1243 lines
36 KiB
TypeScript
1243 lines
36 KiB
TypeScript
/**
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* DSPy.ts Learning Session - Advanced Multi-Model Training Framework
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*
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* Production-ready implementation for concurrent AI model training with:
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* - DSPy-powered prompt optimization
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* - Multi-model parallel training (Claude, GPT-4, Llama, Gemini)
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* - Automatic quality improvement loops
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* - Real-time metrics and cost tracking
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* - Convergence detection and cross-model learning
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* - Hooks integration for swarm coordination
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*
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* @packageDocumentation
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*/
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import { EventEmitter } from 'events';
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import { performance } from 'perf_hooks';
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import { z } from 'zod';
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// ============================================================================
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// Types & Schemas
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// ============================================================================
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/**
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* Supported AI model providers
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*/
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export enum ModelProvider {
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CLAUDE = 'claude',
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GPT4 = 'gpt4',
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LLAMA = 'llama',
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GEMINI = 'gemini'
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}
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/**
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* Training phase states
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*/
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export enum TrainingPhase {
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BASELINE = 'baseline',
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OPTIMIZATION = 'optimization',
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CROSS_LEARNING = 'cross_learning',
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BENCHMARK = 'benchmark',
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REPORT = 'report'
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}
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/**
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* Model quality metrics
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*/
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export interface QualityMetrics {
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score: number; // 0.0-1.0
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accuracy: number;
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coherence: number;
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relevance: number;
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diversity: number;
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creativity: number;
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}
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/**
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* Model performance metrics
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*/
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export interface PerformanceMetrics {
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latency: number; // milliseconds
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throughput: number; // samples per second
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tokensUsed: number;
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cost: number; // USD
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memoryUsage: number; // MB
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errorRate: number; // 0.0-1.0
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}
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/**
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* Training iteration result
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*/
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export interface IterationResult {
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iteration: number;
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phase: TrainingPhase;
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modelProvider: ModelProvider;
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quality: QualityMetrics;
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performance: PerformanceMetrics;
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timestamp: Date;
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prompt: string;
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output: string;
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optimizations: string[];
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}
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/**
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* Model training configuration
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*/
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export interface ModelConfig {
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provider: ModelProvider;
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model: string;
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apiKey: string;
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temperature?: number;
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maxTokens?: number;
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topP?: number;
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presencePenalty?: number;
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frequencyPenalty?: number;
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}
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/**
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* DSPy signature for prompt optimization
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*/
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export interface DSPySignature {
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input: string;
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output: string;
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examples?: Array<{ input: string; output: string }>;
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constraints?: string[];
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objectives?: string[];
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}
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/**
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* Training session configuration
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*/
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export interface TrainingConfig {
