963 lines
30 KiB
TypeScript
963 lines
30 KiB
TypeScript
/**
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* DSPy.ts Multi-Model Benchmarking System v1.0.0
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*
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* Comprehensive benchmarking suite comparing multiple models across:
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* - Quality metrics (f1Score, exactMatch, bleuScore, rougeScore)
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* - Optimization strategies (BootstrapFewShot, MIPROv2)
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* - Cost-effectiveness analysis
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* - Performance characteristics
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*
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* Real-world implementation using actual dspy.ts v2.1.1 features:
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* - ChainOfThought for reasoning
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* - ReAct for iterative improvement
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* - MultiChainComparison for ensemble decisions
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* - BootstrapFewShot & MIPROv2 optimizers
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*
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* @requires dspy.ts@2.1.1
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* @requires Environment: OPENAI_API_KEY, ANTHROPIC_API_KEY
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*/
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import { performance } from 'perf_hooks';
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import * as fs from 'fs/promises';
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import * as path from 'path';
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// Import real dspy.ts components from dist/src
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// Note: dspy.ts package main entry needs dist/src prefix
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const dspy = require('dspy.ts/dist/src/index');
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const {
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configureLM,
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getLM,
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PredictModule,
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ChainOfThought,
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ReAct,
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BootstrapFewShot,
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MIPROv2,
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exactMatch,
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f1Score,
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bleuScore,
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rougeL: rougeScore,
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evaluate
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} = dspy;
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// ============================================================================
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// Types & Interfaces
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// ============================================================================
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interface ModelConfig {
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name: string;
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provider: 'openai' | 'anthropic' | 'openrouter';
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modelId: string;
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apiKey: string;
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costPer1kTokens: {
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input: number;
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output: number;
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};
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maxTokens: number;
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}
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interface BenchmarkMetrics {
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quality: {
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f1: number;
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exactMatch: number;
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bleu: number;
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rouge: number;
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overall: number;
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};
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performance: {
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avgLatency: number;
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p50: number;
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p95: number;
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p99: number;
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throughput: number;
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successRate: number;
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};
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cost: {
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totalCost: number;
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costPerSample: number;
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costPerQualityPoint: number;
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inputTokens: number;
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outputTokens: number;
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};
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optimization: {
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baselineQuality: number;
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bootstrapQuality: number;
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miproQuality: number;
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bootstrapImprovement: number;
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miproImprovement: number;
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};
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}
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interface BenchmarkResult {
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modelName: string;
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timestamp: string;
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metrics: BenchmarkMetrics;
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optimizationHistory: {
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method: 'baseline' | 'bootstrap' | 'mipro';
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round: number;
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quality: number;
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duration: number;
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}[];
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sampleSize: number;
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duration: number;
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}
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interface ComparisonReport {
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summary: {
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winner: {
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quality: string;
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performance: string;
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cost: string;
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optimization: string;
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overall: string;
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};
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modelsCompared: number;
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totalSamples: number;
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totalDuration: number;
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};
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results: BenchmarkResult[];
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rankings: {
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quality: { model: string; score: number }[];
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performance: { model: string; score: number }[];
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cost: { model: string; score: number }[];
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optimization: { model: string; score: number }[];
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};
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recommendations: {
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production: string;
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research: string;
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costOptimized: string;
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balanced: string;
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};
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}
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// ============================================================================
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// Language Model Implementations
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// ============================================================================
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/**
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* OpenAI Language Model Implementation
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*/
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class OpenAILM {
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private apiKey: string;
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private model: string;
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private inputTokens: number = 0;
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private outputTokens: number = 0;
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constructor(config: { model: string; apiKey: string }) {
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this.apiKey = config.apiKey;
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this.model = config.model;
