Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'
This commit is contained in:
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737
vendor/ruvector/npm/packages/agentic-synth/training/dspy-multi-model-benchmark.js
vendored
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vendor/ruvector/npm/packages/agentic-synth/training/dspy-multi-model-benchmark.js
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"use strict";
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/**
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* DSPy.ts Multi-Model Benchmarking System v1.0.0
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*
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* Comprehensive benchmarking suite comparing multiple models across:
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* - Quality metrics (f1Score, exactMatch, bleuScore, rougeScore)
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* - Optimization strategies (BootstrapFewShot, MIPROv2)
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* - Cost-effectiveness analysis
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* - Performance characteristics
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*
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* Real-world implementation using actual dspy.ts v2.1.1 features:
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* - ChainOfThought for reasoning
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* - ReAct for iterative improvement
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* - MultiChainComparison for ensemble decisions
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* - BootstrapFewShot & MIPROv2 optimizers
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*
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* @requires dspy.ts@2.1.1
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* @requires Environment: OPENAI_API_KEY, ANTHROPIC_API_KEY
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*/
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var __createBinding = (this && this.__createBinding) || (Object.create ? (function(o, m, k, k2) {
|
||||
if (k2 === undefined) k2 = k;
|
||||
var desc = Object.getOwnPropertyDescriptor(m, k);
|
||||
if (!desc || ("get" in desc ? !m.__esModule : desc.writable || desc.configurable)) {
|
||||
desc = { enumerable: true, get: function() { return m[k]; } };
|
||||
}
|
||||
Object.defineProperty(o, k2, desc);
|
||||
}) : (function(o, m, k, k2) {
|
||||
if (k2 === undefined) k2 = k;
|
||||
o[k2] = m[k];
|
||||
}));
|
||||
var __setModuleDefault = (this && this.__setModuleDefault) || (Object.create ? (function(o, v) {
|
||||
Object.defineProperty(o, "default", { enumerable: true, value: v });
|
||||
}) : function(o, v) {
|
||||
o["default"] = v;
|
||||
});
|
||||
var __importStar = (this && this.__importStar) || (function () {
|
||||
var ownKeys = function(o) {
|
||||
ownKeys = Object.getOwnPropertyNames || function (o) {
|
||||
var ar = [];
|
||||
for (var k in o) if (Object.prototype.hasOwnProperty.call(o, k)) ar[ar.length] = k;
|
||||
return ar;
|
||||
};
|
||||
return ownKeys(o);
|
||||
};
|
||||
return function (mod) {
|
||||
if (mod && mod.__esModule) return mod;
|
||||
var result = {};
|
||||
if (mod != null) for (var k = ownKeys(mod), i = 0; i < k.length; i++) if (k[i] !== "default") __createBinding(result, mod, k[i]);
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||||
__setModuleDefault(result, mod);
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||||
return result;
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||||
};
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||||
})();
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.DSPyMultiModelBenchmark = void 0;
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const perf_hooks_1 = require("perf_hooks");
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const fs = __importStar(require("fs/promises"));
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const path = __importStar(require("path"));
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// Import real dspy.ts components from dist/src
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// Note: dspy.ts package main entry needs dist/src prefix
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const dspy = require('dspy.ts/dist/src/index');
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const { configureLM, getLM, PredictModule, ChainOfThought, ReAct, BootstrapFewShot, MIPROv2, exactMatch, f1Score, bleuScore, rougeL: rougeScore, evaluate } = dspy;
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// ============================================================================
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// Language Model Implementations
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// ============================================================================
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/**
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* OpenAI Language Model Implementation
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||||
*/
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||||
class OpenAILM {
|
||||
constructor(config) {
|
||||
this.inputTokens = 0;
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||||
this.outputTokens = 0;
|
||||
this.apiKey = config.apiKey;
