937 lines
35 KiB
JavaScript
937 lines
35 KiB
JavaScript
"use strict";
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/**
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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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Object.defineProperty(exports, "__esModule", { value: true });
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exports.DSPyTrainingSession = exports.OptimizationEngine = exports.BenchmarkCollector = exports.GeminiAgent = exports.LlamaAgent = exports.GPT4Agent = exports.ClaudeSonnetAgent = exports.ModelTrainingAgent = exports.TrainingConfigSchema = exports.TrainingPhase = exports.ModelProvider = void 0;
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const events_1 = require("events");
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const perf_hooks_1 = require("perf_hooks");
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const zod_1 = require("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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var ModelProvider;
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(function (ModelProvider) {
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ModelProvider["CLAUDE"] = "claude";
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ModelProvider["GPT4"] = "gpt4";
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ModelProvider["LLAMA"] = "llama";
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ModelProvider["GEMINI"] = "gemini";
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})(ModelProvider || (exports.ModelProvider = ModelProvider = {}));
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/**
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* Training phase states
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*/
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var TrainingPhase;
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(function (TrainingPhase) {
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TrainingPhase["BASELINE"] = "baseline";
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TrainingPhase["OPTIMIZATION"] = "optimization";
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TrainingPhase["CROSS_LEARNING"] = "cross_learning";
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TrainingPhase["BENCHMARK"] = "benchmark";
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TrainingPhase["REPORT"] = "report";
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})(TrainingPhase || (exports.TrainingPhase = TrainingPhase = {}));
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exports.TrainingConfigSchema = zod_1.z.object({
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models: zod_1.z.array(zod_1.z.object({
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provider: zod_1.z.nativeEnum(ModelProvider),
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model: zod_1.z.string(),
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apiKey: zod_1.z.string(),
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temperature: zod_1.z.number().optional(),
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maxTokens: zod_1.z.number().optional(),
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topP: zod_1.z.number().optional(),
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presencePenalty: zod_1.z.number().optional(),
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frequencyPenalty: zod_1.z.number().optional()
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})).min(1, 'At least one model is required'),
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optimizationRounds: zod_1.z.number().default(5),
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convergenceThreshold: zod_1.z.number().default(0.95),
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maxConcurrency: zod_1.z.number().default(4),
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enableCrossLearning: zod_1.z.boolean().default(true),
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enableHooksIntegration: zod_1.z.boolean().default(true),
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costBudget: zod_1.z.number().optional(),
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timeoutPerIteration: zod_1.z.number().default(30000),
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baselineIterations: zod_1.z.number().default(3),
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benchmarkSamples: zod_1.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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class ModelTrainingAgent extends events_1.EventEmitter {
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constructor(config) {
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super();
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this.results = [];
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this.currentIteration = 0;
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this.totalCost = 0;
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this.isConverged = false;
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this.config = config;
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}
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/**
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* Calculate quality metrics for generated output
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*/
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async calculateQuality(output, expectedSignature) {
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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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calculatePerformance(startTime, endTime, tokensUsed) {
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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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calculateCost(tokensUsed) {
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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 current results
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*/
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getResults() {
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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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getTotalCost() {
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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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hasConverged() {
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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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calculateOverallScore(output, signature) {
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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 (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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calculateAccuracy(output, signature) {
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// Check if output matches expected format
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if (!output || output.trim().length === 0)
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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 => this.checkConstraint(output, c));
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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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calculateCoherence(output) {