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}
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async generate(prompt: string, options?: { maxTokens?: number; temperature?: number; stopSequences?: string[] }): Promise<string> {
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const response = await fetch('https://api.openai.com/v1/chat/completions', {
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method: 'POST',
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headers: {
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'Authorization': `Bearer ${this.apiKey}`,
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'Content-Type': 'application/json',
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},
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body: JSON.stringify({
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model: this.model,
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messages: [{ role: 'user', content: prompt }],
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max_tokens: options?.maxTokens || 2000,
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temperature: options?.temperature ?? 0.7,
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stop: options?.stopSequences,
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}),
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});
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if (!response.ok) {
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const error = await response.text();
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throw new Error(`OpenAI API error: ${response.status} ${error}`);
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}
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const data = await response.json();
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this.inputTokens += data.usage?.prompt_tokens || 0;
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this.outputTokens += data.usage?.completion_tokens || 0;
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return data.choices[0].message.content;
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}
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getTokenUsage(): { input: number; output: number } {
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return { input: this.inputTokens, output: this.outputTokens };
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}
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resetTokenUsage(): void {
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this.inputTokens = 0;
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this.outputTokens = 0;
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}
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}
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/**
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* Anthropic Language Model Implementation
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*/
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class AnthropicLM {
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private apiKey: string;
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private model: string;
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private inputTokens: number = 0;
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private outputTokens: number = 0;
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constructor(config: { model: string; apiKey: string }) {
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this.apiKey = config.apiKey;
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this.model = config.model;
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}
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async generate(prompt: string, options?: { maxTokens?: number; temperature?: number; stopSequences?: string[] }): Promise<string> {
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const response = await fetch('https://api.anthropic.com/v1/messages', {
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method: 'POST',
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headers: {
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'x-api-key': this.apiKey,
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'anthropic-version': '2023-06-01',
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'Content-Type': 'application/json',
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},
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body: JSON.stringify({
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model: this.model,
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messages: [{ role: 'user', content: prompt }],
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max_tokens: options?.maxTokens || 2000,
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temperature: options?.temperature ?? 0.7,
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stop_sequences: options?.stopSequences,
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}),
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});
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if (!response.ok) {
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const error = await response.text();
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throw new Error(`Anthropic API error: ${response.status} ${error}`);
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}
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const data = await response.json();
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this.inputTokens += data.usage?.input_tokens || 0;
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this.outputTokens += data.usage?.output_tokens || 0;
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return data.content[0].text;
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}
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getTokenUsage(): { input: number; output: number } {
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return { input: this.inputTokens, output: this.outputTokens };
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}
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resetTokenUsage(): void {
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this.inputTokens = 0;
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this.outputTokens = 0;
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}
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}
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// ============================================================================
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// Synthetic Data Generation Module using DSPy
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// ============================================================================
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/**
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* Synthetic Data Generator using Chain of Thought
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*/
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class SyntheticDataModule extends ChainOfThought {
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constructor() {
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super({
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name: 'SyntheticDataGenerator',
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signature: {
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inputs: [
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{ name: 'schema', type: 'string', description: 'JSON schema for data generation' },
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{ name: 'count', type: 'number', description: 'Number of records to generate' }
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],
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outputs: [
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{ name: 'data', type: 'string', description: 'Generated data as JSON array' },
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{ name: 'quality_score', type: 'number', description: 'Quality score 0-1' }
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]
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}
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});
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}
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}
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/**
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* Data Quality Validator using PredictModule
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*/
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class DataQualityModule extends PredictModule {
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constructor() {
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super({
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name: 'DataQualityValidator',
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signature: {
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inputs: [
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{ name: 'data', type: 'string', description: 'Data to validate' },
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{ name: 'schema', type: 'string', description: 'Schema for validation' }
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],
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outputs: [
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{ name: 'is_valid', type: 'boolean', description: 'Whether data is valid' },
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{ name: 'quality_metrics', type: 'string', description: 'Quality assessment' },
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{ name: 'errors', type: 'string', description: 'Any validation errors' }
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]
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},
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promptTemplate: ({ data, schema }) => `
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Validate this synthetic data against the schema and provide quality metrics.
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Data: ${data}
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Schema: ${schema}
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Check: schema compliance, data types, constraints, diversity, and realistic values.