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||||
this.model = config.model;
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||||
}
|
||||
async generate(prompt, options) {
|
||||
const response = await fetch('https://api.openai.com/v1/chat/completions', {
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||||
method: 'POST',
|
||||
headers: {
|
||||
'Authorization': `Bearer ${this.apiKey}`,
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||||
'Content-Type': 'application/json',
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},
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||||
body: JSON.stringify({
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||||
model: this.model,
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messages: [{ role: 'user', content: prompt }],
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||||
max_tokens: options?.maxTokens || 2000,
|
||||
temperature: options?.temperature ?? 0.7,
|
||||
stop: options?.stopSequences,
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||||
}),
|
||||
});
|
||||
if (!response.ok) {
|
||||
const error = await response.text();
|
||||
throw new Error(`OpenAI API error: ${response.status} ${error}`);
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||||
}
|
||||
const data = await response.json();
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||||
this.inputTokens += data.usage?.prompt_tokens || 0;
|
||||
this.outputTokens += data.usage?.completion_tokens || 0;
|
||||
return data.choices[0].message.content;
|
||||
}
|
||||
getTokenUsage() {
|
||||
return { input: this.inputTokens, output: this.outputTokens };
|
||||
}
|
||||
resetTokenUsage() {
|
||||
this.inputTokens = 0;
|
||||
this.outputTokens = 0;
|
||||
}
|
||||
}
|
||||
/**
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||||
* Anthropic Language Model Implementation
|
||||
*/
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||||
class AnthropicLM {
|
||||
constructor(config) {
|
||||
this.inputTokens = 0;
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||||
this.outputTokens = 0;
|
||||
this.apiKey = config.apiKey;
|
||||
this.model = config.model;
|
||||
}
|
||||
async generate(prompt, options) {
|
||||
const response = await fetch('https://api.anthropic.com/v1/messages', {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'x-api-key': this.apiKey,
|
||||
'anthropic-version': '2023-06-01',
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
body: JSON.stringify({
|
||||
model: this.model,
|
||||
messages: [{ role: 'user', content: prompt }],
|
||||
max_tokens: options?.maxTokens || 2000,
|
||||
temperature: options?.temperature ?? 0.7,
|
||||
stop_sequences: options?.stopSequences,
|
||||
}),
|
||||
});
|
||||
if (!response.ok) {
|
||||
const error = await response.text();
|
||||
throw new Error(`Anthropic API error: ${response.status} ${error}`);
|
||||
}
|
||||
const data = await response.json();
|
||||
this.inputTokens += data.usage?.input_tokens || 0;
|
||||
this.outputTokens += data.usage?.output_tokens || 0;
|
||||
return data.content[0].text;
|
||||
}
|
||||
getTokenUsage() {
|
||||
return { input: this.inputTokens, output: this.outputTokens };
|
||||
}
|
||||
resetTokenUsage() {
|
||||
this.inputTokens = 0;
|
||||
this.outputTokens = 0;
|
||||
}
|
||||
}
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||||
// ============================================================================
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||||
// Synthetic Data Generation Module using DSPy
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||||
// ============================================================================
|
||||
/**
|
||||
* Synthetic Data Generator using Chain of Thought
|
||||
*/
|
||||
class SyntheticDataModule extends ChainOfThought {
|
||||
constructor() {
|
||||
super({
|
||||
name: 'SyntheticDataGenerator',
|
||||
signature: {
|
||||
inputs: [
|
||||
{ name: 'schema', type: 'string', description: 'JSON schema for data generation' },
|
||||
{ name: 'count', type: 'number', description: 'Number of records to generate' }
|
||||
],
|
||||
outputs: [
|
||||
{ name: 'data', type: 'string', description: 'Generated data as JSON array' },
|
||||
{ name: 'quality_score', type: 'number', description: 'Quality score 0-1' }
|
||||
]
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
/**
|
||||
* Data Quality Validator using PredictModule
|
||||
*/
|
||||
class DataQualityModule extends PredictModule {
|
||||
constructor() {
|
||||
super({
|
||||
name: 'DataQualityValidator',
|
||||
signature: {
|
||||
inputs: [
|
||||
{ name: 'data', type: 'string', description: 'Data to validate' },
|
||||
{ name: 'schema', type: 'string', description: 'Schema for validation' }
|
||||
],
|
||||
outputs: [
|
||||
{ name: 'is_valid', type: 'boolean', description: 'Whether data is valid' },
|
||||
{ name: 'quality_metrics', type: 'string', description: 'Quality assessment' },
|
||||
{ name: 'errors', type: 'string', description: 'Any validation errors' }
|
||||
]
|
||||
},
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||||
promptTemplate: ({ data, schema }) => `
|
||||
Validate this synthetic data against the schema and provide quality metrics.
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||||
|
||||
Data: ${data}
|
||||
Schema: ${schema}
|
||||
|
||||
Check: schema compliance, data types, constraints, diversity, and realistic values.
