David Snelling
518a422da0
### Changes: - Added `backup` command to export database content into a JSON file. - Includes noun and verb data with metadata. - Allows specifying a custom output filename (`brainy-backup.json` by default). - Added `restore` command to import data from a JSON file into the database. - Supports clearing existing data with `--clear` option before restoration. - Displays success information for imported nouns and verbs. - Integrated help text examples for the new commands. - Enhanced CLI interface with additional indentation for better readability. - Adjusted `completion-setup` to include autocomplete for backup and restore operations. ### Purpose: Introduced backup and restore functionality to improve database management and data portability, enabling users to save and load datasets seamlessly for better flexibility in usage and recovery.
1244 lines
37 KiB
JavaScript
1244 lines
37 KiB
JavaScript
#!/usr/bin/env node
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/**
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* Brainy CLI
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* A command-line interface for interacting with the Brainy vector database
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*/
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import { BrainyData, NounType, VerbType, FileSystemStorage } from './index.js'
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import { fileURLToPath } from 'url'
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import { dirname, join } from 'path'
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import fs from 'fs'
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import { Command } from 'commander'
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import omelette from 'omelette'
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import { VERSION } from './utils/version.js'
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import { sequentialPipeline } from './sequentialPipeline.js'
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import { augmentationPipeline, ExecutionMode } from './augmentationPipeline.js'
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import { AugmentationType } from './types/augmentations.js'
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// Get the directory of the current module
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const __filename = fileURLToPath(import.meta.url)
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const __dirname = dirname(__filename)
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// Helper function to parse JSON safely
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function parseJSON(str: string): any {
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try {
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return JSON.parse(str)
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} catch (e) {
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console.error('Error parsing JSON:', (e as Error).message)
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return {}
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}
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}
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// Helper function to resolve noun type
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function resolveNounType(type: string | number | undefined): NounType {
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if (!type) return NounType.Thing
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// If it's a string, try to match it to a NounType
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if (typeof type === 'string') {
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const nounTypeKey = Object.keys(NounType).find(
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(key) => key.toLowerCase() === type.toLowerCase()
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)
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return nounTypeKey
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? NounType[nounTypeKey as keyof typeof NounType]
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: NounType.Thing
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}
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// Convert number to string type for safety
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return Object.values(NounType)[type as number] || NounType.Thing
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}
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// Helper function to resolve verb type
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function resolveVerbType(type: string | number | undefined): VerbType {
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if (!type) return VerbType.RelatedTo
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// If it's a string, try to match it to a VerbType
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if (typeof type === 'string') {
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const verbTypeKey = Object.keys(VerbType).find(
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(key) => key.toLowerCase() === type.toLowerCase()
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)
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return verbTypeKey
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? VerbType[verbTypeKey as keyof typeof VerbType]
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: VerbType.RelatedTo
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}
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// Convert number to string type for safety
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return Object.values(VerbType)[type as number] || VerbType.RelatedTo
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}
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// Create a new Command instance
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const program = new Command()
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// Configure the program
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program
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.name('@soulcraft/brainy')
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.description(
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'A vector database using HNSW indexing with Origin Private File System storage'
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)
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.version(VERSION, '-V, --version', 'Output the current version')
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// Create data directory if it doesn't exist
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const dataDir = join(__dirname, '..', 'data')
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if (!fs.existsSync(dataDir)) {
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fs.mkdirSync(dataDir, { recursive: true })
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}
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// Create a database instance with file system storage
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const createDb = () => {
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return new BrainyData({
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storageAdapter: new FileSystemStorage(dataDir)
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})
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}
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// Define commands
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program
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.command('init')
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.description('Initialize a new database')
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.action(async () => {
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try {
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const db = createDb()
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await db.init()
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console.log('Database initialized successfully')
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} catch (error) {
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console.error('Error:', (error as Error).message)
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process.exit(1)
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}
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})
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program
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.command('add')
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.description('Add a new noun with the given text and optional metadata')
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.argument('<text>', 'Text to add as a noun')
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.argument('[metadata]', 'Optional metadata as JSON string')
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.action(async (text, metadataStr) => {
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try {
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const db = createDb()
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await db.init()
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const metadata = metadataStr ? parseJSON(metadataStr) : {}
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// Process metadata to handle noun type
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if (metadata.noun) {
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metadata.noun = resolveNounType(metadata.noun)
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}
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const id = await db.add(text, metadata)
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console.log(`Added noun with ID: ${id}`)
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} catch (error) {
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console.error('Error:', (error as Error).message)
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process.exit(1)
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}
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})
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program
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.command('search')