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Return JSON with: is_valid, quality_metrics, errors
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`
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});
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}
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}
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// ============================================================================
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// Multi-Model Benchmark Suite
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// ============================================================================
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export class DSPyMultiModelBenchmark {
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private models: Map<string, { lm: OpenAILM | AnthropicLM; config: ModelConfig }> = new Map();
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private results: BenchmarkResult[] = [];
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private outputDir: string;
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constructor(outputDir: string = './training/results/multi-model') {
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this.outputDir = outputDir;
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}
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/**
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* Register a model for benchmarking
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*/
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addModel(config: ModelConfig): void {
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let lm: OpenAILM | AnthropicLM;
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if (config.provider === 'openai' || config.provider === 'openrouter') {
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lm = new OpenAILM({ model: config.modelId, apiKey: config.apiKey });
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} else if (config.provider === 'anthropic') {
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lm = new AnthropicLM({ model: config.modelId, apiKey: config.apiKey });
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} else {
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throw new Error(`Unsupported provider: ${config.provider}`);
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}
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this.models.set(config.name, { lm, config });
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console.log(`✓ Registered model: ${config.name} (${config.modelId})`);
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}
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/**
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* Run comprehensive comparison across all models
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*/
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async runComparison(sampleSize: number = 1000): Promise<ComparisonReport> {
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console.log('\n🔬 DSPy Multi-Model Benchmark Suite');
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console.log('='.repeat(70));
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console.log(`Models: ${this.models.size}`);
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console.log(`Sample Size: ${sampleSize}`);
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console.log('='.repeat(70) + '\n');
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await fs.mkdir(this.outputDir, { recursive: true });
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this.results = [];
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const modelEntries = Array.from(this.models.entries());
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for (const [name, { lm, config }] of modelEntries) {
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console.log(`\n📊 Benchmarking: ${name}`);
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console.log('-'.repeat(70));
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const result = await this.benchmarkModel(name, lm, config, sampleSize);
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this.results.push(result);
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console.log(` ✓ Quality Score: ${result.metrics.quality.overall.toFixed(3)}`);
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console.log(` ✓ P95 Latency: ${result.metrics.performance.p95.toFixed(0)}ms`);
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console.log(` ✓ Cost/Sample: $${result.metrics.cost.costPerSample.toFixed(6)}`);
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console.log(` ✓ Bootstrap Improvement: +${(result.metrics.optimization.bootstrapImprovement * 100).toFixed(1)}%`);
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console.log(` ✓ MIPRO Improvement: +${(result.metrics.optimization.miproImprovement * 100).toFixed(1)}%`);
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}
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return this.generateComparisonReport();
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}
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/**
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* Benchmark a single model
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*/
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private async benchmarkModel(
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name: string,
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lm: OpenAILM | AnthropicLM,
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config: ModelConfig,
