Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'

vendor/ruvector/npm/packages/agentic-synth-examples/bin/cli.js

@@ -0,0 +1,155 @@
+#!/usr/bin/env node
+
+/**
+ * Agentic Synth Examples CLI
+ * Run production-ready examples directly
+ */
+
+import { Command } from 'commander';
+
+const program = new Command();
+
+program
+  .name('agentic-synth-examples')
+  .description('Production-ready examples for @ruvector/agentic-synth')
+  .version('0.1.0')
+  .addHelpText('after', `
+Examples:
+  $ agentic-synth-examples dspy train --models gemini,claude
+  $ agentic-synth-examples self-learn --task code-generation
+  $ agentic-synth-examples generate --type stock-market
+  $ agentic-synth-examples list
+
+Available Examples:
+  dspy          - Multi-model DSPy training and benchmarking
+  self-learn    - Self-learning and adaptive systems
+  stock-market  - Financial market simulation
+  cicd          - CI/CD pipeline test data
+  security      - Security testing scenarios
+  ad-roas       - Marketing campaign optimization
+  swarm         - Multi-agent swarm coordination
+  jujutsu       - Agentic-jujutsu version control
+
+Learn more:
+  https://www.npmjs.com/package/@ruvector/agentic-synth-examples
+  https://github.com/ruvnet/ruvector/tree/main/packages/agentic-synth-examples
+`);
+
+program
+  .command('list')
+  .description('List all available examples')
+  .action(() => {
+    console.log(`
+📚 Available Examples for @ruvector/agentic-synth
+
+🧠 Machine Learning & AI:
+  • dspy              - Multi-model DSPy training with optimization
+  • self-learn        - Self-learning systems that improve over time
+  • prompt-engineering - Automatic prompt optimization
+  • model-benchmark   - Compare different AI models
+
+💼 Business & Analytics:
+  • ad-roas           - Marketing campaign optimization
+  • employee-perf     - HR and workforce simulation
+  • customer-analytics - User behavior and segmentation
+  • revenue-forecast  - Financial prediction data
+
+💰 Finance & Trading:
+  • stock-market      - Realistic stock market data
+  • crypto-trading    - Cryptocurrency market simulation
+  • risk-analysis     - Financial risk scenarios
+  • portfolio-opt     - Investment strategy data
+
+🔒 Security & Testing:
+  • security          - Penetration testing scenarios
+  • log-analytics     - Security and monitoring logs
+  • anomaly-detection - Unusual pattern generation
+  • vulnerability     - Security test cases
+
+🚀 DevOps & CI/CD:
+  • cicd              - Pipeline testing data
+  • deployment        - Release testing data
+  • performance       - Load and stress test data
+  • monitoring        - Alert and incident data
+
+🤖 Agentic Systems:
+  • swarm             - Multi-agent orchestration
+  • agent-memory      - Context and memory patterns
+  • jujutsu           - Version control for AI
+  • distributed       - Federated learning examples
+
+Usage:
+  $ agentic-synth-examples <command> [options]
+  $ agentic-synth-examples dspy train --models gemini
+  $ agentic-synth-examples stock-market --count 1000
+
+For more information:
+  $ agentic-synth-examples <command> --help
+`);
+  });
+
+program
+  .command('dspy')
+  .description('DSPy multi-model training and optimization')
+  .argument('[subcommand]', 'train, benchmark, or optimize')
+  .option('-m, --models <models>', 'Comma-separated model providers')
+  .option('-r, --rounds <number>', 'Optimization rounds', '5')
+  .option('-c, --convergence <number>', 'Quality threshold', '0.95')
+  .option('-o, --output <path>', 'Output file path')
+  .action((subcommand, options) => {
+    console.log('🧠 DSPy Multi-Model Training\n');
+    console.log('This example demonstrates training multiple AI models');
+    console.log('with automatic prompt optimization using DSPy.ts.\n');
+    console.log('Configuration:');
+    console.log(`  Models: ${options.models || 'gemini,claude,gpt4'}`);
+    console.log(`  Rounds: ${options.rounds}`);
+    console.log(`  Convergence: ${options.convergence}`);
+    console.log('\n⚠️  Note: Full implementation coming in v0.2.0');
+    console.log('For now, see the source code in training/dspy-learning-session.ts');
+  });
+
+program
+  .command('self-learn')
+  .description('Self-learning adaptive generation systems')
+  .option('-t, --task <task>', 'Task type (code-generation, text-summary, etc.)')
+  .option('-i, --iterations <number>', 'Learning iterations', '10')
+  .option('-l, --learning-rate <rate>', 'Learning rate', '0.1')
+  .action((options) => {
+    console.log('🔄 Self-Learning System\n');
+    console.log('This example shows how to build systems that improve');
+    console.log('their output quality automatically through feedback loops.\n');
+    console.log('Configuration:');
+    console.log(`  Task: ${options.task || 'general'}`);
+    console.log(`  Iterations: ${options.iterations}`);
+    console.log(`  Learning Rate: ${options.learningRate}`);
+    console.log('\n⚠️  Note: Full implementation coming in v0.2.0');
+  });
+
+program
+  .command('generate')
+  .description('Generate example synthetic data')
+  .option('-t, --type <type>', 'Data type (stock-market, cicd, security, etc.)')
+  .option('-c, --count <number>', 'Number of records', '100')
+  .option('-o, --output <path>', 'Output file path')
+  .action((options) => {
+    console.log(`📊 Generating ${options.type || 'generic'} data\n`);
+    console.log(`Count: ${options.count} records`);
+    if (options.output) {
+      console.log(`Output: ${options.output}`);
+    }
+    console.log('\n⚠️  Note: Full implementation coming in v0.2.0');
+    console.log('Use the main @ruvector/agentic-synth package for generation now.');
+  });
+
+// Error handler for unknown commands
+program.on('command:*', function () {
+  console.error('Invalid command: %s\nSee --help for a list of available commands.', program.args.join(' '));
+  process.exit(1);
+});
+
+// Show help if no command provided
+if (process.argv.length === 2) {
+  program.help();
+}
+
+program.parse();