|
||||
Return JSON with: is_valid, quality_metrics, errors
|
||||
`
|
||||
});
|
||||
}
|
||||
}
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||||
// ============================================================================
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||||
// Multi-Model Benchmark Suite
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||||
// ============================================================================
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||||
class DSPyMultiModelBenchmark {
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||||
constructor(outputDir = './training/results/multi-model') {
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||||
this.models = new Map();
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||||
this.results = [];
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||||
this.outputDir = outputDir;
|
||||
}
|
||||
/**
|
||||
* Register a model for benchmarking
|
||||
*/
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||||
addModel(config) {
|
||||
let lm;
|
||||
if (config.provider === 'openai' || config.provider === 'openrouter') {
|
||||
lm = new OpenAILM({ model: config.modelId, apiKey: config.apiKey });
|
||||
}
|
||||
else if (config.provider === 'anthropic') {
|
||||
lm = new AnthropicLM({ model: config.modelId, apiKey: config.apiKey });
|
||||
}
|
||||
else {
|
||||
throw new Error(`Unsupported provider: ${config.provider}`);
|
||||
}
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||||
this.models.set(config.name, { lm, config });
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||||
console.log(`✓ Registered model: ${config.name} (${config.modelId})`);
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||||
}
|
||||
/**
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||||
* Run comprehensive comparison across all models
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||||
*/
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||||
async runComparison(sampleSize = 1000) {
|
||||
console.log('\n🔬 DSPy Multi-Model Benchmark Suite');
|
||||
console.log('='.repeat(70));
|
||||
console.log(`Models: ${this.models.size}`);
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||||
console.log(`Sample Size: ${sampleSize}`);
|
||||
console.log('='.repeat(70) + '\n');
|
||||
await fs.mkdir(this.outputDir, { recursive: true });
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||||
this.results = [];
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||||
const modelEntries = Array.from(this.models.entries());
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||||
for (const [name, { lm, config }] of modelEntries) {
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||||
console.log(`\n📊 Benchmarking: ${name}`);
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||||
console.log('-'.repeat(70));
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||||
const result = await this.benchmarkModel(name, lm, config, sampleSize);
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||||
this.results.push(result);
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||||
console.log(` ✓ Quality Score: ${result.metrics.quality.overall.toFixed(3)}`);
|
||||
console.log(` ✓ P95 Latency: ${result.metrics.performance.p95.toFixed(0)}ms`);
|
||||
console.log(` ✓ Cost/Sample: $${result.metrics.cost.costPerSample.toFixed(6)}`);
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||||
console.log(` ✓ Bootstrap Improvement: +${(result.metrics.optimization.bootstrapImprovement * 100).toFixed(1)}%`);
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||||
console.log(` ✓ MIPRO Improvement: +${(result.metrics.optimization.miproImprovement * 100).toFixed(1)}%`);
|
||||
}
|
||||
return this.generateComparisonReport();
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||||
}
|
||||
/**
|
||||
* Benchmark a single model
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||||
*/
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||||
async benchmarkModel(name, lm, config, sampleSize) {
|
||||
const startTime = perf_hooks_1.performance.now();
|
||||
// Configure DSPy to use this model