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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)
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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) => sum + Math.pow(s.length - avgLength, 2), 0) / 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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calculateRelevance(output, signature) {
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// Check keyword overlap with input signature
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const inputWords = new Set(signature.input.toLowerCase().split(/\s+/).filter(w => w.length > 3));
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const outputWords = new Set(output.toLowerCase().split(/\s+/).filter(w => w.length > 3));
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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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calculateDiversity(output) {
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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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calculateCreativity(output) {
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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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checkConstraint(output, constraint) {
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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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calculateErrorRate() {
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if (this.results.length === 0)
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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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exports.ModelTrainingAgent = ModelTrainingAgent;
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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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class ClaudeSonnetAgent extends ModelTrainingAgent {
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async execute(prompt, signature) {
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const startTime = perf_hooks_1.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 = perf_hooks_1.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 = {
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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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}
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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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async callClaudeAPI(prompt, signature) {
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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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estimateTokens(prompt, output) {
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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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getCostPer1KTokens() {
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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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exports.ClaudeSonnetAgent = ClaudeSonnetAgent;
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/**
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* GPT-4 training agent
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*/
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class GPT4Agent extends ModelTrainingAgent {
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async execute(prompt, signature) {
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const startTime = perf_hooks_1.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 = perf_hooks_1.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 = {
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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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}
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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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async callGPT4API(prompt, signature) {
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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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estimateTokens(prompt, output) {
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return Math.ceil((prompt.length + output.length) / 4);
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}
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getCostPer1KTokens() {
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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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exports.GPT4Agent = GPT4Agent;
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/**
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* Llama training agent
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*/
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class LlamaAgent extends ModelTrainingAgent {
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async execute(prompt, signature) {
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const startTime = perf_hooks_1.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 = perf_hooks_1.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 = {
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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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}
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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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async callLlamaAPI(prompt, signature) {
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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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estimateTokens(prompt, output) {
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return Math.ceil((prompt.length + output.length) / 4);
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}
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getCostPer1KTokens() {
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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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exports.LlamaAgent = LlamaAgent;
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/**
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* Gemini training agent
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*/
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class GeminiAgent extends ModelTrainingAgent {
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async execute(prompt, signature) {
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const startTime = perf_hooks_1.performance.now();
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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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const endTime = perf_hooks_1.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 = {
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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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this.results.push(result);