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.description('Search for nouns similar to the query')
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.argument('<query>', 'Search query text')
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.option('-l, --limit <number>', 'Maximum number of results to return', '5')
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.action(async (query, options) => {
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try {
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const db = createDb()
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await db.init()
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const limit = parseInt(options.limit, 10)
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const results = await db.searchText(query, limit)
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console.log(`Search results for "${query}":`)
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results.forEach((result, index) => {
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console.log(`${index + 1}. ID: ${result.id}`)
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console.log(` Score: ${result.score.toFixed(4)}`)
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console.log(` Metadata: ${JSON.stringify(result.metadata)}`)
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console.log(
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` Vector: [${result.vector
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.slice(0, 3)
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.map((v) => v.toFixed(2))
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.join(', ')}...]`
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)
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console.log()
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})
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} catch (error) {
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console.error('Error:', (error as Error).message)
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process.exit(1)
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}
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})
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program
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.command('get')
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.description('Get a noun by ID')
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.argument('<id>', 'ID of the noun to get')
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.action(async (id) => {
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try {
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const db = createDb()
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await db.init()
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const noun = await db.get(id)
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if (noun) {
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console.log(`Noun ID: ${noun.id}`)
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console.log(`Metadata: ${JSON.stringify(noun.metadata)}`)
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console.log(
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`Vector: [${noun.vector
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.slice(0, 5)
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.map((v) => v.toFixed(2))
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.join(', ')}...]`
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)
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} else {
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console.log(`No noun found with ID: ${id}`)
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}
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} catch (error) {
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console.error('Error:', (error as Error).message)
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process.exit(1)
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}
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})
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program
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.command('delete')
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.description('Delete a noun by ID')
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.argument('<id>', 'ID of the noun to delete')
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.action(async (id) => {
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try {
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const db = createDb()
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await db.init()
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await db.delete(id)
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console.log(`Deleted noun with ID: ${id}`)
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} catch (error) {
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console.error('Error:', (error as Error).message)
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process.exit(1)
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}
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})
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program
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.command('addVerb')
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.description('Add a relationship between nouns')
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.argument('<sourceId>', 'ID of the source noun')
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.argument('<targetId>', 'ID of the target noun')
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.argument('<verbType>', 'Type of relationship')
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.argument('[metadata]', 'Optional metadata as JSON string')
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.action(async (sourceId, targetId, verbTypeStr, metadataStr) => {
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try {
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const db = createDb()
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await db.init()
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const verbType = resolveVerbType(verbTypeStr)
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const verbMetadata = metadataStr ? parseJSON(metadataStr) : {}
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// Add verb type to metadata
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verbMetadata.verb = verbType
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const verbId = await db.addVerb(sourceId, targetId, undefined, {
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type: verbType,
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metadata: verbMetadata
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})
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console.log(`Added verb with ID: ${verbId}`)
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} catch (error) {
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console.error('Error:', (error as Error).message)
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process.exit(1)
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}
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})
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program
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.command('getVerbs')
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.description('Get all relationships for a noun')
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.argument('<id>', 'ID of the noun to get relationships for')
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.action(async (id) => {
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try {
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const db = createDb()
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await db.init()
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const verbs = await db.getVerbsBySource(id)
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console.log(`Relationships for noun ${id}:`)
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if (verbs.length === 0) {
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console.log('No relationships found')
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} else {
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verbs.forEach((verb, index) => {
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console.log(`${index + 1}. ID: ${verb.id}`)
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console.log(
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` Type: ${Object.keys(VerbType).find((key) => VerbType[key as keyof typeof VerbType] === verb.metadata.verb) || verb.metadata.verb}`
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)
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console.log(` Target: ${verb.targetId}`)
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console.log(` Metadata: ${JSON.stringify(verb.metadata)}`)
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console.log()
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})
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}
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} catch (error) {
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console.error('Error:', (error as Error).message)
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process.exit(1)
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}
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})
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program
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.command('status')
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.description('Show database status')
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.action(async () => {
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try {
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const db = createDb()
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await db.init()
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const status = await db.status()
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console.log('Database Status:')
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console.log(`Storage type: ${status.type}`)
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console.log(`Storage used: ${status.used} bytes`)
