chore: enforce consistent coding style and semicolon removal
- Update ESLint configuration to enforce no semicolons (`semi: ['error', 'never']`) - Fix all instances of semicolons in `*.ts` files to align with style rules - Adjust related ESLint rules for `no-extra-semi` - Simplify unnecessary semicolon patterns in global variable assignments
This commit is contained in:
parent
cfd74adcb3
commit
797839c135
5 changed files with 207 additions and 207 deletions
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@ -4,13 +4,13 @@
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* CLI interface for semantic similarity, clustering, and neural operations
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*/
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import inquirer from 'inquirer';
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import chalk from 'chalk';
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import ora from 'ora';
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import fs from 'node:fs';
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import path from 'node:path';
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import { Brainy } from '../../brainyData.js';
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import { NeuralAPI } from '../../neural/neuralAPI.js';
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import inquirer from 'inquirer'
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import chalk from 'chalk'
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import ora from 'ora'
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import fs from 'node:fs'
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import path from 'node:path'
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import { Brainy } from '../../brainyData.js'
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import { NeuralAPI } from '../../neural/neuralAPI.js'
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interface CommandArguments {
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action?: string;
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@ -90,52 +90,52 @@ export const neuralCommand = {
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type: 'boolean',
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default: false,
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alias: 'e'
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});
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})
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},
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handler: async (argv: CommandArguments) => {
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console.log(chalk.cyan('\n🧠 NEURAL SIMILARITY API'));
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console.log(chalk.gray('━'.repeat(50)));
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console.log(chalk.cyan('\n🧠 NEURAL SIMILARITY API'))
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console.log(chalk.gray('━'.repeat(50)))
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// Initialize Brainy and Neural API
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const brain = new Brainy();
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const neural = new NeuralAPI(brain);
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const brain = new Brainy()
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const neural = new NeuralAPI(brain)
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try {
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const action = argv.action || await promptForAction();
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const action = argv.action || await promptForAction()
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switch (action) {
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case 'similar':
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await handleSimilarCommand(neural, argv);
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break;
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await handleSimilarCommand(neural, argv)
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break
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case 'clusters':
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await handleClustersCommand(neural, argv);
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break;
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await handleClustersCommand(neural, argv)
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break
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case 'hierarchy':
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await handleHierarchyCommand(neural, argv);
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break;
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await handleHierarchyCommand(neural, argv)
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break
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case 'neighbors':
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await handleNeighborsCommand(neural, argv);
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break;
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await handleNeighborsCommand(neural, argv)
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break
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case 'path':
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await handlePathCommand(neural, argv);
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break;
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await handlePathCommand(neural, argv)
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break
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case 'outliers':
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await handleOutliersCommand(neural, argv);
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break;
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await handleOutliersCommand(neural, argv)
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break
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case 'visualize':
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await handleVisualizeCommand(neural, argv);
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break;
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await handleVisualizeCommand(neural, argv)
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break
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default:
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console.log(chalk.red(`❌ Unknown action: ${action}`));
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showHelp();
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console.log(chalk.red(`❌ Unknown action: ${action}`))
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showHelp()
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}
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} catch (error) {
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console.error(chalk.red('💥 Error:'), error instanceof Error ? error.message : error);
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process.exit(1);
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console.error(chalk.red('💥 Error:'), error instanceof Error ? error.message : error)
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process.exit(1)
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}
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}
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};
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}