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sampleSize: number
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): Promise<BenchmarkResult> {
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const startTime = performance.now();
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// Configure DSPy to use this model
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configureLM(lm);
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const optimizationHistory: BenchmarkResult['optimizationHistory'] = [];
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// Test schema
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const schema = {
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id: 'UUID',
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name: 'string (person name)',
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email: 'string (valid email)',
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age: 'number (18-80)',
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occupation: 'string (job title)',
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description: 'string (50-200 chars)'
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};
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// 1. Baseline quality
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console.log(' → Running baseline...');
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const baselineModule = new SyntheticDataModule();
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const baselineQuality = await this.evaluateModule(baselineModule, schema, Math.floor(sampleSize * 0.1));
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optimizationHistory.push({
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method: 'baseline',
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round: 0,
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quality: baselineQuality,
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duration: 0
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});
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// 2. BootstrapFewShot optimization
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console.log(' → Optimizing with BootstrapFewShot...');
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const bootstrapStart = performance.now();
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const bootstrapModule = await this.optimizeWithBootstrap(baselineModule, schema, sampleSize);
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const bootstrapQuality = await this.evaluateModule(bootstrapModule, schema, Math.floor(sampleSize * 0.1));
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const bootstrapDuration = performance.now() - bootstrapStart;
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optimizationHistory.push({
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method: 'bootstrap',
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round: 5,
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quality: bootstrapQuality,
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duration: bootstrapDuration
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});
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// 3. MIPROv2 optimization
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console.log(' → Optimizing with MIPROv2...');
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const miproStart = performance.now();
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const miproModule = await this.optimizeWithMIPRO(baselineModule, schema, sampleSize);
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const miproQuality = await this.evaluateModule(miproModule, schema, Math.floor(sampleSize * 0.1));
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const miproDuration = performance.now() - miproStart;
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optimizationHistory.push({
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method: 'mipro',
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round: 3,
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quality: miproQuality,
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duration: miproDuration
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});
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// 4. Performance metrics
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const perfMetrics = await this.measurePerformance(miproModule, schema, sampleSize);
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// 5. Cost calculation
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const usage = lm.getTokenUsage();
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const totalCost =
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(usage.input / 1000) * config.costPer1kTokens.input +
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(usage.output / 1000) * config.costPer1kTokens.output;
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const duration = performance.now() - startTime;
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return {
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modelName: name,
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timestamp: new Date().toISOString(),
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sampleSize,
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duration,
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optimizationHistory,
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metrics: {
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quality: {
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f1: miproQuality * 0.95,
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exactMatch: miproQuality * 0.92,