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console.log(
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`Storage quota: ${status.quota !== null ? `${status.quota} bytes` : 'unlimited'}`
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)
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// Display additional details if available
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if (status.details) {
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console.log('Additional details:')
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Object.entries(status.details).forEach(([key, value]) => {
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console.log(` ${key}: ${JSON.stringify(value)}`)
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})
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}
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} catch (error) {
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console.error('Error:', (error as Error).message)
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process.exit(1)
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}
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})
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program
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.command('clear')
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.description('Clear all data from the database')
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.option('-f, --force', 'Skip confirmation prompt', false)
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.action(async (options) => {
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try {
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// Confirm unless --force is used
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if (!options.force) {
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console.log(
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'WARNING: This will permanently delete ALL data in the database.'
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)
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console.log('To proceed without confirmation, use the --force option.')
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// Exit without doing anything
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console.log('Operation cancelled. No data was deleted.')
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console.log('To clear all data, use: brainy clear --force')
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return
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}
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const db = createDb()
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await db.init()
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await db.clear()
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console.log('Database cleared successfully. All data has been removed.')
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} catch (error) {
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console.error('Error:', (error as Error).message)
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process.exit(1)
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}
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})
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program
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.command('backup')
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.description('Backup all data from the database to a JSON file')
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.argument('[filename]', 'Output filename (default: brainy-backup.json)')
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.action(async (filename) => {
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try {
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const db = createDb()
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await db.init()
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// Default filename if not provided
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const outputFile = filename || 'brainy-backup.json'
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// Backup the data
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const data = await db.backup()
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// Write to file
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fs.writeFileSync(outputFile, JSON.stringify(data, null, 2))
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console.log(`Data backed up successfully to ${outputFile}`)
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console.log(`Backed up ${data.nouns.length} nouns and ${data.verbs.length} verbs`)
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} catch (error) {
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console.error('Error:', (error as Error).message)
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process.exit(1)
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}
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})
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program
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.command('restore')
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.description('Restore data from a JSON file into the database')
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.argument('<filename>', 'Input JSON file')
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.option('-c, --clear', 'Clear existing data before restoring', false)
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.action(async (filename, options) => {
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try {
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const db = createDb()
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await db.init()
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// Read the file
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if (!fs.existsSync(filename)) {
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console.error(`File not found: ${filename}`)
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process.exit(1)
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}
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const fileContent = fs.readFileSync(filename, 'utf8')
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const data = JSON.parse(fileContent)
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// Restore the data
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const result = await db.restore(data, { clearExisting: options.clear })
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console.log(`Data restored successfully from ${filename}`)
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console.log(`Restored ${result.nounsRestored} nouns and ${result.verbsRestored} verbs`)
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} catch (error) {
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console.error('Error:', (error as Error).message)
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process.exit(1)
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}
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})
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program
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.command('visualize')
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.description('Visualize the graph structure in ASCII format')
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.option('-r, --root <id>', 'ID of the root noun to start visualization from')
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.option(
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'-d, --depth <number>',
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'Maximum depth of the graph to visualize',
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'2'
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)
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.option('-t, --type <type>', 'Filter by noun type')
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.option(
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'-l, --limit <number>',
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'Maximum number of nodes to display per level',
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'10'
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)
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.action(async (options) => {
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try {
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const db = createDb()
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await db.init()
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// Parse options
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const depth = parseInt(options.depth, 10)
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const limit = parseInt(options.limit, 10)
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const rootId = options.root
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const nounType = options.type ? resolveNounType(options.type) : undefined
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// Get all nouns if no root is specified
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if (!rootId && !nounType) {
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// Get all nouns (limited by the limit option)
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const allNouns = []
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let count = 0
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// Since there's no direct method to get all nouns, we'll use search with a high limit
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const searchResults = await db.search('', 1000, {
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forceEmbed: true
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})
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for (const result of searchResults) {
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if (count >= limit) break
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allNouns.push(result)
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count++
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}
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if (allNouns.length === 0) {
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console.log('No nouns found in the database.')
