Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'

vendor/ruvector/npm/packages/agentic-synth/examples/dspy-complete-example.d.ts

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+/**
+ * COMPREHENSIVE DSPy.ts + AgenticSynth Integration Example
+ *
+ * E-commerce Product Data Generation with DSPy Optimization
+ *
+ * This example demonstrates:
+ * 1. ✅ Real DSPy.ts (v2.1.1) module usage - ChainOfThought, Predict, Refine
+ * 2. ✅ Integration with AgenticSynth for baseline data generation
+ * 3. ✅ BootstrapFewShot optimizer for learning from high-quality examples
+ * 4. ✅ Quality metrics and comparison (baseline vs optimized)
+ * 5. ✅ Production-ready error handling and progress tracking
+ * 6. ✅ Multiple LM provider support (OpenAI, Anthropic)
+ *
+ * Usage:
+ * ```bash
+ * export OPENAI_API_KEY=sk-...
+ * export GEMINI_API_KEY=...
+ * npx tsx examples/dspy-complete-example.ts
+ * ```
+ *
+ * @author rUv
+ * @license MIT
+ */
+import 'dotenv/config';
+interface Product {
+    id?: string;
+    name: string;
+    category: string;
+    description: string;
+    price: number;
+    rating: number;
+    features?: string[];
+    tags?: string[];
+}
+interface QualityMetrics {
+    completeness: number;
+    coherence: number;
+    persuasiveness: number;
+    seoQuality: number;
+    overall: number;
+}
+interface ComparisonResults {
+    baseline: {
+        products: Product[];
+        avgQuality: number;
+        metrics: QualityMetrics;
+        generationTime: number;
+        cost: number;
+    };
+    optimized: {
+        products: Product[];
+        avgQuality: number;
+        metrics: QualityMetrics;
+        generationTime: number;
+        cost: number;
+    };
+    improvement: {
+        qualityGain: number;
+        speedChange: number;
+        costEfficiency: number;
+    };
+}
+/**
+ * Calculate quality metrics for a product description
+ */
+declare function calculateQualityMetrics(product: Product): QualityMetrics;
+/**
+ * Calculate average quality across multiple products
+ */
+declare function calculateAverageQuality(products: Product[]): {
+    avgQuality: number;
+    metrics: QualityMetrics;
+};
+/**
+ * Generate baseline product data using AgenticSynth (Gemini)
+ */
+declare function generateBaseline(count: number): Promise<{
+    products: Product[];
+    time: number;
+    cost: number;
+}>;
+/**
+ * Create high-quality training examples for DSPy
+ */
+declare function createTrainingExamples(): Array<{
+    category: string;
+    priceRange: string;
+    product: Product;
+}>;
+/**
+ * Setup DSPy with OpenAI and create optimized module
+ */
+declare function setupDSPyOptimization(): Promise<{
+    optimizedModule: any;
+    setupTime: number;
+}>;
+/**
+ * Generate products using optimized DSPy module
+ */
+declare function generateWithDSPy(optimizedModule: any, count: number): Promise<{
+    products: Product[];
+    time: number;
+    cost: number;
+}>;
+/**
+ * Compare baseline vs optimized results
+ */
+declare function compareResults(baselineData: {
+    products: Product[];
+    time: number;
+    cost: number;
+}, optimizedData: {
+    products: Product[];
+    time: number;
+    cost: number;
+}): ComparisonResults;
+export { generateBaseline, setupDSPyOptimization, generateWithDSPy, compareResults, calculateQualityMetrics, calculateAverageQuality, createTrainingExamples };
+//# sourceMappingURL=dspy-complete-example.d.ts.map