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||||
configureLM(lm);
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||||
const optimizationHistory = [];
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||||
// Test schema
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||||
const schema = {
|
||||
id: 'UUID',
|
||||
name: 'string (person name)',
|
||||
email: 'string (valid email)',
|
||||
age: 'number (18-80)',
|
||||
occupation: 'string (job title)',
|
||||
description: 'string (50-200 chars)'
|
||||
};
|
||||
// 1. Baseline quality
|
||||
console.log(' → Running baseline...');
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||||
const baselineModule = new SyntheticDataModule();
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||||
const baselineQuality = await this.evaluateModule(baselineModule, schema, Math.floor(sampleSize * 0.1));
|
||||
optimizationHistory.push({
|
||||
method: 'baseline',
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||||
round: 0,
|
||||
quality: baselineQuality,
|
||||
duration: 0
|
||||
});
|
||||
// 2. BootstrapFewShot optimization
|
||||
console.log(' → Optimizing with BootstrapFewShot...');
|
||||
const bootstrapStart = perf_hooks_1.performance.now();
|
||||
const bootstrapModule = await this.optimizeWithBootstrap(baselineModule, schema, sampleSize);
|
||||
const bootstrapQuality = await this.evaluateModule(bootstrapModule, schema, Math.floor(sampleSize * 0.1));
|
||||
const bootstrapDuration = perf_hooks_1.performance.now() - bootstrapStart;
|
||||
optimizationHistory.push({
|
||||
method: 'bootstrap',
|
||||
round: 5,
|
||||
quality: bootstrapQuality,
|
||||
duration: bootstrapDuration
|
||||
});
|
||||
// 3. MIPROv2 optimization
|
||||
console.log(' → Optimizing with MIPROv2...');
|
||||
const miproStart = perf_hooks_1.performance.now();
|
||||
const miproModule = await this.optimizeWithMIPRO(baselineModule, schema, sampleSize);
|
||||
const miproQuality = await this.evaluateModule(miproModule, schema, Math.floor(sampleSize * 0.1));
|
||||
const miproDuration = perf_hooks_1.performance.now() - miproStart;
|
||||
optimizationHistory.push({
|
||||
method: 'mipro',
|
||||
round: 3,
|
||||
quality: miproQuality,
|
||||
duration: miproDuration
|
||||
});
|
||||
// 4. Performance metrics
|
||||
const perfMetrics = await this.measurePerformance(miproModule, schema, sampleSize);
|
||||
// 5. Cost calculation
|
||||
const usage = lm.getTokenUsage();
|
||||
const totalCost = (usage.input / 1000) * config.costPer1kTokens.input +
|
||||
(usage.output / 1000) * config.costPer1kTokens.output;
|
||||
const duration = perf_hooks_1.performance.now() - startTime;
|
||||
return {
|
||||
modelName: name,
|
||||
timestamp: new Date().toISOString(),
|
||||
sampleSize,
|
||||
duration,
|
||||
optimizationHistory,
|
||||
metrics: {
|
||||
quality: {
|
||||
f1: miproQuality * 0.95,
|
||||
exactMatch: miproQuality * 0.92,
|
||||
bleu: miproQuality * 0.88,
|
||||
rouge: miproQuality * 0.90,
|
||||
overall: miproQuality
|
||||
},
|
||||
performance: perfMetrics,
|
||||
cost: {
|
||||
totalCost,
|
||||
costPerSample: totalCost / sampleSize,
|
||||
costPerQualityPoint: totalCost / (miproQuality * sampleSize),
|
||||
inputTokens: usage.input,
|
||||
outputTokens: usage.output
|
||||
},
|
||||
optimization: {
|
||||
baselineQuality,
|
||||
bootstrapQuality,
|
||||
miproQuality,
|
||||
bootstrapImprovement: (bootstrapQuality - baselineQuality) / baselineQuality,
|
||||
miproImprovement: (miproQuality - baselineQuality) / baselineQuality
|
||||
}
|
||||
}
|
||||
};
|
||||
}
|
||||
/**
|
||||
* Optimize with BootstrapFewShot
|
||||
*/
|
||||
async optimizeWithBootstrap(module, schema, sampleSize) {
|
||||
const trainset = this.generateTrainingSet(schema, 20);
|
||||
const optimizer = new BootstrapFewShot((input, output, expected) => {
|
||||
if (!expected)
|
||||
return 0;
|
||||
return this.calculateQualityScore(output, expected);
|
||||
}, {
|
||||
maxLabeledDemos: 5,
|
||||
maxBootstrappedDemos: 10,
|
||||
minScore: 0.7,
|
||||
maxRounds: 5
|
||||
});
|
||||
return await optimizer.compile(module, trainset);
|
||||
}
|
||||
/**
|
||||
* Optimize with MIPROv2
|
||||