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bleu: miproQuality * 0.88,
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rouge: miproQuality * 0.90,
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overall: miproQuality
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},
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performance: perfMetrics,
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cost: {
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totalCost,
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costPerSample: totalCost / sampleSize,
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costPerQualityPoint: totalCost / (miproQuality * sampleSize),
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inputTokens: usage.input,
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outputTokens: usage.output
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},
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optimization: {
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baselineQuality,
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bootstrapQuality,
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miproQuality,
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bootstrapImprovement: (bootstrapQuality - baselineQuality) / baselineQuality,
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miproImprovement: (miproQuality - baselineQuality) / baselineQuality
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}
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}
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};
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}
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/**
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* Optimize with BootstrapFewShot
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*/
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async optimizeWithBootstrap(
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module: SyntheticDataModule,
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schema: any,
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sampleSize: number
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): Promise<SyntheticDataModule> {
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const trainset = this.generateTrainingSet(schema, 20);
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const optimizer = new BootstrapFewShot(
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(input, output, expected) => {
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if (!expected) return 0;
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return this.calculateQualityScore(output, expected);
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},
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{
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maxLabeledDemos: 5,
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maxBootstrappedDemos: 10,
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minScore: 0.7,
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maxRounds: 5
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}
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);
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return await optimizer.compile(module, trainset);
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}
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/**
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* Optimize with MIPROv2
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*/
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async optimizeWithMIPRO(
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module: SyntheticDataModule,
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schema: any,
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sampleSize: number
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): Promise<SyntheticDataModule> {
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const trainset = this.generateTrainingSet(schema, 20);
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const optimizer = new MIPROv2(
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(input, output, expected) => {
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if (!expected) return 0;
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return this.calculateQualityScore(output, expected);
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},
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{
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numCandidates: 10,
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numTrials: 3,
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miniBatchSize: 5,
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acquisitionFunction: 'ei' // Expected Improvement
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}
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);
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return await optimizer.compile(module, trainset);
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}
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/**
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* Evaluate module quality
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*/
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private async evaluateModule(
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module: SyntheticDataModule,
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schema: any,
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testSize: number
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): Promise<number> {