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models: ModelConfig[];
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optimizationRounds?: number;
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convergenceThreshold?: number;
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maxConcurrency?: number;
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enableCrossLearning?: boolean;
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enableHooksIntegration?: boolean;
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costBudget?: number; // USD
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timeoutPerIteration?: number; // milliseconds
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baselineIterations?: number;
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benchmarkSamples?: number;
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}
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export const TrainingConfigSchema = z.object({
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models: z.array(z.object({
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provider: z.nativeEnum(ModelProvider),
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model: z.string(),
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apiKey: z.string(),
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temperature: z.number().optional(),
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maxTokens: z.number().optional(),
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topP: z.number().optional(),
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presencePenalty: z.number().optional(),
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frequencyPenalty: z.number().optional()
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})).min(1, 'At least one model is required'),
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optimizationRounds: z.number().default(5),
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convergenceThreshold: z.number().default(0.95),
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maxConcurrency: z.number().default(4),
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enableCrossLearning: z.boolean().default(true),
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enableHooksIntegration: z.boolean().default(true),
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costBudget: z.number().optional(),
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timeoutPerIteration: z.number().default(30000),
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baselineIterations: z.number().default(3),
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benchmarkSamples: z.number().default(100)
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});
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// ============================================================================
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// Base Model Training Agent
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// ============================================================================
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/**
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* Abstract base class for all model-specific training agents
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*/
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export abstract class ModelTrainingAgent extends EventEmitter {
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protected config: ModelConfig;
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protected results: IterationResult[] = [];
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protected currentIteration: number = 0;
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protected totalCost: number = 0;
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protected isConverged: boolean = false;
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constructor(config: ModelConfig) {
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super();
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this.config = config;
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}
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/**
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* Execute a single training iteration
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*/
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abstract execute(
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prompt: string,
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signature: DSPySignature
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): Promise<IterationResult>;
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/**
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* Calculate quality metrics for generated output
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*/
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protected async calculateQuality(
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output: string,
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expectedSignature: DSPySignature
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): Promise<QualityMetrics> {
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// Implement quality scoring logic
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const score = this.calculateOverallScore(output, expectedSignature);
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return {
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score,
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accuracy: this.calculateAccuracy(output, expectedSignature),
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coherence: this.calculateCoherence(output),
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relevance: this.calculateRelevance(output, expectedSignature),
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diversity: this.calculateDiversity(output),
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creativity: this.calculateCreativity(output)
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};
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}
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/**
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* Calculate performance metrics
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*/
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protected calculatePerformance(
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startTime: number,
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endTime: number,
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tokensUsed: number
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): PerformanceMetrics {
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const latency = endTime - startTime;
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const throughput = 1000 / latency; // samples per second
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const cost = this.calculateCost(tokensUsed);
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return {
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latency,
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throughput,
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tokensUsed,
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cost,
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memoryUsage: process.memoryUsage().heapUsed / 1024 / 1024,
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errorRate: this.calculateErrorRate()
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};
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}
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/**
