508 lines
19 KiB
JavaScript
508 lines
19 KiB
JavaScript
|
/**
|
||
|
|
* 🧠 Neural Similarity API Commands
|
||
|
|
*
|
||
|
|
* CLI interface for semantic similarity, clustering, and neural operations
|
||
|
|
*/
|
||
|
|
import inquirer from 'inquirer';
|
||
|
|
import chalk from 'chalk';
|
||
|
|
import ora from 'ora';
|
||
|
|
import fs from 'fs';
|
||
|
|
import path from 'path';
|
||
|
|
import { BrainyData } from '../../brainyData.js';
|
||
|
|
import { NeuralAPI } from '../../neural/neuralAPI.js';
|
||
|
|
export const neuralCommand = {
|
||
|
|
command: 'neural [action]',
|
||
|
|
describe: '🧠 Neural similarity and clustering operations',
|
||
|
|
builder: (yargs) => {
|
||
|
|
return yargs
|
||
|
|
.positional('action', {
|
||
|
|
describe: 'Neural operation to perform',
|
||
|
|
type: 'string',
|
||
|
|
choices: ['similar', 'clusters', 'hierarchy', 'neighbors', 'path', 'outliers', 'visualize']
|
||
|
|
})
|
||
|
|
.option('id', {
|
||
|
|
describe: 'Item ID for similarity operations',
|
||
|
|
type: 'string',
|
||
|
|
alias: 'i'
|
||
|
|
})
|
||
|
|
.option('query', {
|
||
|
|
describe: 'Query text for similarity search',
|
||
|
|
type: 'string',
|
||
|
|
alias: 'q'
|
||
|
|
})
|
||
|
|
.option('threshold', {
|
||
|
|
describe: 'Similarity threshold (0-1)',
|
||
|
|
type: 'number',
|
||
|
|
default: 0.7,
|
||
|
|
alias: 't'
|
||
|
|
})
|
||
|
|
.option('format', {
|
||
|
|
describe: 'Output format',
|
||
|
|
type: 'string',
|
||
|
|
choices: ['json', 'table', 'tree', 'graph'],
|
||
|
|
default: 'table',
|
||
|
|
alias: 'f'
|
||
|
|
})
|
||
|
|
.option('output', {
|
||
|
|
describe: 'Output file path',
|
||
|
|
type: 'string',
|
||
|
|
alias: 'o'
|
||
|
|
})
|
||
|
|
.option('limit', {
|
||
|
|
describe: 'Maximum number of results',
|
||
|
|
type: 'number',
|
||
|
|
default: 10,
|
||
|
|
alias: 'l'
|
||
|
|
})
|
||
|
|
.option('algorithm', {
|
||
|
|
describe: 'Clustering algorithm',
|
||
|
|
type: 'string',
|
||
|
|
choices: ['hierarchical', 'kmeans', 'dbscan', 'auto'],
|
||
|
|
default: 'auto',
|
||
|
|
alias: 'a'
|
||
|
|
})
|
||
|
|
.option('dimensions', {
|
||
|
|
describe: 'Visualization dimensions (2 or 3)',
|
||
|
|
type: 'number',
|
||
|
|
choices: [2, 3],
|
||
|
|
default: 2,
|
||
|
|
alias: 'd'
|
||
|
|
})
|
||
|
|
.option('explain', {
|
||
|
|
describe: 'Include detailed explanations',
|
||
|
|
type: 'boolean',
|
||
|
|
default: false,
|
||
|
|
alias: 'e'
|
||
|
|
});
|
||
|
|
},
|
||
|
|
handler: async (argv) => {
|
||
|
|
console.log(chalk.cyan('\n🧠 NEURAL SIMILARITY API'));
|
||
|
|
console.log(chalk.gray('━'.repeat(50)));
|
||
|
|
// Initialize Brainy and Neural API
|
||
|
|
const brain = new BrainyData();
|
||
|
|
const neural = new NeuralAPI(brain);
|
||
|
|
try {
|
||
|
|
const action = argv.action || await promptForAction();
|
||
|
|
switch (action) {
|
||
|
|
case 'similar':
|
||
|
|
await handleSimilarCommand(neural, argv);
|
||
|
|
break;
|
||
|
|
case 'clusters':
|
||
|
|
await handleClustersCommand(neural, argv);
|
||
|
|
break;
|
||
|
|
case 'hierarchy':
|
||
|
|
await handleHierarchyCommand(neural, argv);
|
||
|
|
break;
|
||
|
|
case 'neighbors':
|
||
|
|
await handleNeighborsCommand(neural, argv);
|
||
|
|
break;
|
||
|
|
case 'path':
|
||
|
|
await handlePathCommand(neural, argv);
|
||
|
|
break;
|
||
|
|
case 'outliers':
|
||
|
|
await handleOutliersCommand(neural, argv);
