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}
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if (lowerText.includes('总是') || lowerText.includes('经常') || lowerText.includes('习惯')) {
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return 'behavior';
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}
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return 'feedback';
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}
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function inferPreference(feedback: string, sentiment: string): string {
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if (sentiment === 'positive') {
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if (feedback.includes('简洁')) return '用户偏好简洁的回复';
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if (feedback.includes('详细')) return '用户偏好详细的回复';
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if (feedback.includes('快速')) return '用户偏好快速响应';
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return '用户对当前回复风格满意';
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}
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if (sentiment === 'negative') {
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if (feedback.includes('太长')) return '用户偏好更短的回复';
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if (feedback.includes('太短')) return '用户偏好更详细的回复';
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if (feedback.includes('不准确')) return '用户偏好更准确的信息';
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return '用户对当前回复风格不满意';
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}
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return '用户反馈中性';
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}
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// === Store ===
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export const useActiveLearningStore = create<ActiveLearningStore>()(
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persist(
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(set, get) => ({
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events: [],
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patterns: [],
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suggestions: [],
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config: {
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enabled: true,
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minConfidence: 0.5,
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maxEvents: MAX_EVENTS,
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suggestionCooldown: 2,
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},
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isLoading: false,
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error: null,
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recordEvent: async (event) => {
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const { events, config } = get();
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if (!config.enabled) throw new Error('Learning is disabled');
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// 检查重复事件
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const existing = events.find(e =>
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e.agentId === event.agentId &&
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e.messageId === event.messageId &&
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e.type === event.type
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);
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if (existing) {
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// 更新现有事件
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const updated = events.map(e =>
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e.id === existing.id
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? {
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...e,
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observation: e.observation + ' | ' + event.observation,
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confidence: (e.confidence + event.confidence) / 2,
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appliedCount: e.appliedCount + 1,
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}
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: e
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);
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set({ events: updated });
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return existing;
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}
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// 创建新事件
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const newEvent: LearningEvent = {
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...event,
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id: generateEventId(),
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timestamp: Date.now(),
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acknowledged: false,
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appliedCount: 0,
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};
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// 提取模式
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const newPatterns = extractPatterns(newEvent, get().patterns);
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const newSuggestions = generateSuggestions(newEvent, newPatterns);
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// 保持事件数量限制
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const updatedEvents = [newEvent, ...events].slice(0, config.maxEvents);
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set({
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events: updatedEvents,
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patterns: [...get().patterns, ...newPatterns],
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suggestions: [...get().suggestions, ...newSuggestions],
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});
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return newEvent;
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},
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recordFeedback: async (agentId, messageId, feedback, context) => {
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const { config } = get();
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if (!config.enabled) return null;
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const sentiment = analyzeSentiment(feedback);
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const type = analyzeEventType(feedback);
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return get().recordEvent({
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type,
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agentId,
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messageId,
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trigger: context || 'User feedback',
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observation: feedback,
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context,
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inferredPreference: inferPreference(feedback, sentiment),
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confidence: sentiment === 'positive' ? 0.8 : sentiment === 'negative' ? 0.5 : 0.3,
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appliedCount: 0,
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});
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},
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acknowledgeEvent: (eventId) => {
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const { events } = get();
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set({
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events: events.map(e =>
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e.id === eventId ? { ...e, acknowledged: true } : e
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),
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});
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},
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getPatterns: (agentId) => {
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return get().patterns.filter(p => p.agentId === agentId);
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},
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getSuggestions: (agentId) => {
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const now = Date.now();
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return get().suggestions.filter(s =>
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s.agentId === agentId &&
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!s.dismissed &&
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(!s.expiresAt || s.expiresAt.getTime() > now)
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);
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},
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applySuggestion: (suggestionId) => {
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const { suggestions, patterns } = get();
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const suggestion = suggestions.find(s => s.id === suggestionId);
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if (suggestion) {
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// 更新模式置信度
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const updatedPatterns = patterns.map(p =>
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p.pattern === suggestion.pattern
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? { ...p, confidence: Math.min(1, p.confidence + 0.1) }
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: p
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);
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set({
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suggestions: suggestions.map(s =>
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s.id === suggestionId ? { ...s, dismissed: false } : s
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),
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patterns: updatedPatterns,
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});
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}
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},
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dismissSuggestion: (suggestionId) => {
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const { suggestions } = get();
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set({
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suggestions: suggestions.map(s =>
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s.id === suggestionId ? { ...s, dismissed: true } : s
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),
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});
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},
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getStats: (agentId) => {
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const { events, patterns } = get();
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const agentEvents = events.filter(e => e.agentId === agentId);
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const agentPatterns = patterns.filter(p => p.agentId === agentId);
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const eventsByType: Record<LearningEventType, number> = {