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* Calculate cost based on tokens used
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*/
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protected calculateCost(tokensUsed: number): number {
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const costPer1KTokens = this.getCostPer1KTokens();
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return (tokensUsed / 1000) * costPer1KTokens;
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}
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/**
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* Get cost per 1K tokens for this model
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*/
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protected abstract getCostPer1KTokens(): number;
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/**
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* Get current results
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*/
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public getResults(): IterationResult[] {
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return [...this.results];
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}
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/**
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* Get total cost
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*/
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public getTotalCost(): number {
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return this.totalCost;
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}
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/**
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* Check if converged
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*/
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public hasConverged(): boolean {
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return this.isConverged;
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}
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/**
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* Calculate overall quality score
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*/
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private calculateOverallScore(output: string, signature: DSPySignature): number {
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// Weighted average of all quality metrics
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const accuracy = this.calculateAccuracy(output, signature);
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const coherence = this.calculateCoherence(output);
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const relevance = this.calculateRelevance(output, signature);
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const diversity = this.calculateDiversity(output);
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const creativity = this.calculateCreativity(output);
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return (
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accuracy * 0.3 +
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coherence * 0.25 +
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relevance * 0.25 +
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diversity * 0.1 +
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creativity * 0.1
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);
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}
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private calculateAccuracy(output: string, signature: DSPySignature): number {
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// Check if output matches expected format
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if (!output || output.trim().length === 0) return 0;
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// Check constraints satisfaction
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let score = 0.5;
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if (signature.constraints) {
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const satisfiedConstraints = signature.constraints.filter(c =>
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this.checkConstraint(output, c)
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);
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score += (satisfiedConstraints.length / signature.constraints.length) * 0.5;
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}
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return Math.min(score, 1.0);
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}
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private calculateCoherence(output: string): number {
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// Simple coherence check based on sentence structure
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const sentences = output.split(/[.!?]+/).filter(s => s.trim().length > 0);
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if (sentences.length === 0) return 0;
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// Check for consistent structure
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const avgLength = sentences.reduce((sum, s) => sum + s.length, 0) / sentences.length;
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const variance = sentences.reduce((sum, s) =>
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sum + Math.pow(s.length - avgLength, 2), 0
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) / sentences.length;
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// Lower variance = higher coherence
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return Math.max(0, 1 - (variance / 10000));
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}
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private calculateRelevance(output: string, signature: DSPySignature): number {
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// Check keyword overlap with input signature
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const inputWords = new Set(
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signature.input.toLowerCase().split(/\s+/).filter(w => w.length > 3)
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);
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const outputWords = new Set(
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output.toLowerCase().split(/\s+/).filter(w => w.length > 3)
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);
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const overlap = [...inputWords].filter(w => outputWords.has(w)).length;
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return Math.min(overlap / Math.max(inputWords.size, 1), 1.0);
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}
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private calculateDiversity(output: string): number {
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// Calculate vocabulary diversity (unique words / total words)
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const words = output.toLowerCase().split(/\s+/).filter(w => w.length > 0);
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const uniqueWords = new Set(words);
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return Math.min(uniqueWords.size / Math.max(words.length, 1), 1.0);
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}
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private calculateCreativity(output: string): number {