|
||
|
|
break;
|
||
|
|
case 'visualize':
|
||
|
|
await handleVisualizeCommand(neural, argv);
|
||
|
|
break;
|
||
|
|
default:
|
||
|
|
console.log(chalk.red(`❌ Unknown action: ${action}`));
|
||
|
|
showHelp();
|
||
|
|
}
|
||
|
|
}
|
||
|
|
catch (error) {
|
||
|
|
console.error(chalk.red('💥 Error:'), error instanceof Error ? error.message : error);
|
||
|
|
process.exit(1);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
};
|
||
|
|
async function promptForAction() {
|
||
|
|
const answer = await inquirer.prompt([{
|
||
|
|
type: 'list',
|
||
|
|
name: 'action',
|
||
|
|
message: 'Choose a neural operation:',
|
||
|
|
choices: [
|
||
|
|
{ name: '🔗 Calculate similarity between items', value: 'similar' },
|
||
|
|
{ name: '🎯 Find semantic clusters', value: 'clusters' },
|
||
|
|
{ name: '🌳 Show item hierarchy', value: 'hierarchy' },
|
||
|
|
{ name: '🕸️ Find semantic neighbors', value: 'neighbors' },
|
||
|
|
{ name: '🛣️ Find semantic path between items', value: 'path' },
|
||
|
|
{ name: '🚨 Detect outliers', value: 'outliers' },
|
||
|
|
{ name: '📊 Generate visualization data', value: 'visualize' }
|
||
|
|
]
|
||
|
|
}]);
|
||
|
|
return answer.action;
|
||
|
|
}
|
||
|
|
async function handleSimilarCommand(neural, argv) {
|
||
|
|
const spinner = ora('🧠 Calculating semantic similarity...').start();
|
||
|
|
try {
|
||
|
|
let itemA, itemB;
|
||
|
|
if (argv.id && argv.query) {
|
||
|
|
itemA = argv.id;
|
||
|
|
itemB = argv.query;
|
||
|
|
}
|
||
|
|
else if (argv._ && argv._.length >= 3) {
|
||
|
|
itemA = argv._[1];
|
||
|
|
itemB = argv._[2];
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
spinner.stop();
|
||
|
|
const answers = await inquirer.prompt([
|
||
|
|
{
|
||
|
|
type: 'input',
|
||
|
|
name: 'itemA',
|
||
|
|
message: 'First item (ID or text):',
|
||
|
|
validate: (input) => input.length > 0
|
||
|
|
},
|
||
|
|
{
|
||
|
|
type: 'input',
|
||
|
|
name: 'itemB',
|
||
|
|
message: 'Second item (ID or text):',
|
||
|
|
validate: (input) => input.length > 0
|
||
|
|
}
|
||
|
|
]);
|
||
|
|
itemA = answers.itemA;
|
||
|
|
itemB = answers.itemB;
|
||
|
|
spinner.start();
|
||
|
|
}
|
||
|
|
const result = await neural.similar(itemA, itemB, {
|
||
|
|
explain: argv.explain,
|
||
|
|
includeBreakdown: argv.explain
|
||
|
|
});
|
||
|
|
spinner.succeed('✅ Similarity calculated');
|
||
|
|
if (typeof result === 'number') {
|
||
|
|
console.log(`\n🔗 Similarity: ${chalk.cyan((result * 100).toFixed(1))}%`);
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
console.log(`\n🔗 Similarity: ${chalk.cyan((result.score * 100).toFixed(1))}%`);
|
||
|
|
if (result.explanation) {
|
||
|
|
console.log(`💭 Explanation: ${result.explanation}`);
|
||
|
|
}
|
||
|
|
if (result.breakdown) {
|
||
|
|
console.log('\n📊 Breakdown:');
|
||
|
|
console.log(` Semantic: ${chalk.yellow((result.breakdown.semantic * 100).toFixed(1))}%`);
|
||
|
|
if (result.breakdown.taxonomic !== undefined) {
|
||
|
|
console.log(` Taxonomic: ${chalk.yellow((result.breakdown.taxonomic * 100).toFixed(1))}%`);
|
||
|
|
}
|
||
|
|
if (result.breakdown.contextual !== undefined) {
|
||
|
|
console.log(` Contextual: ${chalk.yellow((result.breakdown.contextual * 100).toFixed(1))}%`);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
if (result.hierarchy) {
|
||
|
|
console.log(`\n🌳 Hierarchy: ${result.hierarchy.sharedParent ?