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async function promptForAction(): Promise<string> {
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const answer = await inquirer.prompt([{
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@ -151,25 +151,25 @@ async function promptForAction(): Promise<string> {
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{ name: '🚨 Detect outliers', value: 'outliers' },
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{ name: '📊 Generate visualization data', value: 'visualize' }
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]
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}]);
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}])
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return answer.action;
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return answer.action
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}
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async function handleSimilarCommand(neural: NeuralAPI, argv: CommandArguments): Promise<void> {
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const spinner = ora('🧠 Calculating semantic similarity...').start();
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const spinner = ora('🧠 Calculating semantic similarity...').start()
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try {
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let itemA: string, itemB: string;
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let itemA: string, itemB: string
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if (argv.id && argv.query) {
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itemA = argv.id;
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itemB = argv.query;
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itemA = argv.id
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itemB = argv.query
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} else if (argv._ && argv._.length >= 3) {
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itemA = argv._[1];
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itemB = argv._[2];
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itemA = argv._[1]
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itemB = argv._[2]
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} else {
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spinner.stop();
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spinner.stop()
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const answers = await inquirer.prompt([
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{
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type: 'input',
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@ -183,179 +183,179 @@ async function handleSimilarCommand(neural: NeuralAPI, argv: CommandArguments):
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message: 'Second item (ID or text):',
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validate: (input: string) => input.length > 0
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}
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]);
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itemA = answers.itemA;
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itemB = answers.itemB;
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spinner.start();
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])
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itemA = answers.itemA
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itemB = answers.itemB
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spinner.start()
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}
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const result = await neural.similar(itemA, itemB, {
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explain: argv.explain,
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includeBreakdown: argv.explain
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});
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})
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spinner.succeed('✅ Similarity calculated');
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spinner.succeed('✅ Similarity calculated')
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if (typeof result === 'number') {
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console.log(`\n🔗 Similarity: ${chalk.cyan((result * 100).toFixed(1))}%`);
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console.log(`\n🔗 Similarity: ${chalk.cyan((result * 100).toFixed(1))}%`)
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} else {
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console.log(`\n🔗 Similarity: ${chalk.cyan((result.score * 100).toFixed(1))}%`);
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console.log(`\n🔗 Similarity: ${chalk.cyan((result.score * 100).toFixed(1))}%`)
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if (result.explanation) {
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console.log(`💭 Explanation: ${result.explanation}`);
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console.log(`💭 Explanation: ${result.explanation}`)
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}
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if (result.breakdown) {
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console.log('\n📊 Breakdown:');
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console.log(` Semantic: ${chalk.yellow((result.breakdown.semantic! * 100).toFixed(1))}%`);
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console.log('\n📊 Breakdown:')
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console.log(` Semantic: ${chalk.yellow((result.breakdown.semantic! * 100).toFixed(1))}%`)
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if (result.breakdown.taxonomic !== undefined) {
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console.log(` Taxonomic: ${chalk.yellow((result.breakdown.taxonomic * 100).toFixed(1))}%`);
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console.log(` Taxonomic: ${chalk.yellow((result.breakdown.taxonomic * 100).toFixed(1))}%`)
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}
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if (result.breakdown.contextual !== undefined) {
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console.log(` Contextual: ${chalk.yellow((result.breakdown.contextual * 100).toFixed(1))}%`);
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console.log(` Contextual: ${chalk.yellow((result.breakdown.contextual * 100).toFixed(1))}%`)
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}
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}
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if (result.hierarchy) {
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console.log(`\n🌳 Hierarchy: ${result.hierarchy.sharedParent ?
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`Shared parent at distance ${result.hierarchy.distance}` :
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'No shared parent found'}`);
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'No shared parent found'}`)
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}
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}
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if (argv.output) {
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await saveToFile(argv.output, result, argv.format!);
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await saveToFile(argv.output, result, argv.format!)