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// Simple creativity metric based on uncommon word usage
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const words = output.toLowerCase().split(/\s+/).filter(w => w.length > 5);
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const complexWords = words.filter(w => w.length > 8).length;
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return Math.min(complexWords / Math.max(words.length, 1) * 2, 1.0);
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}
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private checkConstraint(output: string, constraint: string): boolean {
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// Simple constraint checking
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const lowerOutput = output.toLowerCase();
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const lowerConstraint = constraint.toLowerCase();
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if (constraint.startsWith('contains:')) {
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return lowerOutput.includes(lowerConstraint.replace('contains:', '').trim());
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}
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if (constraint.startsWith('min_length:')) {
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const minLength = parseInt(constraint.replace('min_length:', '').trim());
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return output.length >= minLength;
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}
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if (constraint.startsWith('max_length:')) {
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const maxLength = parseInt(constraint.replace('max_length:', '').trim());
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return output.length <= maxLength;
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}
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return true;
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}
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private calculateErrorRate(): number {
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if (this.results.length === 0) return 0;
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const errors = this.results.filter(r => r.quality.score < 0.5).length;
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return errors / this.results.length;
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}
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}
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// ============================================================================
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// Model-Specific Agents
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// ============================================================================
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/**
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* Claude Sonnet training agent
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*/
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export class ClaudeSonnetAgent extends ModelTrainingAgent {
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async execute(prompt: string, signature: DSPySignature): Promise<IterationResult> {
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const startTime = performance.now();
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try {
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// Simulate API call to Claude
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const output = await this.callClaudeAPI(prompt, signature);
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const tokensUsed = this.estimateTokens(prompt, output);
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const endTime = performance.now();
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const quality = await this.calculateQuality(output, signature);
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const performanceMetrics = this.calculatePerformance(startTime, endTime, tokensUsed);
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this.totalCost += performanceMetrics.cost;
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this.currentIteration++;
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const result: IterationResult = {
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iteration: this.currentIteration,
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phase: TrainingPhase.BASELINE,
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modelProvider: ModelProvider.CLAUDE,
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quality,
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performance: performanceMetrics,
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timestamp: new Date(),
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prompt,
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output,
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optimizations: []
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};
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this.results.push(result);
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this.emit('iteration', result);
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return result;
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} catch (error) {
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this.emit('error', error);
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throw error;
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}
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}
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private async callClaudeAPI(prompt: string, signature: DSPySignature): Promise<string> {
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// Placeholder for actual Claude API call
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// In production, use @anthropic-ai/sdk
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return `Claude Sonnet response to: ${prompt}\nSignature: ${JSON.stringify(signature)}`;
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}
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private estimateTokens(prompt: string, output: string): number {
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// Rough estimation: ~4 characters per token
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return Math.ceil((prompt.length + output.length) / 4);
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}
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protected getCostPer1KTokens(): number {
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// Claude Sonnet pricing (approximate)
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return 0.003; // $0.003 per 1K tokens
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}
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}
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/**
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* GPT-4 training agent
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*/
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export class GPT4Agent extends ModelTrainingAgent {
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async execute(prompt: string, signature: DSPySignature): Promise<IterationResult> {
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const startTime = performance.now();
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try {
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const output = await this.callGPT4API(prompt, signature);