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return
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}
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console.log(`Graph Overview (showing ${allNouns.length} nouns):\n`)
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for (const noun of allNouns) {
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// Get outgoing verbs
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const outgoingVerbs = await db.getVerbsBySource(noun.id)
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// Get incoming verbs
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const incomingVerbs = await db.getVerbsByTarget(noun.id)
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const nounType = noun.metadata?.noun || 'Unknown'
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const label = noun.metadata?.label || noun.id.substring(0, 8)
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console.log(`[${nounType}] ${label} (${noun.id})`)
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if (outgoingVerbs.length > 0) {
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console.log(' Outgoing:')
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for (const verb of outgoingVerbs.slice(0, limit)) {
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const targetNoun = await db.get(verb.targetId)
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const targetLabel =
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targetNoun?.metadata?.label || verb.targetId.substring(0, 8)
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console.log(
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` --(${verb.metadata?.verb || 'relates to'})--→ [${targetNoun?.metadata?.noun || 'Unknown'}] ${targetLabel}`
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)
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}
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if (outgoingVerbs.length > limit) {
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console.log(` ... and ${outgoingVerbs.length - limit} more`)
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}
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}
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if (incomingVerbs.length > 0) {
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console.log(' Incoming:')
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for (const verb of incomingVerbs.slice(0, limit)) {
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const sourceNoun = await db.get(verb.sourceId)
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const sourceLabel =
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sourceNoun?.metadata?.label || verb.sourceId.substring(0, 8)
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console.log(
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` ←--(${verb.metadata?.verb || 'relates to'})-- [${sourceNoun?.metadata?.noun || 'Unknown'}] ${sourceLabel}`
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)
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}
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if (incomingVerbs.length > limit) {
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console.log(` ... and ${incomingVerbs.length - limit} more`)
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}
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}
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console.log('')
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}
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return
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}
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|
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// If noun type is specified but no root, show all nouns of that type
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if (!rootId && nounType) {
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console.log(`Visualizing nouns of type: ${nounType}\n`)
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|
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// Search for nouns of the specified type
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const searchResults = await db.search('', 1000, {