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}
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} catch (error) {
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spinner.fail('💥 Failed to calculate similarity');
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throw error;
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spinner.fail('💥 Failed to calculate similarity')
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throw error
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}
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}
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async function handleClustersCommand(neural: NeuralAPI, argv: CommandArguments): Promise<void> {
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const spinner = ora('🎯 Finding semantic clusters...').start();
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const spinner = ora('🎯 Finding semantic clusters...').start()
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try {
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const options = {
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algorithm: argv.algorithm as any,
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threshold: argv.threshold,
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maxClusters: argv.limit
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};
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}
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const clusters = await neural.clusters(argv.query || options);
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const clusters = await neural.clusters(argv.query || options)
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spinner.succeed(`✅ Found ${clusters.length} clusters`);
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spinner.succeed(`✅ Found ${clusters.length} clusters`)
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if (argv.format === 'json') {
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console.log(JSON.stringify(clusters, null, 2));
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console.log(JSON.stringify(clusters, null, 2))
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} else {
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console.log(`\n🎯 ${chalk.cyan(clusters.length)} Semantic Clusters:\n`);
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console.log(`\n🎯 ${chalk.cyan(clusters.length)} Semantic Clusters:\n`)
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clusters.forEach((cluster, index) => {
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console.log(`${chalk.yellow(`Cluster ${index + 1}:`)} ${cluster.label || cluster.id}`);
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console.log(` 📊 Confidence: ${chalk.green((cluster.confidence * 100).toFixed(1))}%`);
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console.log(` 👥 Members: ${cluster.members.length}`);
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console.log(`${chalk.yellow(`Cluster ${index + 1}:`)} ${cluster.label || cluster.id}`)
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console.log(` 📊 Confidence: ${chalk.green((cluster.confidence * 100).toFixed(1))}%`)
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console.log(` 👥 Members: ${cluster.members.length}`)
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if (cluster.members.length <= 5) {
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cluster.members.forEach(member => {
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console.log(` • ${member}`);
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});
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console.log(` • ${member}`)
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})
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} else {
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cluster.members.slice(0, 3).forEach(member => {
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console.log(` • ${member}`);
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});
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console.log(` ... and ${cluster.members.length - 3} more`);
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console.log(` • ${member}`)
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})
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console.log(` ... and ${cluster.members.length - 3} more`)
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}
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console.log();
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});
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console.log()
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})
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}
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if (argv.output) {
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await saveToFile(argv.output, clusters, argv.format!);
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await saveToFile(argv.output, clusters, argv.format!)
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}
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} catch (error) {
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spinner.fail('💥 Failed to find clusters');
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throw error;
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spinner.fail('💥 Failed to find clusters')
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throw error
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}
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}
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async function handleHierarchyCommand(neural: NeuralAPI, argv: CommandArguments): Promise<void> {
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const spinner = ora('🌳 Building semantic hierarchy...').start();
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const spinner = ora('🌳 Building semantic hierarchy...').start()
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try {
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const id = argv.id || argv._[1];
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const id = argv.id || argv._[1]
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if (!id) {
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spinner.stop();
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spinner.stop()
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const answer = await inquirer.prompt([{
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type: 'input',
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name: 'id',
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message: 'Enter item ID:',
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validate: (input: string) => input.length > 0
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}]);
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spinner.start();
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const hierarchy = await neural.hierarchy(answer.id);
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displayHierarchy(hierarchy);
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}])
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spinner.start()
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const hierarchy = await neural.hierarchy(answer.id)
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displayHierarchy(hierarchy)
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} else {
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const hierarchy = await neural.hierarchy(id);
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spinner.succeed('✅ Hierarchy built');
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displayHierarchy(hierarchy);
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const hierarchy = await neural.hierarchy(id)
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spinner.succeed('✅ Hierarchy built')
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displayHierarchy(hierarchy)
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}
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if (argv.output) {
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const hierarchy = await neural.hierarchy(id || argv._[1]);
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await saveToFile(argv.output, hierarchy, argv.format!);
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const hierarchy = await neural.hierarchy(id || argv._[1])
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await saveToFile(argv.output, hierarchy, argv.format!)
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}
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} catch (error) {
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spinner.fail('💥 Failed to build hierarchy');