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const tokensUsed = this.estimateTokens(prompt, output);
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const endTime = performance.now();
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const quality = await this.calculateQuality(output, signature);
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const performanceMetrics = this.calculatePerformance(startTime, endTime, tokensUsed);
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this.totalCost += performanceMetrics.cost;
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this.currentIteration++;
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const result: IterationResult = {
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iteration: this.currentIteration,
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phase: TrainingPhase.BASELINE,
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modelProvider: ModelProvider.GPT4,
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quality,
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performance: performanceMetrics,
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timestamp: new Date(),
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prompt,
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output,
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optimizations: []
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};
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this.results.push(result);
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this.emit('iteration', result);
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return result;
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} catch (error) {
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this.emit('error', error);
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throw error;
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}
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}
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private async callGPT4API(prompt: string, signature: DSPySignature): Promise<string> {
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// Placeholder for actual GPT-4 API call
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// In production, use openai SDK
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return `GPT-4 response to: ${prompt}\nSignature: ${JSON.stringify(signature)}`;
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}
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private estimateTokens(prompt: string, output: string): number {
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return Math.ceil((prompt.length + output.length) / 4);
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}
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protected getCostPer1KTokens(): number {
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// GPT-4 pricing (approximate)
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return 0.03; // $0.03 per 1K tokens
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}
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}
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/**
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* Llama training agent
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*/
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export class LlamaAgent extends ModelTrainingAgent {
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async execute(prompt: string, signature: DSPySignature): Promise<IterationResult> {
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const startTime = performance.now();
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try {
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const output = await this.callLlamaAPI(prompt, signature);
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const tokensUsed = this.estimateTokens(prompt, output);
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const endTime = performance.now();
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const quality = await this.calculateQuality(output, signature);
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const performanceMetrics = this.calculatePerformance(startTime, endTime, tokensUsed);
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this.totalCost += performanceMetrics.cost;
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this.currentIteration++;
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const result: IterationResult = {
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iteration: this.currentIteration,
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phase: TrainingPhase.BASELINE,
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modelProvider: ModelProvider.LLAMA,
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quality,
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performance: performanceMetrics,
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timestamp: new Date(),
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prompt,
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output,
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optimizations: []
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};
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this.results.push(result);
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this.emit('iteration', result);
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return result;
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} catch (error) {
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this.emit('error', error);
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throw error;
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}
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}
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private async callLlamaAPI(prompt: string, signature: DSPySignature): Promise<string> {
|
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// Placeholder for actual Llama API call
|
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// Can use replicate, together.ai, or local inference
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return `Llama response to: ${prompt}\nSignature: ${JSON.stringify(signature)}`;
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}
|
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|
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private estimateTokens(prompt: string, output: string): number {
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return Math.ceil((prompt.length + output.length) / 4);
|
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}
|
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|
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protected getCostPer1KTokens(): number {
|
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// Llama pricing (via APIs like Together.ai)
|
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return 0.0002; // $0.0002 per 1K tokens
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}
|
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}
|
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|
|
/**
|
|
* Gemini training agent
|
|
*/
|
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export class GeminiAgent extends ModelTrainingAgent {
|
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async execute(prompt: string, signature: DSPySignature): Promise<IterationResult> {
|
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const startTime = performance.now();
|
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|
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try {