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this.emit('iteration', result);
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return result;
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}
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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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async callGeminiAPI(prompt, signature) {
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// Placeholder for actual Gemini API call
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// In production, use @google/generative-ai
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return `Gemini response to: ${prompt}\nSignature: ${JSON.stringify(signature)}`;
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}
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estimateTokens(prompt, output) {
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return Math.ceil((prompt.length + output.length) / 4);
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}
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getCostPer1KTokens() {
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// Gemini pricing (approximate)
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return 0.00025; // $0.00025 per 1K tokens
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}
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}
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exports.GeminiAgent = GeminiAgent;
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// ============================================================================
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// Benchmark Collector
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// ============================================================================
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/**
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* Collects and aggregates metrics across all training iterations
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*/
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class BenchmarkCollector {
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constructor() {
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this.metrics = new Map();
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}
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/**
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* Add result to collection
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*/
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addResult(result) {
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if (!this.metrics.has(result.modelProvider)) {
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this.metrics.set(result.modelProvider, []);
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}
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this.metrics.get(result.modelProvider).push(result);
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}
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/**
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* Get metrics for specific model
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*/
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getModelMetrics(provider) {
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return this.metrics.get(provider) || [];
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}
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/**
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* Calculate aggregate statistics
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*/
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getAggregateStats(provider) {
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const results = this.getModelMetrics(provider);
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if (results.length === 0) {
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return null;
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}
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const qualityScores = results.map(r => r.quality.score);
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const latencies = results.map(r => r.performance.latency);
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const costs = results.map(r => r.performance.cost);
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return {
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provider,
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totalIterations: results.length,
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avgQualityScore: this.average(qualityScores),
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minQualityScore: Math.min(...qualityScores),
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maxQualityScore: Math.max(...qualityScores),
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avgLatency: this.average(latencies),
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minLatency: Math.min(...latencies),
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maxLatency: Math.max(...latencies),
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totalCost: costs.reduce((sum, c) => sum + c, 0),
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avgCostPer1K: this.average(costs) * 1000,
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convergenceRate: this.calculateConvergenceRate(qualityScores),
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improvementRate: this.calculateImprovementRate(qualityScores)
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};
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}
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/**
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* Get comparison across all models
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*/
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getComparison() {
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const comparison = {};
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for (const provider of this.metrics.keys()) {
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comparison[provider] = this.getAggregateStats(provider);
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}
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return comparison;
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}
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/**
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* Get best performing model
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*/
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getBestModel() {
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let bestProvider = null;
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let bestScore = -1;
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for (const provider of this.metrics.keys()) {
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const stats = this.getAggregateStats(provider);
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if (stats && stats.avgQualityScore > bestScore) {
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bestScore = stats.avgQualityScore;
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bestProvider = provider;
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}
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}