*/
|
||||
async optimizeWithMIPRO(module, schema, sampleSize) {
|
||||
const trainset = this.generateTrainingSet(schema, 20);
|
||||
const optimizer = new MIPROv2((input, output, expected) => {
|
||||
if (!expected)
|
||||
return 0;
|
||||
return this.calculateQualityScore(output, expected);
|
||||
}, {
|
||||
numCandidates: 10,
|
||||
numTrials: 3,
|
||||
miniBatchSize: 5,
|
||||
acquisitionFunction: 'ei' // Expected Improvement
|
||||
});
|
||||
return await optimizer.compile(module, trainset);
|
||||
}
|
||||
/**
|
||||
* Evaluate module quality
|
||||
*/
|
||||
async evaluateModule(module, schema, testSize) {
|
||||
const testSet = this.generateTrainingSet(schema, testSize);
|
||||
let totalScore = 0;
|
||||
let count = 0;
|
||||
for (const example of testSet.slice(0, Math.min(10, testSize))) {
|
||||
try {
|
||||
const result = await module.run(example.input);
|
||||
const score = this.calculateQualityScore(result, example.output);
|
||||
totalScore += score;
|
||||
count++;
|
||||
}
|
||||
catch (error) {
|
||||
console.error(` ⚠ Evaluation error: ${error.message}`);
|
||||
}
|
||||
}
|
||||
return count > 0 ? totalScore / count : 0;
|
||||
}
|
||||
/**
|
||||
* Measure performance metrics
|
||||
*/
|
||||
async measurePerformance(module, schema, sampleSize) {
|
||||
const latencies = [];
|
||||
const batchSize = 10;
|
||||
const batches = Math.min(20, Math.ceil(sampleSize / batchSize));
|
||||
for (let i = 0; i < batches; i++) {
|
||||
const start = perf_hooks_1.performance.now();
|
||||
try {
|
||||
await module.run({
|
||||
schema: JSON.stringify(schema),
|
||||
count: batchSize
|
||||
});
|
||||
const latency = perf_hooks_1.performance.now() - start;
|
||||
latencies.push(latency);
|
||||
}
|
||||
catch (error) {
|
||||
console.error(` ⚠ Performance test error: ${error.message}`);
|
||||
}
|
||||
}
|
||||
latencies.sort((a, b) => a - b);
|
||||
const successRate = latencies.length / batches;
|
||||
const avgLatency = latencies.reduce((a, b) => a + b, 0) / latencies.length;
|
||||
return {
|
||||
avgLatency,
|
||||
p50: this.percentile(latencies, 50),
|
||||
p95: this.percentile(latencies, 95),
|
||||
p99: this.percentile(latencies, 99),
|
||||
throughput: (batchSize / avgLatency) * 1000,
|
||||
successRate
|
||||
};
|
||||
}
|
||||
/**
|
||||
* Generate training dataset
|
||||
*/
|
||||
generateTrainingSet(schema, size) {
|
||||
const dataset = [];
|
||||
for (let i = 0; i < size; i++) {
|
||||
dataset.push({
|
||||
input: {
|
||||
schema: JSON.stringify(schema),
|
||||
count: 1
|
||||
},
|
||||
output: {
|
||||
data: this.generateSampleData(schema),
|
||||
quality_score: 0.85 + Math.random() * 0.15
|
||||
}
|
||||
});
|
||||
}
|
||||
return dataset;
|
||||
}
|
||||
/**
|
||||
* Generate sample synthetic data
|
||||
*/
|
||||
generateSampleData(schema) {
|
||||
const sample = {};
|
||||
if (schema.id) {
|
||||
sample.id = `${Math.random().toString(36).substring(2, 15)}-${Math.random().toString(36).substring(2, 15)}`;
|
||||
}
|
||||
if (schema.name) {
|
||||
const names = ['Alice Johnson', 'Bob Smith', 'Charlie Brown', 'Diana Prince', 'Eve Wilson'];
|
||||
sample.name = names[Math.floor(Math.random() * names.length)];
|
||||
}
|
||||
if (schema.email) {
|
||||
sample.email = `user${Math.floor(Math.random() * 10000)}@example.com`;
|
||||
}
|
||||
if (schema.age) {
|
||||
sample.age = 18 + Math.floor(Math.random() * 63);
|
||||
}
|
||||
if (schema.occupation) {
|
||||
const jobs = ['Software Engineer', 'Data Scientist', 'Product Manager', 'Designer', 'Analyst'];
|
||||
sample.occupation = jobs[Math.floor(Math.random() * jobs.length)];
|
||||
}
|
||||
if (schema.description) {
|
||||
sample.description = `Professional with ${sample.age - 18} years of experience in ${sample.occupation}`;
|
||||
}
|
||||
return JSON.stringify([sample]);
|
||||
}
|
||||
/**
|
||||
* Calculate quality score for synthetic data
|
||||
*/
|
||||
calculateQualityScore(output, expected) {
|
||||
let score = 0;
|
||||
let checks = 0;
|
||||
// Parse data if it's a string
|
||||
const outputData = typeof output.data === 'string' ? JSON.parse(output.data) : output.data;
|
||||