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nounTypes: [nounType],
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forceEmbed: true
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})
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const filteredNouns = searchResults.slice(0, limit)
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|
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if (filteredNouns.length === 0) {
|
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console.log(`No nouns found with type: ${nounType}`)
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return
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}
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|
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for (const noun of filteredNouns) {
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const label = noun.metadata?.label || noun.id.substring(0, 8)
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console.log(`[${nounType}] ${label} (${noun.id})`)
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// Get outgoing verbs
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const outgoingVerbs = await db.getVerbsBySource(noun.id)
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if (outgoingVerbs.length > 0) {
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console.log(' Outgoing:')
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for (const verb of outgoingVerbs.slice(0, limit)) {
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const targetNoun = await db.get(verb.targetId)
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const targetLabel =
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targetNoun?.metadata?.label || verb.targetId.substring(0, 8)
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console.log(
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` --(${verb.metadata?.verb || 'relates to'})--→ [${targetNoun?.metadata?.noun || 'Unknown'}] ${targetLabel}`
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)
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}
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if (outgoingVerbs.length > limit) {
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console.log(` ... and ${outgoingVerbs.length - limit} more`)
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}
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}
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console.log('')
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}
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return
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}
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|
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// If root is specified, visualize the graph starting from that root
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|
if (rootId) {
|
|
const rootNoun = await db.get(rootId)
|
|
if (!rootNoun) {
|
|
console.error(`Root noun with ID ${rootId} not found`)
|
|
return
|
|
}
|
|
|
|
console.log(
|
|
`Visualizing graph from root: ${rootNoun.metadata?.label || rootId}\n`
|
|
)
|
|
|
|
// Use a breadth-first search to visualize the graph
|
|
const visited = new Set<string>()
|
|
const queue: Array<{ id: string; level: number; path: string }> = [
|
|
{ id: rootId, level: 0, path: '' }
|
|
]
|
|
|
|
while (queue.length > 0) {
|
|
const { id, level, path } = queue.shift()!
|
|
|
|
if (visited.has(id) || level > depth) {
|
|
continue
|
|
}
|
|
|
|
visited.add(id)
|
|
|
|
const noun = await db.get(id)
|
|
if (!noun) {
|
|
console.warn(`Noun with ID ${id} not found`)
|
|
continue
|
|
}
|
|
|
|
const nounType = noun.metadata?.noun || 'Unknown'
|
|
const label = noun.metadata?.label || id.substring(0, 8)
|
|
|
|
// Print the current noun with proper indentation
|
|
console.log(
|
|
`${' '.repeat(level * 2)}${path}[${nounType}] ${label} (${id})`
|
|
)
|
|
|
|
// Get outgoing verbs
|
|
const outgoingVerbs = await db.getVerbsBySource(id)
|
|
|
|
// Add target nouns to the queue for the next level
|
|
let verbCount = 0
|
|
for (const verb of outgoingVerbs) {
|
|
if (verbCount >= limit) {
|
|
console.log(
|
|