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throw error;
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spinner.fail('💥 Failed to build hierarchy')
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throw error
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}
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}
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function displayHierarchy(hierarchy: any): void {
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console.log(`\n🌳 Semantic Hierarchy for ${chalk.cyan(hierarchy.self.id)}:`);
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console.log(`\n🌳 Semantic Hierarchy for ${chalk.cyan(hierarchy.self.id)}:`)
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if (hierarchy.root) {
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console.log(`🔝 Root: ${hierarchy.root.id} (${(hierarchy.root.similarity * 100).toFixed(1)}%)`);
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console.log(`🔝 Root: ${hierarchy.root.id} (${(hierarchy.root.similarity * 100).toFixed(1)}%)`)
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}
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if (hierarchy.grandparent) {
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console.log(`👴 Grandparent: ${hierarchy.grandparent.id} (${(hierarchy.grandparent.similarity * 100).toFixed(1)}%)`);
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console.log(`👴 Grandparent: ${hierarchy.grandparent.id} (${(hierarchy.grandparent.similarity * 100).toFixed(1)}%)`)
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}
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if (hierarchy.parent) {
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console.log(`👨 Parent: ${hierarchy.parent.id} (${(hierarchy.parent.similarity * 100).toFixed(1)}%)`);
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console.log(`👨 Parent: ${hierarchy.parent.id} (${(hierarchy.parent.similarity * 100).toFixed(1)}%)`)
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}
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console.log(`🎯 ${chalk.bold('Self:')} ${hierarchy.self.id}`);
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console.log(`🎯 ${chalk.bold('Self:')} ${hierarchy.self.id}`)
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if (hierarchy.siblings && hierarchy.siblings.length > 0) {
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console.log(`👥 Siblings: ${hierarchy.siblings.length}`);
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console.log(`👥 Siblings: ${hierarchy.siblings.length}`)
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hierarchy.siblings.forEach((sibling: any) => {
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console.log(` • ${sibling.id} (${(sibling.similarity * 100).toFixed(1)}%)`);
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});
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console.log(` • ${sibling.id} (${(sibling.similarity * 100).toFixed(1)}%)`)
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})
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}
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if (hierarchy.children && hierarchy.children.length > 0) {
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console.log(`👶 Children: ${hierarchy.children.length}`);
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console.log(`👶 Children: ${hierarchy.children.length}`)
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hierarchy.children.forEach((child: any) => {
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console.log(` • ${child.id} (${(child.similarity * 100).toFixed(1)}%)`);
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});
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console.log(` • ${child.id} (${(child.similarity * 100).toFixed(1)}%)`)
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})
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}
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}
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async function handleNeighborsCommand(neural: NeuralAPI, argv: CommandArguments): Promise<void> {
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const spinner = ora('🕸️ Finding semantic neighbors...').start();
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const spinner = ora('🕸️ Finding semantic neighbors...').start()
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try {
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const id = argv.id || argv._[1];
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const id = argv.id || argv._[1]
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if (!id) {
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spinner.stop();
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spinner.stop()
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const answer = await inquirer.prompt([{
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type: 'input',
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name: 'id',
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message: 'Enter item ID:',
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validate: (input: string) => input.length > 0
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}]);
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spinner.start();
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}])
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spinner.start()
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}
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const targetId = id || (await inquirer.prompt([{
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@ -363,52 +363,52 @@ async function handleNeighborsCommand(neural: NeuralAPI, argv: CommandArguments)
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name: 'id',
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message: 'Enter item ID:',
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validate: (input: string) => input.length > 0
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}])).id;
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}])).id
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const graph = await neural.neighbors(targetId, {
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limit: argv.limit,
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includeEdges: true
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});
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})
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spinner.succeed(`✅ Found ${graph.neighbors.length} neighbors`);
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spinner.succeed(`✅ Found ${graph.neighbors.length} neighbors`)
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console.log(`\n🕸️ Neighbors of ${chalk.cyan(graph.center)}:`);
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console.log(`\n🕸️ Neighbors of ${chalk.cyan(graph.center)}:`)
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graph.neighbors.forEach((neighbor, index) => {
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console.log(`${index + 1}. ${neighbor.id} (${(neighbor.similarity * 100).toFixed(1)}%)`);
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console.log(`${index + 1}. ${neighbor.id} (${(neighbor.similarity * 100).toFixed(1)}%)`)
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if (neighbor.type) {
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console.log(` Type: ${neighbor.type}`);
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console.log(` Type: ${neighbor.type}`)
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}
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if (neighbor.connections) {
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console.log(` Connections: ${neighbor.connections}`);
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console.log(` Connections: ${neighbor.connections}`)
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}
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});
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})
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if (graph.edges && graph.edges.length > 0) {
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console.log(`\n🔗 ${graph.edges.length} semantic connections found`);
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console.log(`\n🔗 ${graph.edges.length} semantic connections found`)
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}
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if (argv.output) {
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await saveToFile(argv.output, graph, argv.format!);
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await saveToFile(argv.output, graph, argv.format!)