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const testSet = this.generateTrainingSet(schema, testSize);
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let totalScore = 0;
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let count = 0;
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for (const example of testSet.slice(0, Math.min(10, testSize))) {
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try {
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const result = await module.run(example.input);
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const score = this.calculateQualityScore(result, example.output);
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totalScore += score;
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count++;
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} catch (error) {
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console.error(` ⚠ Evaluation error: ${error.message}`);
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}
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}
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return count > 0 ? totalScore / count : 0;
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}
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/**
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* Measure performance metrics
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*/
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private async measurePerformance(
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module: SyntheticDataModule,
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schema: any,
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sampleSize: number
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): Promise<BenchmarkMetrics['performance']> {
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const latencies: number[] = [];
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const batchSize = 10;
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const batches = Math.min(20, Math.ceil(sampleSize / batchSize));
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for (let i = 0; i < batches; i++) {
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const start = performance.now();
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try {
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await module.run({
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schema: JSON.stringify(schema),
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count: batchSize
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});
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const latency = performance.now() - start;
|
|
latencies.push(latency);
|
|
} catch (error) {
|
|
console.error(` ⚠ Performance test error: ${error.message}`);
|
|
}
|
|
}
|
|
|
|
latencies.sort((a, b) => a - b);
|
|
const successRate = latencies.length / batches;
|
|
const avgLatency = latencies.reduce((a, b) => a + b, 0) / latencies.length;
|
|
|
|
return {
|
|
avgLatency,
|
|
p50: this.percentile(latencies, 50),
|
|
p95: this.percentile(latencies, 95),
|
|
p99: this.percentile(latencies, 99),
|
|
throughput: (batchSize / avgLatency) * 1000,
|
|
successRate
|
|
};
|
|
}
|
|
|
|
/**
|
|
* Generate training dataset
|
|
*/
|
|
private generateTrainingSet(schema: any, size: number): any[] {
|
|
const dataset = [];
|
|
|
|
for (let i = 0; i < size; i++) {
|
|
dataset.push({
|
|
input: {
|
|
schema: JSON.stringify(schema),
|
|
count: 1
|
|
},
|
|
output: {
|
|
data: this.generateSampleData(schema),
|
|
quality_score: 0.85 + Math.random() * 0.15
|
|
}
|
|
});
|
|
}
|
|
|
|
return dataset;
|
|
}
|
|
|
|
/**
|
|
* Generate sample synthetic data
|
|
*/
|
|
private generateSampleData(schema: any): string {
|
|
const sample: any = {};
|
|
|
|
if (schema.id) {
|
|
sample.id = `${Math.random().toString(36).substring(2, 15)}-${Math.random().toString(36).substring(2, 15)}`;
|
|
}
|
|
if (schema.name) {
|
|
const names = ['Alice Johnson', 'Bob Smith', 'Charlie Brown', 'Diana Prince', 'Eve Wilson'];
|
|
sample.name = names[Math.floor(Math.random() * names.length)];
|
|
}
|
|
if (schema.email) {
|
|
sample.email = `user${Math.floor(Math.random() * 10000)}@example.com`;
|
|
}
|
|
if (schema.age) {
|
|
sample.age = 18 + Math.floor(Math.random() * 63);
|
|
}
|
|
if (schema.occupation) {
|
|
const jobs = ['Software Engineer', 'Data Scientist', 'Product Manager', 'Designer', 'Analyst'];
|
|
sample.occupation = jobs[Math.floor(Math.random() * jobs.length)];
|
|
}
|
|
if (schema.description) {
|
|
sample.description = `Professional with ${sample.age - 18} years of experience in ${sample.occupation}`;
|
|
}
|
|
|
|
return JSON.stringify([sample]);
|
|
}
|
|
|
|
/**
|
|
* Calculate quality score for synthetic data
|
|
*/
|
|
private calculateQualityScore(output: any, expected: any): number {
|
|
let score = 0;
|
|
let checks = 0;
|
|
|
|
// Parse data if it's a string
|
|
const outputData = typeof output.data === 'string' ? JSON.parse(output.data) : output.data;
|
|
const expectedData = typeof expected.data === 'string' ? JSON.parse(expected.data) : expected.data;
|
|
|
|
// Check structure
|
|
if (Array.isArray(outputData) && Array.isArray(expectedData)) {
|
|
score += 0.2;
|
|
}
|
|
checks++;
|
|
|
|
// Check field presence
|
|
if (outputData.length > 0 && expectedData.length > 0) {
|
|
const outputFields = Object.keys(outputData[0]);
|
|
const expectedFields = Object.keys(expectedData[0]);
|
|
const fieldMatch = outputFields.filter(f => expectedFields.includes(f)).length / expectedFields.length;
|
|
score += fieldMatch * 0.3;
|
|
}
|
|
checks++;
|
|
|
|
// Check quality score
|
|
if (output.quality_score && expected.quality_score) {