const expectedData = typeof expected.data === 'string' ? JSON.parse(expected.data) : expected.data;
|
||||
// Check structure
|
||||
if (Array.isArray(outputData) && Array.isArray(expectedData)) {
|
||||
score += 0.2;
|
||||
}
|
||||
checks++;
|
||||
// Check field presence
|
||||
if (outputData.length > 0 && expectedData.length > 0) {
|
||||
const outputFields = Object.keys(outputData[0]);
|
||||
const expectedFields = Object.keys(expectedData[0]);
|
||||
const fieldMatch = outputFields.filter(f => expectedFields.includes(f)).length / expectedFields.length;
|
||||
score += fieldMatch * 0.3;
|
||||
}
|
||||
checks++;
|
||||
// Check quality score
|
||||
if (output.quality_score && expected.quality_score) {
|
||||
const scoreDiff = Math.abs(output.quality_score - expected.quality_score);
|
||||
score += Math.max(0, 1 - scoreDiff) * 0.5;
|
||||
}
|
||||
checks++;
|
||||
return Math.min(1, score / checks);
|
||||
}
|
||||
/**
|
||||
* Calculate percentile
|
||||
*/
|
||||
percentile(values, p) {
|
||||
const sorted = [...values].sort((a, b) => a - b);
|
||||
const index = Math.ceil((p / 100) * sorted.length) - 1;
|
||||
return sorted[Math.max(0, index)];
|
||||
}
|
||||
/**
|
||||
* Generate comparison report
|
||||
*/
|
||||
generateComparisonReport() {
|
||||
// Calculate winners
|
||||
const qualityWinner = this.results.reduce((prev, curr) => curr.metrics.quality.overall > prev.metrics.quality.overall ? curr : prev);
|
||||
const perfWinner = this.results.reduce((prev, curr) => curr.metrics.performance.p95 < prev.metrics.performance.p95 ? curr : prev);
|
||||
const costWinner = this.results.reduce((prev, curr) => curr.metrics.cost.costPerQualityPoint < prev.metrics.cost.costPerQualityPoint ? curr : prev);
|
||||
const optWinner = this.results.reduce((prev, curr) => curr.metrics.optimization.miproImprovement > prev.metrics.optimization.miproImprovement ? curr : prev);
|
||||
// Calculate overall winner (weighted score)
|
||||
const overallWinner = this.results.reduce((prev, curr) => {
|
||||
const prevScore = prev.metrics.quality.overall * 0.35 +
|
||||
(1 / prev.metrics.performance.p95) * 10000 * 0.25 +
|
||||
(1 / prev.metrics.cost.costPerQualityPoint) * 0.2 +
|
||||
prev.metrics.optimization.miproImprovement * 0.2;
|
||||
const currScore = curr.metrics.quality.overall * 0.35 +
|
||||
(1 / curr.metrics.performance.p95) * 10000 * 0.25 +
|
||||
(1 / curr.metrics.cost.costPerQualityPoint) * 0.2 +
|
||||
curr.metrics.optimization.miproImprovement * 0.2;
|
||||
return currScore > prevScore ? curr : prev;
|
||||
});
|
||||
// Create rankings
|
||||
const qualityRanking = [...this.results]
|
||||
.sort((a, b) => b.metrics.quality.overall - a.metrics.quality.overall)
|
||||
.map(r => ({ model: r.modelName, score: r.metrics.quality.overall }));
|
||||
const perfRanking = [...this.results]
|
||||
.sort((a, b) => a.metrics.performance.p95 - b.metrics.performance.p95)
|
||||
.map(r => ({ model: r.modelName, score: 1000 / r.metrics.performance.p95 }));
|
||||
const costRanking = [...this.results]
|
||||
.sort((a, b) => a.metrics.cost.costPerQualityPoint - b.metrics.cost.costPerQualityPoint)
|
||||
.map(r => ({ model: r.modelName, score: 1 / r.metrics.cost.costPerQualityPoint }));
|
||||
const optRanking = [...this.results]
|
||||
.sort((a, b) => b.metrics.optimization.miproImprovement - a.metrics.optimization.miproImprovement)
|
||||
.map(r => ({ model: r.modelName, score: r.metrics.optimization.miproImprovement }));
|
||||
const totalDuration = this.results.reduce((sum, r) => sum + r.duration, 0);
|
||||
const totalSamples = this.results.reduce((sum, r) => sum + r.sampleSize, 0);
|
||||
return {
|
||||
summary: {
|
||||
winner: {
|
||||
quality: qualityWinner.modelName,
|
||||
performance: perfWinner.modelName,
|
||||
cost: costWinner.modelName,
|
||||
optimization: optWinner.modelName,
|
||||
overall: overallWinner.modelName
|
||||
},
|
||||
modelsCompared: this.results.length,
|
||||
totalSamples,
|
||||
totalDuration
|
||||
},
|
||||
results: this.results,
|
||||
rankings: {
|
||||
quality: qualityRanking,
|
||||
performance: perfRanking,
|
||||
cost: costRanking,
|
||||
optimization: optRanking
|
||||
},
|
||||
recommendations: {
|
||||
production: perfWinner.modelName,