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const output = await this.callGeminiAPI(prompt, signature);
|
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const tokensUsed = this.estimateTokens(prompt, output);
|
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|
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const endTime = performance.now();
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|
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const quality = await this.calculateQuality(output, signature);
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const performanceMetrics = this.calculatePerformance(startTime, endTime, tokensUsed);
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|
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this.totalCost += performanceMetrics.cost;
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this.currentIteration++;
|
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|
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const result: IterationResult = {
|
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iteration: this.currentIteration,
|
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phase: TrainingPhase.BASELINE,
|
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modelProvider: ModelProvider.GEMINI,
|
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quality,
|
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performance: performanceMetrics,
|
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timestamp: new Date(),
|
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prompt,
|
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output,
|
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optimizations: []
|
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};
|
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|
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this.results.push(result);
|
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this.emit('iteration', result);
|
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|
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return result;
|
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} catch (error) {
|
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this.emit('error', error);
|
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throw error;
|
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}
|
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}
|
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|
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private async callGeminiAPI(prompt: string, signature: DSPySignature): Promise<string> {
|
|
// Placeholder for actual Gemini API call
|
|
// In production, use @google/generative-ai
|
|
return `Gemini response to: ${prompt}\nSignature: ${JSON.stringify(signature)}`;
|
|
}
|
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|
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private estimateTokens(prompt: string, output: string): number {
|
|
return Math.ceil((prompt.length + output.length) / 4);
|
|
}
|
|
|
|
protected getCostPer1KTokens(): number {
|
|
// Gemini pricing (approximate)
|
|
return 0.00025; // $0.00025 per 1K tokens
|
|
}
|
|
}
|
|
|
|
// ============================================================================
|
|
// Benchmark Collector
|
|
// ============================================================================
|
|
|
|
/**
|
|
* Collects and aggregates metrics across all training iterations
|
|
*/
|
|
export class BenchmarkCollector {
|
|
private metrics: Map<ModelProvider, IterationResult[]> = new Map();
|
|
|
|
/**
|
|
* Add result to collection
|
|
*/
|
|
public addResult(result: IterationResult): void {
|
|
if (!this.metrics.has(result.modelProvider)) {
|
|
this.metrics.set(result.modelProvider, []);
|
|
}
|
|
this.metrics.get(result.modelProvider)!.push(result);
|
|
}
|
|
|
|
/**
|
|
* Get metrics for specific model
|
|
*/
|
|
public getModelMetrics(provider: ModelProvider): IterationResult[] {
|
|
return this.metrics.get(provider) || [];
|
|
}
|
|
|
|
/**
|
|
* Calculate aggregate statistics
|
|
*/
|
|
public getAggregateStats(provider: ModelProvider) {
|
|
const results = this.getModelMetrics(provider);
|
|
if (results.length === 0) {
|
|
return null;
|
|
}
|
|
|
|
const qualityScores = results.map(r => r.quality.score);
|
|
const latencies = results.map(r => r.performance.latency);
|
|
const costs = results.map(r => r.performance.cost);
|
|
|
|
return {
|
|
provider,
|
|
totalIterations: results.length,
|
|
avgQualityScore: this.average(qualityScores),
|
|
minQualityScore: Math.min(...qualityScores),
|
|
maxQualityScore: Math.max(...qualityScores),
|
|
avgLatency: this.average(latencies),
|
|
minLatency: Math.min(...latencies),
|
|
maxLatency: Math.max(...latencies),
|
|
totalCost: costs.reduce((sum, c) => sum + c, 0),
|
|
avgCostPer1K: this.average(costs) * 1000,
|
|
convergenceRate: this.calculateConvergenceRate(qualityScores),
|
|
improvementRate: this.calculateImprovementRate(qualityScores)
|
|
};
|
|
}
|
|
|
|
/**
|
|
* Get comparison across all models
|
|
*/
|
|
public getComparison() {
|
|
const comparison: Record<string, any> = {};
|
|
|
|
for (const provider of this.metrics.keys()) {
|
|
comparison[provider] = this.getAggregateStats(provider);
|
|
}
|
|
|
|
return comparison;
|
|
}
|
|
|
|
/**
|
|
* Get best performing model
|
|
*/
|
|
public getBestModel(): ModelProvider | null {
|
|
let bestProvider: ModelProvider | null = null;
|
|
let bestScore = -1;
|
|
|
|
for (const provider of this.metrics.keys()) {
|
|
const stats = this.getAggregateStats(provider);
|
|
if (stats && stats.avgQualityScore > bestScore) {
|
|
bestScore = stats.avgQualityScore;
|
|
bestProvider = provider;
|
|
}
|
|
}
|
|
|
|
return bestProvider;
|
|
}
|
|
|
|
/**
|
|
* Generate detailed report
|
|
*/
|
|
public generateReport(): string {
|
|
const comparison = this.getComparison();
|
|
const bestModel = this.getBestModel();
|
|
|
|
let report = '# DSPy Training Session Report\n\n';
|
|
report += `Generated: ${new Date().toISOString()}\n\n`;
|
|
report += `## Best Performing Model: ${bestModel}\n\n`;
|
|
report += '## Model Comparison\n\n';
|
|
|
|
for (const [provider, stats] of Object.entries(comparison)) {
|
|
if (!stats) continue;
|
|
|
|
report += `### ${provider.toUpperCase()}\n`;
|
|
report += `- Iterations: ${stats.totalIterations}\n`;
|
|
report += `- Avg Quality: ${stats.avgQualityScore.toFixed(4)}\n`;
|
|
report += `- Avg Latency: ${stats.avgLatency.toFixed(2)}ms\n`;
|
|
report += `- Total Cost: $${stats.totalCost.toFixed(4)}\n`;
|
|
report += `- Convergence Rate: ${stats.convergenceRate.toFixed(4)}\n`;
|
|
report += `- Improvement Rate: ${stats.improvementRate.toFixed(4)}\n\n`;
|
|
}
|
|
|
|
return report;
|
|
}
|
|
|
|
private average(numbers: number[]): number {
|
|
if (numbers.length === 0) return 0;
|
|
return numbers.reduce((sum, n) => sum + n, 0) / numbers.length;
|
|
}
|
|
|
|
private calculateConvergenceRate(scores: number[]): number {
|
|
if (scores.length < 2) return 0;
|
|
|
|
const halfPoint = Math.floor(scores.length / 2);
|
|
const firstHalf = scores.slice(0, halfPoint);
|
|
const secondHalf = scores.slice(halfPoint);
|
|
|
|
const firstAvg = this.average(firstHalf);
|
|
const secondAvg = this.average(secondHalf);
|
|
|
|
return secondAvg - firstAvg;
|
|
}
|
|
|
|
private calculateImprovementRate(scores: number[]): number {
|
|
if (scores.length < 2) return 0;
|
|
|
|
const firstScore = scores[0];
|
|
const lastScore = scores[scores.length - 1];
|
|
|
|
return (lastScore - firstScore) / firstScore;
|
|
}
|
|
}
|
|
|
|