|
||
|
|
`Shared parent at distance ${result.hierarchy.distance}` :
|
||
|
|
'No shared parent found'}`);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
if (argv.output) {
|
||
|
|
await saveToFile(argv.output, result, argv.format);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
catch (error) {
|
||
|
|
spinner.fail('💥 Failed to calculate similarity');
|
||
|
|
throw error;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
async function handleClustersCommand(neural, argv) {
|
||
|
|
const spinner = ora('🎯 Finding semantic clusters...').start();
|
||
|
|
try {
|
||
|
|
const options = {
|
||
|
|
algorithm: argv.algorithm,
|
||
|
|
threshold: argv.threshold,
|
||
|
|
maxClusters: argv.limit
|
||
|
|
};
|
||
|
|
const clusters = await neural.clusters(argv.query || options);
|
||
|
|
spinner.succeed(`✅ Found ${clusters.length} clusters`);
|
||
|
|
if (argv.format === 'json') {
|
||
|
|
console.log(JSON.stringify(clusters, null, 2));
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
console.log(`\n🎯 ${chalk.cyan(clusters.length)} Semantic Clusters:\n`);
|
||
|
|
clusters.forEach((cluster, index) => {
|
||
|
|
console.log(`${chalk.yellow(`Cluster ${index + 1}:`)} ${cluster.label || cluster.id}`);
|
||
|
|
console.log(` 📊 Confidence: ${chalk.green((cluster.confidence * 100).toFixed(1))}%`);
|
||
|
|
console.log(` 👥 Members: ${cluster.members.length}`);
|
||
|
|
if (cluster.members.length <= 5) {
|
||
|
|
cluster.members.forEach(member => {
|
||
|
|
console.log(` • ${member}`);
|
||
|
|
});
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
cluster.members.slice(0, 3).forEach(member => {
|
||
|
|
console.log(` • ${member}`);
|
||
|
|
});
|
||
|
|
console.log(` ... and ${cluster.members.length - 3} more`);
|
||
|
|
}
|
||
|
|
console.log();
|
||
|
|
});
|
||
|
|
}
|
||
|
|
if (argv.output) {
|
||
|
|
await saveToFile(argv.output, clusters, argv.format);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
catch (error) {
|
||
|
|
spinner.fail('💥 Failed to find clusters');
|
||
|
|
throw error;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
async function handleHierarchyCommand(neural, argv) {
|
||
|
|
const spinner = ora('🌳 Building semantic hierarchy...').start();
|
||
|
|
try {
|
||
|
|
const id = argv.id || argv._[1];
|
||
|
|
if (!id) {
|
||
|
|
spinner.stop();
|
||
|
|
const answer = await inquirer.prompt([{
|
||
|
|
type: 'input',
|
||
|
|
name: 'id',
|
||
|
|
message: 'Enter item ID:',
|
||
|
|
validate: (input) => input.length > 0
|
||
|
|
}]);
|
||
|
|
spinner.start();
|
||
|
|
const hierarchy = await neural.hierarchy(answer.id);
|
||
|
|
displayHierarchy(hierarchy);
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
const hierarchy = await neural.hierarchy(id);
|
||
|
|
spinner.succeed('✅ Hierarchy built');
|
||
|
|
displayHierarchy(hierarchy);
|
||
|
|
}
|
||
|
|
if (argv.output) {
|
||
|
|
const hierarchy = await neural.hierarchy(id || argv._[1]);
|
||
|
|
await saveToFile(argv.output, hierarchy, argv.format);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
catch (error) {
|
||
|
|
spinner.fail('💥 Failed to build hierarchy');
|
||
|
|
throw error;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
function displayHierarchy(hierarchy) {
|
||
|
|
console.log(`\n🌳 Semantic Hierarchy for ${chalk.cyan(hierarchy.self.id)}:`);
|
||
|
|
if (hierarchy.root) {
|
||
|
|
console.log(`🔝 Root: ${hierarchy.root.id} (${(hierarchy.root.similarity * 100).toFixed(1)}%)`);
|
||
|
|