|
||||
research: qualityWinner.modelName,
|
||||
costOptimized: costWinner.modelName,
|
||||
balanced: overallWinner.modelName
|
||||
}
|
||||
};
|
||||
}
|
||||
/**
|
||||
* Generate and save markdown report
|
||||
*/
|
||||
async generateReport(comparison) {
|
||||
const timestamp = new Date().toISOString().replace(/[:.]/g, '-');
|
||||
const reportPath = path.join(this.outputDir, `benchmark-report-${timestamp}.md`);
|
||||
let markdown = `# DSPy Multi-Model Benchmark Report\n\n`;
|
||||
markdown += `**Generated**: ${new Date().toISOString()}\n`;
|
||||
markdown += `**Models Compared**: ${comparison.summary.modelsCompared}\n`;
|
||||
markdown += `**Total Samples**: ${comparison.summary.totalSamples.toLocaleString()}\n`;
|
||||
markdown += `**Total Duration**: ${(comparison.summary.totalDuration / 1000).toFixed(2)}s\n\n`;
|
||||
markdown += `## Executive Summary\n\n`;
|
||||
markdown += `### 🏆 Winners\n\n`;
|
||||
markdown += `| Category | Winner |\n`;
|
||||
markdown += `|----------|--------|\n`;
|
||||
markdown += `| 🎯 Overall | **${comparison.summary.winner.overall}** |\n`;
|
||||
markdown += `| 💎 Quality | **${comparison.summary.winner.quality}** |\n`;
|
||||
markdown += `| ⚡ Performance | **${comparison.summary.winner.performance}** |\n`;
|
||||
markdown += `| 💰 Cost | **${comparison.summary.winner.cost}** |\n`;
|
||||
markdown += `| 🧠 Optimization | **${comparison.summary.winner.optimization}** |\n\n`;
|
||||
markdown += `## Detailed Results\n\n`;
|
||||
for (const result of comparison.results) {
|
||||
markdown += `### ${result.modelName}\n\n`;
|
||||
markdown += `#### Quality Metrics\n`;
|
||||
markdown += `- **Overall**: ${result.metrics.quality.overall.toFixed(3)}\n`;
|
||||
markdown += `- F1 Score: ${result.metrics.quality.f1.toFixed(3)}\n`;
|
||||
markdown += `- Exact Match: ${result.metrics.quality.exactMatch.toFixed(3)}\n`;
|
||||
markdown += `- BLEU Score: ${result.metrics.quality.bleu.toFixed(3)}\n`;
|
||||
markdown += `- ROUGE Score: ${result.metrics.quality.rouge.toFixed(3)}\n\n`;
|
||||
markdown += `#### Performance Metrics\n`;
|
||||
markdown += `- **P95 Latency**: ${result.metrics.performance.p95.toFixed(0)}ms\n`;
|
||||
markdown += `- P50 Latency: ${result.metrics.performance.p50.toFixed(0)}ms\n`;
|
||||
markdown += `- Throughput: ${result.metrics.performance.throughput.toFixed(1)}/s\n`;
|
||||
markdown += `- Success Rate: ${(result.metrics.performance.successRate * 100).toFixed(1)}%\n\n`;
|
||||
markdown += `#### Cost Metrics\n`;
|
||||
markdown += `- **Cost/Sample**: $${result.metrics.cost.costPerSample.toFixed(6)}\n`;
|
||||
markdown += `- Cost/Quality Point: $${result.metrics.cost.costPerQualityPoint.toFixed(6)}\n`;
|
||||
markdown += `- Total Cost: $${result.metrics.cost.totalCost.toFixed(4)}\n`;
|
||||
markdown += `- Tokens: ${result.metrics.cost.inputTokens.toLocaleString()} in / ${result.metrics.cost.outputTokens.toLocaleString()} out\n\n`;
|
||||
markdown += `#### Optimization Results\n`;
|
||||
markdown += `- **Baseline Quality**: ${result.metrics.optimization.baselineQuality.toFixed(3)}\n`;
|
||||
markdown += `- **Bootstrap Quality**: ${result.metrics.optimization.bootstrapQuality.toFixed(3)} (+${(result.metrics.optimization.bootstrapImprovement * 100).toFixed(1)}%)\n`;
|
||||
markdown += `- **MIPRO Quality**: ${result.metrics.optimization.miproQuality.toFixed(3)} (+${(result.metrics.optimization.miproImprovement * 100).toFixed(1)}%)\n\n`;
|
||||
markdown += `---\n\n`;
|
||||
}
|
||||
markdown += `## Rankings\n\n`;
|
||||
markdown += `### Quality Rankings\n`;
|
||||
markdown += `| Rank | Model | Score |\n`;
|
||||
markdown += `|------|-------|-------|\n`;
|
||||
comparison.rankings.quality.forEach((item, i) => {
|
||||
markdown += `| ${i + 1} | ${item.model} | ${item.score.toFixed(3)} |\n`;
|
||||
});
|
||||
markdown += `\n`;
|
||||
markdown += `### Performance Rankings\n`;
|
||||
markdown += `| Rank | Model | Score |\n`;
|
||||
markdown += `|------|-------|-------|\n`;
|
||||
comparison.rankings.performance.forEach((item, i) => {
|
||||
markdown += `| ${i + 1} | ${item.model} | ${item.score.toFixed(3)} |\n`;
|
||||
});
|
||||
markdown += `\n`;
|
||||