// ============================================================================
|
|
// DSPy Optimization Engine
|
|
// ============================================================================
|
|
|
|
/**
|
|
* DSPy-powered prompt optimization engine
|
|
*/
|
|
export class OptimizationEngine {
|
|
private signatures: Map<string, DSPySignature> = new Map();
|
|
private optimizationHistory: Map<string, string[]> = new Map();
|
|
|
|
/**
|
|
* Create a new DSPy signature
|
|
*/
|
|
public createSignature(
|
|
name: string,
|
|
input: string,
|
|
output: string,
|
|
options?: {
|
|
examples?: Array<{ input: string; output: string }>;
|
|
constraints?: string[];
|
|
objectives?: string[];
|
|
}
|
|
): DSPySignature {
|
|
const signature: DSPySignature = {
|
|
input,
|
|
output,
|
|
examples: options?.examples || [],
|
|
constraints: options?.constraints || [],
|
|
objectives: options?.objectives || []
|
|
};
|
|
|
|
this.signatures.set(name, signature);
|
|
return signature;
|
|
}
|
|
|
|
/**
|
|
* Optimize prompt based on previous results
|
|
*/
|
|
public async optimizePrompt(
|
|
basePrompt: string,
|
|
results: IterationResult[],
|
|
signature: DSPySignature
|
|
): Promise<string> {
|
|
// Analyze results to identify improvement areas
|
|
const avgQuality = results.reduce((sum, r) => sum + r.quality.score, 0) / results.length;
|
|
|
|
let optimizedPrompt = basePrompt;
|
|
const optimizations: string[] = [];
|
|
|
|
// Apply optimization strategies based on signature and results
|
|
if (avgQuality < 0.7) {
|
|
// Add examples if quality is low
|
|
if (signature.examples && signature.examples.length > 0) {
|
|
optimizedPrompt = this.addExamples(optimizedPrompt, signature.examples);
|
|
optimizations.push('added_examples');
|
|
}
|
|
}
|
|
|
|
if (signature.constraints && signature.constraints.length > 0) {
|
|
optimizedPrompt = this.addConstraints(optimizedPrompt, signature.constraints);
|
|
optimizations.push('added_constraints');
|
|
}
|
|
|
|
if (signature.objectives && signature.objectives.length > 0) {
|
|
optimizedPrompt = this.addObjectives(optimizedPrompt, signature.objectives);
|
|
optimizations.push('added_objectives');
|
|
}
|
|
|
|
// Apply learning from best results
|
|
const bestResults = results
|
|
.filter(r => r.quality.score > 0.8)
|
|
.sort((a, b) => b.quality.score - a.quality.score)
|
|
.slice(0, 3);
|
|
|
|
if (bestResults.length > 0) {
|
|
optimizedPrompt = this.incorporateBestPractices(optimizedPrompt, bestResults);
|
|
optimizations.push('incorporated_best_practices');
|
|
}
|
|
|
|
// Store optimization history
|
|
if (!this.optimizationHistory.has(basePrompt)) {
|
|
this.optimizationHistory.set(basePrompt, []);
|
|
}
|
|
this.optimizationHistory.get(basePrompt)!.push(optimizedPrompt);
|
|
|
|
return optimizedPrompt;
|
|
}
|
|
|
|
/**
|
|
* Enable cross-model learning
|
|
*/
|
|
public async crossModelOptimization(
|
|
allResults: Map<ModelProvider, IterationResult[]>
|
|
): Promise<Map<ModelProvider, string>> {
|
|
const optimizedPrompts = new Map<ModelProvider, string>();
|
|
|
|
// Find best performing model
|
|
let bestProvider: ModelProvider | null = null;
|
|
let bestScore = -1;
|
|
|
|
for (const [provider, results] of allResults.entries()) {
|
|
const avgScore = results.reduce((sum, r) => sum + r.quality.score, 0) / results.length;
|
|
if (avgScore > bestScore) {
|
|
bestScore = avgScore;
|
|
bestProvider = provider;
|
|
}
|
|
}
|
|
|
|
if (!bestProvider) return optimizedPrompts;
|
|
|
|
// Extract best practices from best model
|
|
const bestResults = allResults.get(bestProvider)!;
|
|
const bestPrompts = bestResults
|
|
.filter(r => r.quality.score > 0.85)
|
|
.map(r => r.prompt);
|
|
|
|
// Apply to other models
|
|
for (const [provider, results] of allResults.entries()) {
|
|
if (provider === bestProvider) continue;
|
|
|
|
const basePrompt = results[results.length - 1]?.prompt || '';
|
|
const optimized = this.mergePromptStrategies(basePrompt, bestPrompts);
|
|
optimizedPrompts.set(provider, optimized);
|
|
}
|
|
|
|
return optimizedPrompts;
|
|
}
|
|
|
|
private addExamples(prompt: string, examples: Array<{ input: string; output: string }>): string {
|
|
let enhanced = prompt + '\n\nExamples:\n';
|
|
examples.forEach((ex, i) => {
|
|
enhanced += `${i + 1}. Input: ${ex.input}\n Output: ${ex.output}\n`;
|
|
});
|
|
return enhanced;
|
|
}
|
|
|
|
private addConstraints(prompt: string, constraints: string[]): string {
|
|
let enhanced = prompt + '\n\nConstraints:\n';
|
|
constraints.forEach((c, i) => {
|
|
enhanced += `${i + 1}. ${c}\n`;
|
|
});
|
|
return enhanced;
|
|
}
|
|
|
|
private addObjectives(prompt: string, objectives: string[]): string {
|
|
let enhanced = prompt + '\n\nObjectives:\n';
|
|
objectives.forEach((o, i) => {
|
|
enhanced += `${i + 1}. ${o}\n`;
|
|
});
|
|
return enhanced;
|
|
}
|
|
|
|
private incorporateBestPractices(prompt: string, bestResults: IterationResult[]): string {
|
|
// Extract common patterns from best results
|
|
const commonPhrases = this.extractCommonPhrases(bestResults.map(r => r.output));
|
|
|
|
let enhanced = prompt + '\n\nBest practices (from top results):\n';
|
|
commonPhrases.slice(0, 3).forEach((phrase, i) => {
|
|
enhanced += `${i + 1}. ${phrase}\n`;
|
|
});
|
|
|
|
return enhanced;
|
|
}
|
|
|
|
private extractCommonPhrases(outputs: string[]): string[] {
|
|
// Simple common phrase extraction
|
|
const phrases: string[] = [];
|
|
outputs.forEach(output => {
|
|
const sentences = output.split(/[.!?]+/).filter(s => s.trim().length > 20);
|
|
phrases.push(...sentences);
|
|
});
|
|
return phrases;
|
|
}
|
|
|
|
private mergePromptStrategies(basePrompt: string, bestPrompts: string[]): string {
|
|
// Merge strategies from best prompts
|
|
let merged = basePrompt;
|
|
|
|
// Extract unique instructions from best prompts
|
|
bestPrompts.forEach(bp => {
|
|
const instructions = bp.split('\n').filter(line =>
|
|
line.includes(':') || line.includes('must') || line.includes('should')
|
|
);
|
|
|
|
instructions.forEach(instruction => {
|
|
if (!merged.includes(instruction)) {
|
|
merged += '\n' + instruction;
|
|
}
|
|
});
|
|
});
|
|
|
|
return merged;
|
|
}
|
|
}
|
|
|
|
// ============================================================================
|
|
// Main Training Session
|
|
// ============================================================================
|
|
|
|
/**
|
|
* Main DSPy training session orchestrator
|
|
*/
|
|
export class DSPyTrainingSession extends EventEmitter {
|
|
private config: TrainingConfig;
|
|
private agents: Map<ModelProvider, ModelTrainingAgent> = new Map();
|
|
private collector: BenchmarkCollector;
|
|
private optimizer: OptimizationEngine;
|
|
private currentPhase: TrainingPhase = TrainingPhase.BASELINE;
|
|
private startTime: number = 0;
|
|
private totalCost: number = 0;
|
|
|
|
constructor(config: TrainingConfig) {
|
|
super();
|
|
this.config = TrainingConfigSchema.parse(config);
|
|
this.collector = new BenchmarkCollector();
|
|
this.optimizer = new OptimizationEngine();
|
|
|
|
this.initializeAgents();
|
|
}
|
|
|
|
/**
|
|
* Initialize model agents
|
|
*/
|
|
private initializeAgents(): void {
|
|
for (const modelConfig of this.config.models) {
|
|
let agent: ModelTrainingAgent;
|
|
|
|
switch (modelConfig.provider) {
|
|
case ModelProvider.CLAUDE:
|
|
agent = new ClaudeSonnetAgent(modelConfig);
|
|
break;
|
|
case ModelProvider.GPT4:
|
|
agent = new GPT4Agent(modelConfig);
|
|
break;
|
|
case ModelProvider.LLAMA:
|
|
agent = new LlamaAgent(modelConfig);
|
|
break;
|
|
case ModelProvider.GEMINI:
|
|
agent = new GeminiAgent(modelConfig);
|
|
break;
|
|
default:
|
|
throw new Error(`Unsupported model provider: ${modelConfig.provider}`);
|
|
}
|
|
|
|
// Forward agent events