}
|
||
|
|
if (hierarchy.grandparent) {
|
||
|
|
console.log(`👴 Grandparent: ${hierarchy.grandparent.id} (${(hierarchy.grandparent.similarity * 100).toFixed(1)}%)`);
|
||
|
|
}
|
||
|
|
if (hierarchy.parent) {
|
||
|
|
console.log(`👨 Parent: ${hierarchy.parent.id} (${(hierarchy.parent.similarity * 100).toFixed(1)}%)`);
|
||
|
|
}
|
||
|
|
console.log(`🎯 ${chalk.bold('Self:')} ${hierarchy.self.id}`);
|
||
|
|
if (hierarchy.siblings && hierarchy.siblings.length > 0) {
|
||
|
|
console.log(`👥 Siblings: ${hierarchy.siblings.length}`);
|
||
|
|
hierarchy.siblings.forEach((sibling) => {
|
||
|
|
console.log(` • ${sibling.id} (${(sibling.similarity * 100).toFixed(1)}%)`);
|
||
|
|
});
|
||
|
|
}
|
||
|
|
if (hierarchy.children && hierarchy.children.length > 0) {
|
||
|
|
console.log(`👶 Children: ${hierarchy.children.length}`);
|
||
|
|
hierarchy.children.forEach((child) => {
|
||
|
|
console.log(` • ${child.id} (${(child.similarity * 100).toFixed(1)}%)`);
|
||
|
|
});
|
||
|
|
}
|
||
|
|
}
|
||
|
|
async function handleNeighborsCommand(neural, argv) {
|
||
|
|
const spinner = ora('🕸️ Finding semantic neighbors...').start();
|
||
|
|
try {
|
||
|
|
const id = argv.id || argv._[1];
|
||
|
|
if (!id) {
|
||
|
|
spinner.stop();
|
||
|
|
const answer = await inquirer.prompt([{
|
||
|
|
type: 'input',
|
||
|
|
name: 'id',
|
||
|
|
message: 'Enter item ID:',
|
||
|
|
validate: (input) => input.length > 0
|
||
|
|
}]);
|
||
|
|
spinner.start();
|
||
|
|
}
|
||
|
|
const targetId = id || (await inquirer.prompt([{
|
||
|
|
type: 'input',
|
||
|
|
name: 'id',
|
||
|
|
message: 'Enter item ID:',
|
||
|
|
validate: (input) => input.length > 0
|
||
|
|
}])).id;
|
||
|
|
const graph = await neural.neighbors(targetId, {
|
||
|
|
limit: argv.limit,
|
||
|
|
includeEdges: true
|
||
|
|
});
|
||
|
|
spinner.succeed(`✅ Found ${graph.neighbors.length} neighbors`);
|
||
|
|
console.log(`\n🕸️ Neighbors of ${chalk.cyan(graph.center)}:`);
|
||
|
|
graph.neighbors.forEach((neighbor, index) => {
|
||
|
|
console.log(`${index + 1}. ${neighbor.id} (${(neighbor.similarity * 100).toFixed(1)}%)`);
|
||
|
|
if (neighbor.type) {
|
||
|
|
console.log(` Type: ${neighbor.type}`);
|
||
|
|
}
|
||
|
|
if (neighbor.connections) {
|
||
|
|
console.log(` Connections: ${neighbor.connections}`);
|
||
|
|
}
|
||
|
|
});
|
||
|
|
if (graph.edges && graph.edges.length > 0) {
|
||
|
|
console.log(`\n🔗 ${graph.edges.length} semantic connections found`);
|
||
|
|
}
|
||
|
|
if (argv.output) {
|
||
|
|
await saveToFile(argv.output, graph, argv.format);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
catch (error) {
|
||
|
|
spinner.fail('💥 Failed to find neighbors');
|
||
|
|
throw error;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
async function handlePathCommand(neural, argv) {
|
||
|
|
const spinner = ora('🛣️ Finding semantic path...').start();
|
||
|
|
try {
|
||
|
|
let fromId, toId;
|
||
|
|
if (argv._ && argv._.length >= 3) {
|
||
|
|
fromId = argv._[1];
|
||
|
|
toId = argv._[2];
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
spinner.stop();
|
||
|
|
const answers = await inquirer.prompt([
|
||
|
|
{
|
||
|
|
type: 'input',
|
||
|
|
name: 'from',
|
||
|
|
message: 'From item ID:',
|
||
|
|
validate: (input) => input.length > 0
|
||
|
|
},
|
||
|
|
{
|
||
|
|
type: 'input',
|
||
|
|
name: 'to',
|
||
|
|
message: 'To item ID:',
|
||
|
|
validate: (input) => input.length > 0
|
||
|
|
}
|
||
|
|
]);
|
||
|
|
fromId = answers.from;
|
||