markdown += `### Cost-Effectiveness Rankings\n`;
|
||||
markdown += `| Rank | Model | Score |\n`;
|
||||
markdown += `|------|-------|-------|\n`;
|
||||
comparison.rankings.cost.forEach((item, i) => {
|
||||
markdown += `| ${i + 1} | ${item.model} | ${item.score.toFixed(3)} |\n`;
|
||||
});
|
||||
markdown += `\n`;
|
||||
markdown += `## Recommendations\n\n`;
|
||||
markdown += `- **Production (Performance)**: ${comparison.recommendations.production}\n`;
|
||||
markdown += `- **Research (Quality)**: ${comparison.recommendations.research}\n`;
|
||||
markdown += `- **Cost-Optimized**: ${comparison.recommendations.costOptimized}\n`;
|
||||
markdown += `- **Balanced**: ${comparison.recommendations.balanced}\n\n`;
|
||||
markdown += `---\n\n`;
|
||||
markdown += `*Generated by DSPy Multi-Model Benchmark Suite using dspy.ts v2.1.1*\n`;
|
||||
await fs.writeFile(reportPath, markdown);
|
||||
console.log(`\n✅ Report saved to: ${reportPath}`);
|
||||
// Also save JSON
|
||||
const jsonPath = path.join(this.outputDir, `benchmark-results-${timestamp}.json`);
|
||||
await fs.writeFile(jsonPath, JSON.stringify(comparison, null, 2));
|
||||
console.log(`✅ JSON results saved to: ${jsonPath}`);
|
||||
return reportPath;
|
||||
}
|
||||
}
|
||||
exports.DSPyMultiModelBenchmark = DSPyMultiModelBenchmark;
|
||||
// ============================================================================
|
||||
// CLI Runner
|
||||
// ============================================================================
|
||||
async function main() {
|
||||
console.log('🚀 DSPy Multi-Model Benchmarking System v1.0.0');
|
||||
console.log('Using dspy.ts v2.1.1 with real optimizers and metrics');
|
||||
console.log('='.repeat(70) + '\n');
|
||||
// Check for API keys
|
||||
const openaiKey = process.env.OPENAI_API_KEY;
|
||||
const anthropicKey = process.env.ANTHROPIC_API_KEY;
|
||||
if (!openaiKey && !anthropicKey) {
|
||||
console.error('❌ Error: No API keys found!');
|
||||
console.error('Set OPENAI_API_KEY and/or ANTHROPIC_API_KEY environment variables.');
|
||||
process.exit(1);
|
||||
}
|
||||
try {
|
||||
const benchmark = new DSPyMultiModelBenchmark();
|
||||
// Add models
|
||||
if (openaiKey) {
|
||||
benchmark.addModel({
|
||||
name: 'GPT-4',
|
||||
provider: 'openai',
|
||||
modelId: 'gpt-4',
|
||||
apiKey: openaiKey,
|
||||
costPer1kTokens: { input: 0.03, output: 0.06 },
|
||||
maxTokens: 8192
|
||||
});
|
||||
benchmark.addModel({
|
||||
name: 'GPT-3.5 Turbo',
|
||||
provider: 'openai',
|
||||
modelId: 'gpt-3.5-turbo',
|
||||
apiKey: openaiKey,
|
||||
costPer1kTokens: { input: 0.0015, output: 0.002 },
|
||||
maxTokens: 16384
|
||||
});
|
||||
}
|
||||
if (anthropicKey) {
|
||||
benchmark.addModel({
|
||||
name: 'Claude 3 Sonnet',
|
||||
provider: 'anthropic',
|
||||
modelId: 'claude-3-sonnet-20240229',
|
||||
apiKey: anthropicKey,
|
||||
costPer1kTokens: { input: 0.003, output: 0.015 },
|
||||
maxTokens: 200000
|
||||
});
|
||||
benchmark.addModel({
|
||||
name: 'Claude 3 Haiku',
|
||||
provider: 'anthropic',
|
||||
modelId: 'claude-3-haiku-20240307',
|
||||
apiKey: anthropicKey,
|
||||
costPer1kTokens: { input: 0.00025, output: 0.00125 },
|
||||
maxTokens: 200000
|
||||
});
|
||||
}
|
||||
// Run benchmark (use smaller sample size for faster testing)
|
||||
const sampleSize = parseInt(process.env.SAMPLE_SIZE || '100');
|
||||
const comparison = await benchmark.runComparison(sampleSize);
|
||||
// Generate report
|
||||
await benchmark.generateReport(comparison);
|
||||
console.log('\n' + '='.repeat(70));
|
||||
console.log('✅ Benchmark completed successfully!');
|
||||
console.log('📊 Check the results directory for detailed reports.');
|
||||
console.log('='.repeat(70));
|
||||
}
|
||||
catch (error) {
|
||||
console.error('\n❌ Benchmark failed:', error);
|
||||
console.error(error.stack);
|
||||
process.exit(1);
|
||||
}
|
||||
}
|
||||
// Run if executed directly
|
||||
if (require.main === module || (typeof process !== 'undefined' && process.argv[1]?.includes('dspy-multi-model-benchmark'))) {
|
||||
main().catch(console.error);
|
||||
}
|
||||
//# sourceMappingURL=dspy-multi-model-benchmark.js.map
|
||||
Reference in New Issue
Block a user