|
|
agent.on('iteration', (result) => this.handleIteration(result));
|
|
agent.on('error', (error) => this.emit('error', error));
|
|
|
|
this.agents.set(modelConfig.provider, agent);
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Run complete training pipeline
|
|
*/
|
|
public async run(basePrompt: string, signature: DSPySignature): Promise<void> {
|
|
this.startTime = performance.now();
|
|
this.emit('start', { phase: TrainingPhase.BASELINE });
|
|
|
|
try {
|
|
// Phase 1: Baseline generation
|
|
await this.runBaseline(basePrompt, signature);
|
|
|
|
// Phase 2: DSPy optimization
|
|
await this.runOptimization(basePrompt, signature);
|
|
|
|
// Phase 3: Cross-model learning
|
|
if (this.config.enableCrossLearning) {
|
|
await this.runCrossLearning(signature);
|
|
}
|
|
|
|
// Phase 4: Final benchmark
|
|
await this.runBenchmark(basePrompt, signature);
|
|
|
|
// Phase 5: Generate report
|
|
await this.generateReport();
|
|
|
|
const endTime = performance.now();
|
|
this.emit('complete', {
|
|
duration: endTime - this.startTime,
|
|
totalCost: this.totalCost,
|
|
report: this.collector.generateReport()
|
|
});
|
|
|
|
// Integrate with hooks if enabled
|
|
if (this.config.enableHooksIntegration) {
|
|
await this.integrateWithHooks();
|
|
}
|
|
|
|
} catch (error) {
|
|
this.emit('error', error);
|
|
throw error;
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Phase 1: Baseline generation (all models)
|
|
*/
|
|
private async runBaseline(basePrompt: string, signature: DSPySignature): Promise<void> {
|
|
this.currentPhase = TrainingPhase.BASELINE;
|
|
this.emit('phase', TrainingPhase.BASELINE);
|
|
|
|
const iterations = this.config.baselineIterations || 3;
|
|
|
|
for (let i = 0; i < iterations; i++) {
|
|
// Run all agents in parallel
|
|
const promises = Array.from(this.agents.values()).map(agent =>
|
|
agent.execute(basePrompt, signature)
|
|
);
|
|
|
|
await Promise.all(promises);
|
|
|
|
// Check cost budget
|
|
if (this.config.costBudget && this.totalCost >= this.config.costBudget) {
|
|
this.emit('budget_exceeded', this.totalCost);
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Phase 2: DSPy optimization (5 rounds per model)
|
|
*/
|
|
private async runOptimization(basePrompt: string, signature: DSPySignature): Promise<void> {
|
|
this.currentPhase = TrainingPhase.OPTIMIZATION;
|
|
this.emit('phase', TrainingPhase.OPTIMIZATION);
|
|
|
|
const rounds = this.config.optimizationRounds || 5;
|
|
|
|
for (let round = 0; round < rounds; round++) {
|
|
this.emit('optimization_round', round + 1);
|
|
|
|
// Optimize prompts for each model based on previous results
|
|
for (const [provider, agent] of this.agents.entries()) {
|
|
const results = agent.getResults();
|
|
const optimizedPrompt = await this.optimizer.optimizePrompt(
|
|
basePrompt,
|
|
results,
|
|
signature
|
|
);
|
|
|
|
// Execute with optimized prompt
|
|
await agent.execute(optimizedPrompt, signature);
|
|
|
|
// Check convergence
|
|
if (agent.hasConverged()) {
|
|
this.emit('converged', provider);
|
|
}
|
|
}
|
|
|
|
// Check cost budget
|
|
if (this.config.costBudget && this.totalCost >= this.config.costBudget) {
|
|
this.emit('budget_exceeded', this.totalCost);
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Phase 3: Cross-model learning (share best patterns)
|
|
*/
|
|
private async runCrossLearning(signature: DSPySignature): Promise<void> {
|
|
this.currentPhase = TrainingPhase.CROSS_LEARNING;
|
|
this.emit('phase', TrainingPhase.CROSS_LEARNING);
|
|
|
|
// Collect all results
|
|
const allResults = new Map<ModelProvider, IterationResult[]>();
|
|
for (const [provider, agent] of this.agents.entries()) {
|
|
allResults.set(provider, agent.getResults());
|
|
}
|
|
|
|
// Generate cross-model optimizations
|
|
const optimizedPrompts = await this.optimizer.crossModelOptimization(allResults);
|
|
|
|
// Apply optimizations
|
|
for (const [provider, optimizedPrompt] of optimizedPrompts.entries()) {
|
|
const agent = this.agents.get(provider);
|
|
if (agent) {
|
|
await agent.execute(optimizedPrompt, signature);
|
|
}
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Phase 4: Final benchmark comparison
|
|
*/
|
|
private async runBenchmark(basePrompt: string, signature: DSPySignature): Promise<void> {
|
|
this.currentPhase = TrainingPhase.BENCHMARK;
|
|
this.emit('phase', TrainingPhase.BENCHMARK);
|
|
|
|
const samples = Math.min(this.config.benchmarkSamples || 100, 100);
|
|
|
|
for (let i = 0; i < samples; i++) {
|
|
// Run all agents in parallel with final optimized prompts
|
|
const promises = Array.from(this.agents.values()).map(agent => {
|
|
const results = agent.getResults();
|
|
const lastPrompt = results[results.length - 1]?.prompt || basePrompt;
|
|
return agent.execute(lastPrompt, signature);
|
|
});
|
|
|
|
await Promise.all(promises);
|
|
|
|
if (i % 10 === 0) {
|
|
this.emit('benchmark_progress', { completed: i, total: samples });
|
|
}
|
|
|
|
// Check cost budget
|
|
if (this.config.costBudget && this.totalCost >= this.config.costBudget) {
|
|
this.emit('budget_exceeded', this.totalCost);
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Phase 5: Generate comprehensive report
|
|
*/
|
|
private async generateReport(): Promise<void> {
|
|
this.currentPhase = TrainingPhase.REPORT;
|
|
this.emit('phase', TrainingPhase.REPORT);
|
|
|
|
const report = this.collector.generateReport();
|
|
const comparison = this.collector.getComparison();
|
|
const bestModel = this.collector.getBestModel();
|
|
|
|
this.emit('report', {
|
|
report,
|
|
comparison,
|
|
bestModel,
|
|
totalCost: this.totalCost,
|
|
duration: performance.now() - this.startTime
|
|
});
|
|
}
|
|
|
|
/**
|
|
* Handle iteration results
|
|
*/
|
|
private handleIteration(result: IterationResult): void {
|
|
this.collector.addResult(result);
|
|
this.totalCost += result.performance.cost;
|
|
|
|
this.emit('iteration', result);
|
|
this.emit('metrics', {
|
|
provider: result.modelProvider,
|
|
quality: result.quality,
|
|
performance: result.performance,
|
|
totalCost: this.totalCost
|
|
});
|
|
}
|
|
|
|
/**
|
|
* Integrate with Claude Flow hooks for swarm coordination
|
|
*/
|
|
private async integrateWithHooks(): Promise<void> {
|
|
try {
|
|
// Store training results in memory for swarm coordination
|
|
const results = {
|
|
bestModel: this.collector.getBestModel(),
|
|
comparison: this.collector.getComparison(),
|
|
totalCost: this.totalCost,
|
|
timestamp: new Date().toISOString()
|
|
};
|
|
|
|
// Simulate hook integration (in production, use actual hooks)
|
|
this.emit('hooks_integration', {
|
|
action: 'store',
|
|
key: 'swarm/training/dspy-results',
|
|
value: JSON.stringify(results)
|
|
});
|
|
|
|
} catch (error) {
|
|
this.emit('error', new Error(`Hooks integration failed: ${error}`));
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Get current session statistics
|
|
*/
|
|
public getStatistics() {
|
|
return {
|
|
currentPhase: this.currentPhase,
|
|
totalCost: this.totalCost,
|
|
duration: performance.now() - this.startTime,
|
|
bestModel: this.collector.getBestModel(),
|
|
comparison: this.collector.getComparison()
|
|
};
|
|
}
|
|
|
|
/**
|
|
* Stop training session
|
|
*/
|
|
public stop(): void {
|
|
this.emit('stopped', this.getStatistics());
|
|
}
|
|
}
|
|
|
|
// ============================================================================
|
|
// Exports
|
|
// ============================================================================
|
|
|
|
// Note: ModelProvider and TrainingPhase are already exported as enums above
|
|
export type {
|
|
QualityMetrics,
|
|
PerformanceMetrics,
|
|
IterationResult,
|
|
ModelConfig,
|
|
DSPySignature,
|
|
TrainingConfig
|
|
};
|