|
|
toId = answers.to;
|
||
|
|
spinner.start();
|
||
|
|
}
|
||
|
|
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)}`);
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
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)`);
|
||
|
|
path.forEach((hop, index) => {
|
||
|
|
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);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
catch (error) {
|
||
|
|
spinner.fail('💥 Failed to find path');
|
||
|
|
throw error;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
async function handleOutliersCommand(neural, argv) {
|
||
|
|
const spinner = ora('🚨 Detecting semantic outliers...').start();
|
||
|
|
try {
|
||
|
|
const outliers = await neural.outliers(argv.threshold);
|
||
|
|
spinner.succeed(`✅ Found ${outliers.length} outliers`);
|
||
|
|
if (outliers.length === 0) {
|
||
|
|
console.log('\n🎉 No outliers detected - all items are well connected!');
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
console.log(`\n🚨 ${chalk.red(outliers.length)} Semantic Outliers:`);
|
||
|
|
outliers.forEach((outlier, index) => {
|
||
|
|
console.log(`${index + 1}. ${outlier}`);
|
||
|
|
});
|
||
|
|
console.log(`\n💡 These items have similarity < ${argv.threshold} to their nearest neighbors`);
|
||
|
|
}
|
||
|
|
if (argv.output) {
|
||
|
|
await saveToFile(argv.output, outliers, argv.format);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
catch (error) {
|
||
|
|
spinner.fail('💥 Failed to detect outliers');
|
||
|
|
throw error;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
async function handleVisualizeCommand(neural, argv) {
|
||
|
|
const spinner = ora('📊 Generating visualization data...').start();
|
||
|
|
try {
|
||
|
|
const vizData = await neural.visualize({
|
||
|
|
dimensions: argv.dimensions,
|
||
|
|
maxNodes: argv.limit
|
||
|
|
});
|
||
|
|
spinner.succeed('✅ Visualization data generated');
|
||
|
|
console.log(`\n📊 Visualization Data (${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`);
|
||
|
|
if (argv.format === 'json') {
|
||
|
|
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}`);
|
||
|
|
}
|
||
|
|
if (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`);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
catch (error) {
|
||
|
|
spinner.fail('💥 Failed to generate visualization');
|
||
|
|
throw error;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
async function saveToFile(filepath, data, format) {
|
||
|
|
const dir = path.dirname(filepath);
|
||
|
|
if (!fs.existsSync(dir)) {
|
||
|
|
fs.mkdirSync(dir, { recursive: true });
|
||
|
|
}
|
||
|
|
let output;
|
||
|
|
switch (format) {
|
||
|
|
case 'json':
|
||
|
|
output = JSON.stringify(data, null, 2);
|
||
|
|
break;
|
||
|
|
case 'table':
|
||
|
|
output = formatAsTable(data);
|
||
|
|
break;
|
||
|
|
default:
|
||
|
|
output = JSON.stringify(data, null, 2);
|
||
|
|
}
|
||
|
|
fs.writeFileSync(filepath, output, 'utf8');
|
||
|
|
console.log(`💾 Saved to: ${chalk.green(filepath)}`);
|
||
|
|
}
|
||
|
|
function formatAsTable(data) {
|
||
|
|
// Simple table formatting - could be enhanced with a table library
|
||
|
|
if (Array.isArray(data)) {
|
||
|
|
return data.map((item, index) => `${index + 1}. ${JSON.stringify(item)}`).join('\n');
|
||
|
|
}
|
||
|
|
return JSON.stringify(data, null, 2);
|
||
|
|
}
|
||
|
|
function showHelp() {
|
||
|
|
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;
|
||
|
|
//# sourceMappingURL=neural.js.map
|