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}
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} catch (error) {
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spinner.fail('💥 Failed to find neighbors');
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throw error;
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spinner.fail('💥 Failed to find neighbors')
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throw error
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}
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}
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async function handlePathCommand(neural: NeuralAPI, argv: CommandArguments): Promise<void> {
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const spinner = ora('🛣️ Finding semantic path...').start();
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const spinner = ora('🛣️ Finding semantic path...').start()
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try {
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let fromId: string, toId: string;
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let fromId: string, toId: string
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if (argv._ && argv._.length >= 3) {
|
||||
fromId = argv._[1];
|
||||
toId = argv._[2];
|
||||
fromId = argv._[1]
|
||||
toId = argv._[2]
|
||||
} else {
|
||||
spinner.stop();
|
||||
spinner.stop()
|
||||
const answers = await inquirer.prompt([
|
||||
{
|
||||
type: 'input',
|
||||
|
|
@ -422,156 +422,156 @@ async function handlePathCommand(neural: NeuralAPI, argv: CommandArguments): Pro
|
|||
message: 'To item ID:',
|
||||
validate: (input: string) => input.length > 0
|
||||
}
|
||||
]);
|
||||
fromId = answers.from;
|
||||
toId = answers.to;
|
||||
spinner.start();
|
||||
])
|
||||
fromId = answers.from
|
||||
toId = answers.to
|
||||
spinner.start()
|
||||
}
|
||||
|
||||
const path = await neural.semanticPath(fromId, toId);
|
||||
const path = await neural.semanticPath(fromId, toId)
|
||||
|
||||
if (path.length === 0) {
|
||||
spinner.warn('🚫 No semantic path found');
|
||||
console.log(`No path found between ${chalk.cyan(fromId)} and ${chalk.cyan(toId)}`);
|
||||
spinner.warn('🚫 No semantic path found')
|
||||
console.log(`No path found between ${chalk.cyan(fromId)} and ${chalk.cyan(toId)}`)
|
||||
} else {
|
||||
spinner.succeed(`✅ Found path with ${path.length} hops`);
|
||||
spinner.succeed(`✅ Found path with ${path.length} hops`)
|
||||
|
||||
console.log(`\n🛣️ Semantic Path from ${chalk.cyan(fromId)} to ${chalk.cyan(toId)}:`);
|
||||
console.log(`${chalk.cyan(fromId)} (start)`);
|
||||
console.log(`\n🛣️ Semantic Path from ${chalk.cyan(fromId)} to ${chalk.cyan(toId)}:`)
|
||||
console.log(`${chalk.cyan(fromId)} (start)`)
|
||||
|
||||
path.forEach((hop, index) => {
|
||||
console.log(`${' '.repeat(index + 1)}↓ ${(hop.similarity * 100).toFixed(1)}%`);
|
||||
console.log(`${' '.repeat(index + 1)}${hop.id} (hop ${hop.hop})`);
|
||||
});
|
||||
console.log(`${' '.repeat(index + 1)}↓ ${(hop.similarity * 100).toFixed(1)}%`)
|
||||
console.log(`${' '.repeat(index + 1)}${hop.id} (hop ${hop.hop})`)
|
||||
})
|
||||
}
|
||||
|
||||
if (argv.output) {
|
||||
await saveToFile(argv.output, path, argv.format!);
|
||||
await saveToFile(argv.output, path, argv.format!)
|
||||
}
|
||||
|
||||
} catch (error) {
|
||||
spinner.fail('💥 Failed to find path');
|
||||
throw error;
|
||||
spinner.fail('💥 Failed to find path')
|
||||
throw error
|
||||
}
|
||||
}
|
||||
|
||||
async function handleOutliersCommand(neural: NeuralAPI, argv: CommandArguments): Promise<void> {
|
||||
const spinner = ora('🚨 Detecting semantic outliers...').start();
|
||||
const spinner = ora('🚨 Detecting semantic outliers...').start()
|
||||
|
||||
try {
|
||||
const outliers = await neural.outliers(argv.threshold);
|
||||
const outliers = await neural.outliers(argv.threshold)
|
||||
|
||||
spinner.succeed(`✅ Found ${outliers.length} outliers`);
|
||||
spinner.succeed(`✅ Found ${outliers.length} outliers`)
|
||||
|
||||
if (outliers.length === 0) {
|
||||
console.log('\n🎉 No outliers detected - all items are well connected!');
|
||||
console.log('\n🎉 No outliers detected - all items are well connected!')