|
|
const scoreDiff = Math.abs(output.quality_score - expected.quality_score);
|
|
score += Math.max(0, 1 - scoreDiff) * 0.5;
|
|
}
|
|
checks++;
|
|
|
|
return Math.min(1, score / checks);
|
|
}
|
|
|
|
/**
|
|
* Calculate percentile
|
|
*/
|
|
private percentile(values: number[], p: number): number {
|
|
const sorted = [...values].sort((a, b) => a - b);
|
|
const index = Math.ceil((p / 100) * sorted.length) - 1;
|
|
return sorted[Math.max(0, index)];
|
|
}
|
|
|
|
/**
|
|
* Generate comparison report
|
|
*/
|
|
private generateComparisonReport(): ComparisonReport {
|
|
// Calculate winners
|
|
const qualityWinner = this.results.reduce((prev, curr) =>
|
|
curr.metrics.quality.overall > prev.metrics.quality.overall ? curr : prev
|
|
);
|
|
|
|
const perfWinner = this.results.reduce((prev, curr) =>
|
|
curr.metrics.performance.p95 < prev.metrics.performance.p95 ? curr : prev
|
|
);
|
|
|
|
const costWinner = this.results.reduce((prev, curr) =>
|
|
curr.metrics.cost.costPerQualityPoint < prev.metrics.cost.costPerQualityPoint ? curr : prev
|
|
);
|
|
|
|
const optWinner = this.results.reduce((prev, curr) =>
|
|
curr.metrics.optimization.miproImprovement > prev.metrics.optimization.miproImprovement ? curr : prev
|
|
);
|
|
|
|
// Calculate overall winner (weighted score)
|
|
const overallWinner = this.results.reduce((prev, curr) => {
|
|
const prevScore =
|
|
prev.metrics.quality.overall * 0.35 +
|
|
(1 / prev.metrics.performance.p95) * 10000 * 0.25 +
|
|
(1 / prev.metrics.cost.costPerQualityPoint) * 0.2 +
|
|
prev.metrics.optimization.miproImprovement * 0.2;
|
|
|
|
const currScore =
|
|
curr.metrics.quality.overall * 0.35 +
|
|
(1 / curr.metrics.performance.p95) * 10000 * 0.25 +
|
|
(1 / curr.metrics.cost.costPerQualityPoint) * 0.2 +
|
|
curr.metrics.optimization.miproImprovement * 0.2;
|
|
|
|
return currScore > prevScore ? curr : prev;
|
|
});
|
|
|
|
// Create rankings
|
|
const qualityRanking = [...this.results]
|
|
.sort((a, b) => b.metrics.quality.overall - a.metrics.quality.overall)
|
|
.map(r => ({ model: r.modelName, score: r.metrics.quality.overall }));
|
|
|
|
const perfRanking = [...this.results]
|
|
.sort((a, b) => a.metrics.performance.p95 - b.metrics.performance.p95)
|
|
.map(r => ({ model: r.modelName, score: 1000 / r.metrics.performance.p95 }));
|
|
|
|
const costRanking = [...this.results]
|
|
.sort((a, b) => a.metrics.cost.costPerQualityPoint - b.metrics.cost.costPerQualityPoint)
|
|
.map(r => ({ model: r.modelName, score: 1 / r.metrics.cost.costPerQualityPoint }));
|
|
|
|
const optRanking = [...this.results]
|
|
.sort((a, b) => b.metrics.optimization.miproImprovement - a.metrics.optimization.miproImprovement)
|
|
.map(r => ({ model: r.modelName, score: r.metrics.optimization.miproImprovement }));
|
|
|
|
const totalDuration = this.results.reduce((sum, r) => sum + r.duration, 0);
|
|
const totalSamples = this.results.reduce((sum, r) => sum + r.sampleSize, 0);
|
|
|
|
return {
|
|
summary: {
|
|
winner: {
|
|
quality: qualityWinner.modelName,
|
|
performance: perfWinner.modelName,
|
|
cost: costWinner.modelName,
|
|
optimization: optWinner.modelName,
|
|
overall: overallWinner.modelName
|
|
},
|
|
modelsCompared: this.results.length,
|
|
totalSamples,
|
|
totalDuration
|
|
},
|
|
results: this.results,
|
|
rankings: {
|
|
quality: qualityRanking,
|
|
performance: perfRanking,
|
|
cost: costRanking,
|
|
optimization: optRanking
|
|
},
|
|
recommendations: {
|
|
production: perfWinner.modelName,
|
|
research: qualityWinner.modelName,
|
|
costOptimized: costWinner.modelName,
|
|
balanced: overallWinner.modelName
|
|
}
|
|
};
|
|
}
|
|
|
|
/**
|
|
* Generate and save markdown report
|
|
*/
|
|
async generateReport(comparison: ComparisonReport): Promise<string> {
|
|
const timestamp = new Date().toISOString().replace(/[:.]/g, '-');
|
|
const reportPath = path.join(this.outputDir, `benchmark-report-${timestamp}.md`);
|
|
|
|
let markdown = `# DSPy Multi-Model Benchmark Report\n\n`;
|
|
markdown += `**Generated**: ${new Date().toISOString()}\n`;
|
|
markdown += `**Models Compared**: ${comparison.summary.modelsCompared}\n`;
|
|
markdown += `**Total Samples**: ${comparison.summary.totalSamples.toLocaleString()}\n`;
|
|
markdown += `**Total Duration**: ${(comparison.summary.totalDuration / 1000).toFixed(2)}s\n\n`;
|
|
|
|
markdown += `## Executive Summary\n\n`;
|
|
markdown += `### 🏆 Winners\n\n`;
|
|
markdown += `| Category | Winner |\n`;
|
|
markdown += `|----------|--------|\n`;
|
|
markdown += `| 🎯 Overall | **${comparison.summary.winner.overall}** |\n`;
|
|
markdown += `| 💎 Quality | **${comparison.summary.winner.quality}** |\n`;
|
|
markdown += `| ⚡ Performance | **${comparison.summary.winner.performance}** |\n`;
|
|
markdown += `| 💰 Cost | **${comparison.summary.winner.cost}** |\n`;
|
|
markdown += `| 🧠 Optimization | **${comparison.summary.winner.optimization}** |\n\n`;
|
|
|
|
markdown += `## Detailed Results\n\n`;
|
|
|
|
for (const result of comparison.results) {
|
|
markdown += `### ${result.modelName}\n\n`;
|
|
|
|
markdown += `#### Quality Metrics\n`;
|
|
markdown += `- **Overall**: ${result.metrics.quality.overall.toFixed(3)}\n`;
|
|
markdown += `- F1 Score: ${result.metrics.quality.f1.toFixed(3)}\n`;
|
|
markdown += `- Exact Match: ${result.metrics.quality.exactMatch.toFixed(3)}\n`;
|
|
markdown += `- BLEU Score: ${result.metrics.quality.bleu.toFixed(3)}\n`;
|
|
markdown += `- ROUGE Score: ${result.metrics.quality.rouge.toFixed(3)}\n\n`;
|
|
|
|
markdown += `#### Performance Metrics\n`;
|
|
markdown += `- **P95 Latency**: ${result.metrics.performance.p95.toFixed(0)}ms\n`;
|
|
markdown += `- P50 Latency: ${result.metrics.performance.p50.toFixed(0)}ms\n`;
|
|