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return bestProvider;
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}
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/**
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* Generate detailed report
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*/
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generateReport() {
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const comparison = this.getComparison();
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const bestModel = this.getBestModel();
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let report = '# DSPy Training Session Report\n\n';
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report += `Generated: ${new Date().toISOString()}\n\n`;
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report += `## Best Performing Model: ${bestModel}\n\n`;
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report += '## Model Comparison\n\n';
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for (const [provider, stats] of Object.entries(comparison)) {
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if (!stats)
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continue;
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report += `### ${provider.toUpperCase()}\n`;
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report += `- Iterations: ${stats.totalIterations}\n`;
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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;
|
|
}
|
|
average(numbers) {
|
|
if (numbers.length === 0)
|
|
return 0;
|
|
return numbers.reduce((sum, n) => sum + n, 0) / numbers.length;
|
|
}
|
|
calculateConvergenceRate(scores) {
|
|
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;
|
|
}
|
|
calculateImprovementRate(scores) {
|
|
if (scores.length < 2)
|
|
return 0;
|
|
const firstScore = scores[0];
|
|
const lastScore = scores[scores.length - 1];
|
|
return (lastScore - firstScore) / firstScore;
|
|
}
|
|
}
|
|
exports.BenchmarkCollector = BenchmarkCollector;
|
|
// ============================================================================
|
|
// DSPy Optimization Engine
|
|
// ============================================================================
|
|
/**
|
|
* DSPy-powered prompt optimization engine
|
|
*/
|
|
class OptimizationEngine {
|
|
constructor() {
|
|
this.signatures = new Map();
|
|
this.optimizationHistory = new Map();
|
|
}
|
|
/**
|
|
* Create a new DSPy signature
|
|
*/
|
|
createSignature(name, input, output, options) {
|
|
const signature = {
|
|
input,
|
|
output,
|
|
examples: options?.examples || [],
|
|
constraints: options?.constraints || [],
|
|
objectives: options?.objectives || []
|
|
};
|
|
this.signatures.set(name, signature);
|
|
return signature;
|
|
}
|
|
/**
|
|
* Optimize prompt based on previous results
|
|
*/
|
|
async optimizePrompt(basePrompt, results, signature) {
|
|
// Analyze results to identify improvement areas
|
|
const avgQuality = results.reduce((sum, r) => sum + r.quality.score, 0) / results.length;
|
|
let optimizedPrompt = basePrompt;
|
|
const optimizations = [];
|
|
// 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
|
|
*/
|
|
async crossModelOptimization(allResults) {
|
|
const optimizedPrompts = new Map();
|
|
// Find best performing model
|
|
let bestProvider = 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;
|
|
}
|
|
addExamples(prompt, examples) {
|
|
let enhanced = prompt + '\n\nExamples:\n';
|
|
examples.forEach((ex, i) => {
|
|
enhanced += `${i + 1}. Input: ${ex.input}\n Output: ${ex.output}\n`;
|
|
});
|
|
return enhanced;
|
|
}
|
|
addConstraints(prompt, constraints) {
|
|
let enhanced = prompt + '\n\nConstraints:\n';
|
|
constraints.forEach((c, i) => {
|
|
enhanced += `${i + 1}. ${c}\n`;
|
|
});
|
|
return enhanced;
|
|
}
|
|
addObjectives(prompt, objectives) {
|
|
let enhanced = prompt + '\n\nObjectives:\n';
|
|
objectives.forEach((o, i) => {
|
|
enhanced += `${i + 1}. ${o}\n`;
|
|
});
|
|
return enhanced;
|
|
}
|
|
incorporateBestPractices(prompt, bestResults) {
|
|
// Extract common patterns from best results
|
|
const commonPhrases = this.extractCommonPhrases(bestResults.map(r => r.output));
|
|
let enhanced = prompt + '\n\nBest practices (from top results):\n';
|
|
commonPhrases.slice(0, 3).forEach((phrase, i) => {
|
|
enhanced += `${i + 1}. ${phrase}\n`;
|
|
});
|
|
return enhanced;
|
|
}
|
|
extractCommonPhrases(outputs) {
|
|
// Simple common phrase extraction
|
|
const phrases = [];
|
|
outputs.forEach(output => {
|
|
const sentences = output.split(/[.!?]+/).filter(s => s.trim().length > 20);
|
|
phrases.push(...sentences);
|
|
});
|
|
return phrases;
|
|
}
|
|
mergePromptStrategies(basePrompt, bestPrompts) {
|
|
// 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;
|
|
}
|
|
}
|
|
exports.OptimizationEngine = OptimizationEngine;
|
|
// ============================================================================
|
|
// Main Training Session
|
|
// ============================================================================
|
|
/**
|
|
* Main DSPy training session orchestrator
|
|
*/
|
|
class DSPyTrainingSession extends events_1.EventEmitter {
|
|
constructor(config) {
|
|
super();
|
|
this.agents = new Map();
|
|
this.currentPhase = TrainingPhase.BASELINE;
|
|
this.startTime = 0;
|
|
this.totalCost = 0;
|
|
this.config = exports.TrainingConfigSchema.parse(config);
|
|
this.collector = new BenchmarkCollector();
|
|
this.optimizer = new OptimizationEngine();
|
|
this.initializeAgents();
|
|
}
|
|
/**
|
|
* Initialize model agents
|
|
*/
|
|
initializeAgents() {
|
|
for (const modelConfig of this.config.models) {
|
|
let agent;
|
|
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
|
|
*/
|
|
async run(basePrompt, signature) {
|
|
this.startTime = perf_hooks_1.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 = perf_hooks_1.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)
|
|
*/
|
|
async runBaseline(basePrompt, signature) {
|
|
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)
|
|
*/
|
|
async runOptimization(basePrompt, signature) {
|
|
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)
|
|
*/
|
|
async runCrossLearning(signature) {
|
|
this.currentPhase = TrainingPhase.CROSS_LEARNING;
|
|
this.emit('phase', TrainingPhase.CROSS_LEARNING);
|
|
// Collect all results
|
|
const allResults = new Map();
|
|
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
|
|
*/
|
|
async runBenchmark(basePrompt, signature) {
|
|
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
|
|
*/
|
|
async generateReport() {
|
|
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: perf_hooks_1.performance.now() - this.startTime
|
|
});
|
|
}
|
|
/**
|
|
* Handle iteration results
|
|
*/
|
|
handleIteration(result) {
|
|
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
|
|
*/
|
|
async integrateWithHooks() {
|
|
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
|
|
*/
|
|
getStatistics() {
|
|
return {
|
|
currentPhase: this.currentPhase,
|
|
totalCost: this.totalCost,
|
|
duration: perf_hooks_1.performance.now() - this.startTime,
|
|
bestModel: this.collector.getBestModel(),
|
|
comparison: this.collector.getComparison()
|
|
};
|
|
}
|
|
/**
|
|
* Stop training session
|
|
*/
|
|
stop() {
|
|
this.emit('stopped', this.getStatistics());
|
|
}
|
|
}
|
|
exports.DSPyTrainingSession = DSPyTrainingSession;
|
|
//# sourceMappingURL=dspy-learning-session.js.map
|