|
||||
} else {
|
||||
console.log(`\n🚨 ${chalk.red(outliers.length)} Semantic Outliers:`);
|
||||
console.log(`\n🚨 ${chalk.red(outliers.length)} Semantic Outliers:`)
|
||||
outliers.forEach((outlier, index) => {
|
||||
console.log(`${index + 1}. ${outlier}`);
|
||||
});
|
||||
console.log(`${index + 1}. ${outlier}`)
|
||||
})
|
||||
|
||||
console.log(`\n💡 These items have similarity < ${argv.threshold} to their nearest neighbors`);
|
||||
console.log(`\n💡 These items have similarity < ${argv.threshold} to their nearest neighbors`)
|
||||
}
|
||||
|
||||
if (argv.output) {
|
||||
await saveToFile(argv.output, outliers, argv.format!);
|
||||
await saveToFile(argv.output, outliers, argv.format!)
|
||||
}
|
||||
|
||||
} catch (error) {
|
||||
spinner.fail('💥 Failed to detect outliers');
|
||||
throw error;
|
||||
spinner.fail('💥 Failed to detect outliers')
|
||||
throw error
|
||||
}
|
||||
}
|
||||
|
||||
async function handleVisualizeCommand(neural: NeuralAPI, argv: CommandArguments): Promise<void> {
|
||||
const spinner = ora('📊 Generating visualization data...').start();
|
||||
const spinner = ora('📊 Generating visualization data...').start()
|
||||
|
||||
try {
|
||||
const vizData = await neural.visualize({
|
||||
dimensions: argv.dimensions as 2 | 3,
|
||||
maxNodes: argv.limit
|
||||
});
|
||||
})
|
||||
|
||||
spinner.succeed('✅ Visualization data generated');
|
||||
spinner.succeed('✅ Visualization data generated')
|
||||
|
||||
console.log(`\n📊 Visualization Data (${vizData.format} layout):`);
|
||||
console.log(`📍 Nodes: ${vizData.nodes.length}`);
|
||||
console.log(`🔗 Edges: ${vizData.edges.length}`);
|
||||
console.log(`🎯 Clusters: ${vizData.clusters?.length || 0}`);
|
||||
console.log(`📐 Dimensions: ${vizData.layout?.dimensions}D`);
|
||||
console.log(`\n📊 Visualization Data (${vizData.format} layout):`)
|
||||
console.log(`📍 Nodes: ${vizData.nodes.length}`)
|
||||
console.log(`🔗 Edges: ${vizData.edges.length}`)
|
||||
console.log(`🎯 Clusters: ${vizData.clusters?.length || 0}`)
|
||||
console.log(`📐 Dimensions: ${vizData.layout?.dimensions}D`)
|
||||
|
||||
if (argv.format === 'json') {
|
||||
console.log('\nData:');
|
||||
console.log(JSON.stringify(vizData, null, 2));
|
||||
console.log('\nData:')
|
||||
console.log(JSON.stringify(vizData, null, 2))
|
||||
} else {
|
||||
console.log('\n🎨 Style Settings:');
|
||||
console.log(` Node Colors: ${vizData.style?.nodeColors}`);
|
||||
console.log(` Edge Width: ${vizData.style?.edgeWidth}`);
|
||||
console.log(` Labels: ${vizData.style?.labels}`);
|
||||
console.log('\n🎨 Style Settings:')
|
||||
console.log(` Node Colors: ${vizData.style?.nodeColors}`)
|
||||
console.log(` Edge Width: ${vizData.style?.edgeWidth}`)
|
||||
console.log(` Labels: ${vizData.style?.labels}`)
|
||||
}
|
||||
|
||||
if (argv.output) {
|
||||
await saveToFile(argv.output, vizData, 'json');
|
||||
console.log(`\n💾 Visualization data saved to: ${chalk.green(argv.output)}`);
|
||||
await saveToFile(argv.output, vizData, 'json')
|
||||
console.log(`\n💾 Visualization data saved to: ${chalk.green(argv.output)}`)
|
||||
} else {
|
||||
console.log(`\n💡 Use --output to save visualization data for external tools`);
|
||||
console.log(`\n💡 Use --output to save visualization data for external tools`)
|
||||
}
|
||||
|
||||
} catch (error) {
|
||||
spinner.fail('💥 Failed to generate visualization');
|
||||
throw error;
|
||||
spinner.fail('💥 Failed to generate visualization')
|
||||
throw error
|
||||
}
|
||||
}
|
||||
|
||||
async function saveToFile(filepath: string, data: any, format: string): Promise<void> {
