arela
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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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TypeScript
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>;
}
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