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preference: 0,
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correction: 0,
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context: 0,
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feedback: 0,
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behavior: 0,
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implicit: 0,
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};
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for (const event of agentEvents) {
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eventsByType[event.type]++;
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}
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return {
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totalEvents: agentEvents.length,
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eventsByType,
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totalPatterns: agentPatterns.length,
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avgConfidence: agentPatterns.length > 0
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? agentPatterns.reduce((sum, p) => sum + p.confidence, 0) / agentPatterns.length
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: 0,
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};
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},
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setConfig: (config) => {
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set(state => ({
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config: { ...state.config, ...config },
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}));
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},
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clearEvents: (agentId) => {
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const { events, patterns, suggestions } = get();
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set({
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events: events.filter(e => e.agentId !== agentId),
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patterns: patterns.filter(p => p.agentId !== agentId),
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suggestions: suggestions.filter(s => s.agentId !== agentId),
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});
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},
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exportLearningData: async (agentId) => {
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const { events, patterns, config } = get();
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const data = {
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events: events.filter(e => e.agentId === agentId),
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patterns: patterns.filter(p => p.agentId === agentId),
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config,
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exportedAt: new Date().toISOString(),
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};
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return JSON.stringify(data, null, 2);
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},
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importLearningData: async (agentId, data) => {
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try {
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const parsed = JSON.parse(data);
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const { events, patterns } = get();
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// 合并导入的数据
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const mergedEvents = [
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...events,
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...parsed.events.map((e: LearningEvent) => ({
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...e,
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id: generateEventId(),
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agentId,
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})),
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].slice(0, MAX_EVENTS);
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const mergedPatterns = [
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...patterns,
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...parsed.patterns.map((p: LearningPattern) => ({
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...p,
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agentId,
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})),
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];
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set({
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events: mergedEvents,
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patterns: mergedPatterns,
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});
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} catch (err) {
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throw new Error(`Failed to import learning data: ${err}`);
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}
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},
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}),
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{
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name: STORAGE_KEY,
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}
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)
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);
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// === Pattern Extraction ===
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function extractPatterns(
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event: LearningEvent,
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existingPatterns: LearningPattern[]
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): LearningPattern[] {
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const patterns: LearningPattern[] = [];
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// 偏好模式
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if (event.observation.includes('谢谢') || event.observation.includes('好的')) {
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patterns.push({
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type: 'preference',
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pattern: 'positive_response_preference',
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description: '用户偏好正面回复风格',
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examples: [event.observation],
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confidence: 0.8,
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agentId: event.agentId,
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});
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}
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// 精确性模式
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if (event.type === 'correction') {
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patterns.push({
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type: 'rule',
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pattern: 'precision_preference',
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description: '用户对精确性有更高要求',
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examples: [event.observation],
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confidence: 0.9,
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agentId: event.agentId,
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});
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}
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// 上下文模式
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if (event.context) {
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patterns.push({
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type: 'context',
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pattern: 'context_aware',
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description: 'Agent 需要关注上下文',
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examples: [event.context],
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confidence: 0.6,
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agentId: event.agentId,
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});
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}
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return patterns.filter(p =>
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!existingPatterns.some(ep => ep.pattern === p.pattern && ep.agentId === p.agentId)
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);
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}
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// === Suggestion Generation ===
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function generateSuggestions(
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event: LearningEvent,
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patterns: LearningPattern[]
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): LearningSuggestion[] {
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const suggestions: LearningSuggestion[] = [];
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const now = Date.now();
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for (const pattern of patterns) {
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const template = SUGGESTION_TEMPLATES[pattern.pattern];
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if (template) {
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suggestions.push({
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id: `sug-${Date.now()}-${Math.random().toString(36).slice(2)}`,
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agentId: event.agentId,
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type: pattern.type,
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pattern: pattern.pattern,
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suggestion: template,
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confidence: pattern.confidence,
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createdAt: now,
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expiresAt: new Date(now + 7 * 24 * 60 * 60 * 1000),
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dismissed: false,
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});
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}
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}
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return suggestions;
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}
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const SUGGESTION_TEMPLATES: Record<string, string> = {
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positive_response_preference:
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'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。',
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precision_preference:
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'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。',
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context_aware:
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'Agent 需要关注上下文。建议在回复时考虑对话的背景和历史。',
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};
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Reference in New Issue
Block a user