|
||||
const dir = path.dirname(filepath);
|
||||
const dir = path.dirname(filepath)
|
||||
if (!fs.existsSync(dir)) {
|
||||
fs.mkdirSync(dir, { recursive: true });
|
||||
fs.mkdirSync(dir, { recursive: true })
|
||||
}
|
||||
|
||||
let output: string;
|
||||
let output: string
|
||||
switch (format) {
|
||||
case 'json':
|
||||
output = JSON.stringify(data, null, 2);
|
||||
break;
|
||||
output = JSON.stringify(data, null, 2)
|
||||
break
|
||||
case 'table':
|
||||
output = formatAsTable(data);
|
||||
break;
|
||||
output = formatAsTable(data)
|
||||
break
|
||||
default:
|
||||
output = JSON.stringify(data, null, 2);
|
||||
output = JSON.stringify(data, null, 2)
|
||||
}
|
||||
|
||||
fs.writeFileSync(filepath, output, 'utf8');
|
||||
console.log(`💾 Saved to: ${chalk.green(filepath)}`);
|
||||
fs.writeFileSync(filepath, output, 'utf8')
|
||||
console.log(`💾 Saved to: ${chalk.green(filepath)}`)
|
||||
}
|
||||
|
||||
function formatAsTable(data: any): string {
|
||||
// Simple table formatting - could be enhanced with a table library
|
||||
if (Array.isArray(data)) {
|
||||
return data.map((item, index) => `${index + 1}. ${JSON.stringify(item)}`).join('\n');
|
||||
return data.map((item, index) => `${index + 1}. ${JSON.stringify(item)}`).join('\n')
|
||||
}
|
||||
return JSON.stringify(data, null, 2);
|
||||
return JSON.stringify(data, null, 2)
|
||||
}
|
||||
|
||||
function showHelp(): void {
|
||||
console.log('\n🧠 Neural Similarity API Commands:');
|
||||
console.log('');
|
||||
console.log(' brainy neural similar <item1> <item2> Calculate similarity');
|
||||
console.log(' brainy neural clusters Find semantic clusters');
|
||||
console.log(' brainy neural hierarchy <id> Show item hierarchy');
|
||||
console.log(' brainy neural neighbors <id> Find semantic neighbors');
|
||||
console.log(' brainy neural path <from> <to> Find semantic path');
|
||||
console.log(' brainy neural outliers Detect outliers');
|
||||
console.log(' brainy neural visualize Generate visualization data');
|
||||
console.log('');
|
||||
console.log('Options:');
|
||||
console.log(' --threshold, -t Similarity threshold (0-1)');
|
||||
console.log(' --format, -f Output format (json|table|tree|graph)');
|
||||
console.log(' --output, -o Save to file');
|
||||
console.log(' --limit, -l Maximum results');
|
||||
console.log(' --explain, -e Include explanations');
|
||||
console.log('');
|
||||
console.log('\n🧠 Neural Similarity API Commands:')
|
||||
console.log('')
|
||||
console.log(' brainy neural similar <item1> <item2> Calculate similarity')
|
||||
console.log(' brainy neural clusters Find semantic clusters')
|
||||
console.log(' brainy neural hierarchy <id> Show item hierarchy')
|
||||
console.log(' brainy neural neighbors <id> Find semantic neighbors')
|
||||
console.log(' brainy neural path <from> <to> Find semantic path')
|
||||
console.log(' brainy neural outliers Detect outliers')
|
||||
console.log(' brainy neural visualize Generate visualization data')
|
||||
console.log('')
|
||||
console.log('Options:')
|
||||
console.log(' --threshold, -t Similarity threshold (0-1)')
|
||||
console.log(' --format, -f Output format (json|table|tree|graph)')
|
||||
console.log(' --output, -o Save to file')
|
||||
console.log(' --limit, -l Maximum results')
|
||||
console.log(' --explain, -e Include explanations')
|
||||
console.log('')
|
||||
}
|
||||
|
||||
export default neuralCommand;
|
||||
export default neuralCommand
|
||||
Loading…
Add table
Add a link
Reference in a new issue