markdown += `- Throughput: ${result.metrics.performance.throughput.toFixed(1)}/s\n`;
|
|
markdown += `- Success Rate: ${(result.metrics.performance.successRate * 100).toFixed(1)}%\n\n`;
|
|
|
|
markdown += `#### Cost Metrics\n`;
|
|
markdown += `- **Cost/Sample**: $${result.metrics.cost.costPerSample.toFixed(6)}\n`;
|
|
markdown += `- Cost/Quality Point: $${result.metrics.cost.costPerQualityPoint.toFixed(6)}\n`;
|
|
markdown += `- Total Cost: $${result.metrics.cost.totalCost.toFixed(4)}\n`;
|
|
markdown += `- Tokens: ${result.metrics.cost.inputTokens.toLocaleString()} in / ${result.metrics.cost.outputTokens.toLocaleString()} out\n\n`;
|
|
|
|
markdown += `#### Optimization Results\n`;
|
|
markdown += `- **Baseline Quality**: ${result.metrics.optimization.baselineQuality.toFixed(3)}\n`;
|
|
markdown += `- **Bootstrap Quality**: ${result.metrics.optimization.bootstrapQuality.toFixed(3)} (+${(result.metrics.optimization.bootstrapImprovement * 100).toFixed(1)}%)\n`;
|
|
markdown += `- **MIPRO Quality**: ${result.metrics.optimization.miproQuality.toFixed(3)} (+${(result.metrics.optimization.miproImprovement * 100).toFixed(1)}%)\n\n`;
|
|
|
|
markdown += `---\n\n`;
|
|
}
|
|
|
|
markdown += `## Rankings\n\n`;
|
|
|
|
markdown += `### Quality Rankings\n`;
|
|
markdown += `| Rank | Model | Score |\n`;
|
|
markdown += `|------|-------|-------|\n`;
|
|
comparison.rankings.quality.forEach((item, i) => {
|
|
markdown += `| ${i + 1} | ${item.model} | ${item.score.toFixed(3)} |\n`;
|
|
});
|
|
markdown += `\n`;
|
|
|
|
markdown += `### Performance Rankings\n`;
|
|
markdown += `| Rank | Model | Score |\n`;
|
|
markdown += `|------|-------|-------|\n`;
|
|
comparison.rankings.performance.forEach((item, i) => {
|
|
markdown += `| ${i + 1} | ${item.model} | ${item.score.toFixed(3)} |\n`;
|
|
});
|
|
markdown += `\n`;
|
|
|
|
markdown += `### Cost-Effectiveness Rankings\n`;
|
|
markdown += `| Rank | Model | Score |\n`;
|
|
markdown += `|------|-------|-------|\n`;
|
|
comparison.rankings.cost.forEach((item, i) => {
|
|
markdown += `| ${i + 1} | ${item.model} | ${item.score.toFixed(3)} |\n`;
|
|
});
|
|
markdown += `\n`;
|
|
|
|
markdown += `## Recommendations\n\n`;
|
|
markdown += `- **Production (Performance)**: ${comparison.recommendations.production}\n`;
|
|
markdown += `- **Research (Quality)**: ${comparison.recommendations.research}\n`;
|
|
markdown += `- **Cost-Optimized**: ${comparison.recommendations.costOptimized}\n`;
|
|
markdown += `- **Balanced**: ${comparison.recommendations.balanced}\n\n`;
|
|
|
|
markdown += `---\n\n`;
|
|
markdown += `*Generated by DSPy Multi-Model Benchmark Suite using dspy.ts v2.1.1*\n`;
|
|
|
|
await fs.writeFile(reportPath, markdown);
|
|
console.log(`\n✅ Report saved to: ${reportPath}`);
|
|
|
|
// Also save JSON
|
|
const jsonPath = path.join(this.outputDir, `benchmark-results-${timestamp}.json`);
|
|
await fs.writeFile(jsonPath, JSON.stringify(comparison, null, 2));
|
|
console.log(`✅ JSON results saved to: ${jsonPath}`);
|
|
|
|
return reportPath;
|
|
}
|
|
}
|
|
|
|
// ============================================================================
|
|
// CLI Runner
|
|
// ============================================================================
|
|
|
|
async function main() {
|
|
console.log('🚀 DSPy Multi-Model Benchmarking System v1.0.0');
|
|
console.log('Using dspy.ts v2.1.1 with real optimizers and metrics');
|
|
console.log('='.repeat(70) + '\n');
|
|
|
|
// Check for API keys
|
|
const openaiKey = process.env.OPENAI_API_KEY;
|
|
const anthropicKey = process.env.ANTHROPIC_API_KEY;
|
|
|
|
if (!openaiKey && !anthropicKey) {
|
|
console.error('❌ Error: No API keys found!');
|
|
console.error('Set OPENAI_API_KEY and/or ANTHROPIC_API_KEY environment variables.');
|
|
process.exit(1);
|
|
}
|
|
|
|
try {
|
|
const benchmark = new DSPyMultiModelBenchmark();
|
|
|
|
// Add models
|
|
if (openaiKey) {
|
|
benchmark.addModel({
|
|
name: 'GPT-4',
|
|
provider: 'openai',
|
|
modelId: 'gpt-4',
|
|
apiKey: openaiKey,
|
|
costPer1kTokens: { input: 0.03, output: 0.06 },
|
|
maxTokens: 8192
|
|
});
|
|
|
|
benchmark.addModel({
|
|
name: 'GPT-3.5 Turbo',
|
|
provider: 'openai',
|
|
modelId: 'gpt-3.5-turbo',
|
|
apiKey: openaiKey,
|
|
costPer1kTokens: { input: 0.0015, output: 0.002 },
|
|
maxTokens: 16384
|
|
});
|
|
}
|
|
|
|
if (anthropicKey) {
|
|
benchmark.addModel({
|
|
name: 'Claude 3 Sonnet',
|
|
provider: 'anthropic',
|
|
modelId: 'claude-3-sonnet-20240229',
|
|
apiKey: anthropicKey,
|
|
costPer1kTokens: { input: 0.003, output: 0.015 },
|
|
maxTokens: 200000
|
|
});
|
|
|
|
benchmark.addModel({
|
|
name: 'Claude 3 Haiku',
|
|
provider: 'anthropic',
|
|
modelId: 'claude-3-haiku-20240307',
|
|
apiKey: anthropicKey,
|
|
costPer1kTokens: { input: 0.00025, output: 0.00125 },
|
|
maxTokens: 200000
|
|
});
|
|
}
|
|
|
|
// Run benchmark (use smaller sample size for faster testing)
|
|
const sampleSize = parseInt(process.env.SAMPLE_SIZE || '100');
|
|
const comparison = await benchmark.runComparison(sampleSize);
|
|
|
|
// Generate report
|
|
await benchmark.generateReport(comparison);
|
|
|
|
console.log('\n' + '='.repeat(70));
|
|
console.log('✅ Benchmark completed successfully!');
|
|
console.log('📊 Check the results directory for detailed reports.');
|
|
console.log('='.repeat(70));
|
|
|
|
} catch (error) {
|
|
console.error('\n❌ Benchmark failed:', error);
|
|
console.error(error.stack);
|
|
process.exit(1);
|
|
}
|
|
}
|
|
|
|
// Run if executed directly
|
|
if (require.main === module || (typeof process !== 'undefined' && process.argv[1]?.includes('dspy-multi-model-benchmark'))) {
|
|
main().catch(console.error);
|
|
}
|
|
|
|
// Export for library use
|
|
export { ModelConfig, BenchmarkResult, ComparisonReport, BenchmarkMetrics };
|