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AI-powered CTO with multi-agent orchestration, code summarization, visual testing (web + mobile) for blazing fast development.

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import { MemoryLayer } from "../memory/hexi-memory.js"; import { ClassificationResult, RoutingResult } from "../meta-rag/types.js"; import type { UserFeedback, LearningStats } from "./types.js"; /** * FeedbackLearner - Learns from user feedback to improve routing accuracy * * Features: * - Records user feedback on query results * - Stores feedback in governance layer (audit trail) * - Adjusts layer weights based on corrections * - Tracks accuracy improvement over time * - Detects common mistake patterns */ export declare class FeedbackLearner { private audit; private weights; private projectPath; constructor(projectPath?: string); /** * Initialize the feedback learner */ init(): Promise<void>; /** * Record user feedback on a query result */ recordFeedback(query: string, classification: ClassificationResult, routing: RoutingResult, feedback: UserFeedback): Promise<void>; /** * Adjust layer weights based on user corrections */ private adjustWeights; /** * Get learning statistics */ getStats(): Promise<LearningStats>; /** * Detect common mistake patterns from feedback */ private detectMistakes; /** * Calculate accuracy improvement over time * Compares first 10 feedback vs last 10 feedback */ private calculateImprovement; /** * Load all feedback records from audit log */ private loadAllFeedback; /** * Load layer weights from disk */ private loadWeights; /** * Save layer weights to disk */ private saveWeights; /** * Get current layer weights (for use in routing) */ getWeights(): Map<MemoryLayer, number>; /** * Export feedback data for fine-tuning */ exportForFineTuning(outputPath?: string): Promise<string>; } //# sourceMappingURL=feedback-learner.d.ts.map