Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'

vendor/ruvector/npm/packages/agentic-synth/training/dspy-real-integration.d.ts

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+/**
+ * DSPy.ts Real Integration with Agentic-Synth
+ *
+ * Production-ready integration using actual dspy.ts npm package (v2.1.1)
+ * for synthetic data generation optimization and quality improvement.
+ *
+ * Features:
+ * - ChainOfThought reasoning for data quality assessment
+ * - BootstrapFewShot optimization for learning from successful generations
+ * - Multi-model support (OpenAI, Claude via dspy.ts)
+ * - Real-time quality metrics and evaluation
+ * - Integration with agentic-synth generators
+ *
+ * @packageDocumentation
+ */
+import { EventEmitter } from 'events';
+/**
+ * DSPy trainer configuration
+ */
+export interface DSPyTrainerConfig {
+    models: string[];
+    optimizationRounds?: number;
+    minQualityScore?: number;
+    maxExamples?: number;
+    batchSize?: number;
+    evaluationMetrics?: string[];
+    enableCaching?: boolean;
+    hooks?: {
+        onIterationComplete?: (iteration: number, metrics: QualityMetrics) => void;
+        onOptimizationComplete?: (result: TrainingResult) => void;
+        onError?: (error: Error) => void;
+    };
+}
+/**
+ * Quality metrics for generated data
+ */
+export interface QualityMetrics {
+    accuracy: number;
+    coherence: number;
+    relevance: number;
+    diversity: number;
+    overallScore: number;
+    timestamp: Date;
+}
+/**
+ * Training iteration result
+ */
+export interface IterationMetrics {
+    iteration: number;
+    model: string;
+    quality: QualityMetrics;
+    generatedCount: number;
+    duration: number;
+    tokenUsage?: number;
+}
+/**
+ * Complete training result
+ */
+export interface TrainingResult {
+    success: boolean;
+    iterations: IterationMetrics[];
+    bestIteration: IterationMetrics;
+    optimizedPrompt: string;
+    improvements: {
+        initialScore: number;
+        finalScore: number;
+        improvement: number;
+    };
+    metadata: {
+        totalDuration: number;
+        modelsUsed: string[];
+        totalGenerated: number;
+        convergenceIteration?: number;
+    };
+}
+/**
+ * Evaluation result from dspy.ts
+ */
+export interface EvaluationResult {
+    metrics: {
+        [key: string]: number;
+    };
+    passed: number;
+    failed: number;
+    total: number;
+}
+/**
+ * DSPy example format
+ */
+export interface DSPyExample {
+    input: string;
+    output: string;
+    quality?: number;
+}
+/**
+ * Main trainer class integrating dspy.ts with agentic-synth
+ */
+export declare class DSPyAgenticSynthTrainer extends EventEmitter {
+    private config;
+    private languageModels;
+    private chainOfThought?;
+    private optimizer?;
+    private trainingExamples;
+    private currentIteration;
+    private bestScore;
+    private optimizedPrompt;
+    constructor(config: DSPyTrainerConfig);
+    /**
+     * Initialize DSPy.ts language models and modules
+     */
+    initialize(): Promise<void>;
+    /**
+     * Train with optimization using DSPy.ts
+     */
+    trainWithOptimization(schema: Record<string, any>, examples: DSPyExample[]): Promise<TrainingResult>;
+    /**
+     * Generate optimized data using trained models
+     */
+    generateOptimizedData(count: number, schema?: Record<string, any>): Promise<any[]>;
+    /**
+     * Evaluate data quality using DSPy.ts metrics
+     */
+    evaluateQuality(data: any[]): Promise<QualityMetrics>;
+    /**
+     * Run a single training iteration
+     */
+    private runIteration;
+    /**
+     * Generate a batch of data samples
+     */
+    private generateBatch;
+    /**
+     * Assess data quality for a single item
+     */
+    private assessDataQuality;
+    /**
+     * Build generation prompt
+     */
+    private buildGenerationPrompt;
+    /**
+     * Parse generated data from model response
+     */
+    private parseGeneratedData;
+    /**
+     * Filter successful examples above quality threshold
+     */
+    private filterSuccessfulExamples;
+    /**
+     * Update training examples with new results
+     */
+    private updateTrainingExamples;
+    /**
+     * Create metric function for DSPy optimizer
+     */
+    private createMetricFunction;
+    /**
+     * Convert training examples to DSPy format
+     */
+    private convertToDSPyExamples;
+    /**
+     * Calculate simple similarity between two strings
+     */
+    private calculateSimilarity;
+    /**
+     * Calculate edit distance between strings
+     */
+    private editDistance;
+    /**
+     * Final evaluation across all iterations
+     */
+    private evaluateFinal;
+    /**
+     * Calculate average of numbers
+     */
+    private calculateAverage;
+    /**
+     * Calculate diversity score
+     */
+    private calculateDiversity;
+    /**
+     * Get training statistics
+     */
+    getStatistics(): {
+        totalIterations: number;
+        bestScore: number;
+        trainingExamples: number;
+    };
+}
+//# sourceMappingURL=dspy-real-integration.d.ts.map