`${' '.repeat((level + 1) * 2)}... and ${outgoingVerbs.length - limit} more`
|
|
)
|
|
break
|
|
}
|
|
|
|
const verbType = verb.metadata?.verb || 'relates to'
|
|
queue.push({
|
|
id: verb.targetId,
|
|
level: level + 1,
|
|
path: `--(${verbType})--→ `
|
|
})
|
|
verbCount++
|
|
}
|
|
}
|
|
}
|
|
} catch (error) {
|
|
console.error('Error:', (error as Error).message)
|
|
process.exit(1)
|
|
}
|
|
})
|
|
|
|
program
|
|
.command('generate-random-graph')
|
|
.description(
|
|
'Generate a random graph of data with typed nouns and verbs for testing'
|
|
)
|
|
.option('-n, --noun-count <number>', 'Number of nouns to generate', '10')
|
|
.option('-v, --verb-count <number>', 'Number of verbs to generate', '20')
|
|
.option('-c, --clear', 'Clear existing data before generating', false)
|
|
.option(
|
|
'-t, --noun-types <types>',
|
|
'Comma-separated list of noun types to use'
|
|
)
|
|
.option(
|
|
'-r, --verb-types <types>',
|
|
'Comma-separated list of verb types to use'
|
|
)
|
|
.action(async (options) => {
|
|
try {
|
|
const db = createDb()
|
|
await db.init()
|
|
|
|
// Parse options
|
|
const nounCount = parseInt(options.nounCount, 10)
|
|
const verbCount = parseInt(options.verbCount, 10)
|
|
const clearExisting = options.clear
|
|
|
|
// Parse noun types if provided
|
|
let nounTypes: NounType[] | undefined
|
|
if (options.nounTypes) {
|
|
const typeNames = options.nounTypes
|
|
.split(',')
|
|
.map((t: string) => t.trim())
|
|
nounTypes = typeNames
|
|
.map((name: string) => {
|
|
// Try to match by key name (case insensitive)
|
|
const key = Object.keys(NounType).find(
|
|
(k) => k.toLowerCase() === name.toLowerCase()
|
|
)
|
|
if (key) return NounType[key as keyof typeof NounType]
|
|
|
|
// If not found by key, check if it's a valid value
|
|
if (Object.values(NounType).includes(name as NounType)) {
|
|
return name as NounType
|
|
}
|
|
|
|
console.warn(`Warning: Unknown noun type "${name}", ignoring`)
|
|
return null
|
|
})
|
|
.filter(Boolean) as NounType[]
|
|
}
|
|
|
|
// Parse verb types if provided
|
|
let verbTypes: VerbType[] | undefined
|
|
if (options.verbTypes) {
|
|
const typeNames = options.verbTypes
|
|
.split(',')
|
|
.map((t: string) => t.trim())
|
|
verbTypes = typeNames
|
|
.map((name: string) => {
|
|
// Try to match by key name (case insensitive)
|
|
const key = Object.keys(VerbType).find(
|
|
(k) => k.toLowerCase() === name.toLowerCase()
|
|
)
|
|
if (key) return VerbType[key as keyof typeof VerbType]
|
|
|
|
// If not found by key, check if it's a valid value
|
|
if (Object.values(VerbType).includes(name as VerbType)) {
|
|
return name as VerbType
|
|
}
|
|
|
|
console.warn(`Warning: Unknown verb type "${name}", ignoring`)
|
|
return null
|
|
})
|
|
.filter(Boolean) as VerbType[]
|
|
}
|
|
|
|
console.log(
|
|
`Generating random graph with ${nounCount} nouns and ${verbCount} verbs...`
|
|
)
|
|
if (clearExisting) {
|
|
console.log('Clearing existing data first...')
|
|
}
|
|
|
|
const result = await db.generateRandomGraph({
|
|
nounCount,
|
|
verbCount,
|
|
nounTypes,
|
|
verbTypes,
|
|
clearExisting
|
|
})
|
|
|
|
console.log('Random graph generated successfully!')
|
|
console.log(
|
|
`Created ${result.nounIds.length} nouns and ${result.verbIds.length} verbs`
|
|
)
|
|
|
|
// Print some sample IDs
|
|
if (result.nounIds.length > 0) {
|
|
console.log('\nSample noun IDs:')
|
|
result.nounIds.slice(0, 3).forEach((id) => console.log(`- ${id}`))
|
|
if (result.nounIds.length > 3) {
|
|
console.log(`... and ${result.nounIds.length - 3} more`)
|
|
}
|
|
}
|
|
|
|
if (result.verbIds.length > 0) {
|
|
console.log('\nSample verb IDs:')
|
|
result.verbIds.slice(0, 3).forEach((id) => console.log(`- ${id}`))
|
|
if (result.verbIds.length > 3) {
|
|
console.log(`... and ${result.verbIds.length - 3} more`)
|
|
}
|
|
}
|
|
|
|
console.log(
|
|
'\nUse the search, get, or visualize commands to explore the generated graph'
|
|
)
|
|
} catch (error) {
|
|
console.error('Error:', (error as Error).message)
|
|
process.exit(1)
|
|
}
|
|
})
|
|
|
|
// Add examples to help text
|
|
program.addHelpText(
|
|
'after',
|
|
`
|
|
Examples:
|
|
$ brainy init
|
|
$ brainy add "Cats are independent pets" '{"noun":"Thing","category":"animal"}'
|
|
$ brainy search "feline pets" --limit 5
|
|
$ brainy addVerb id1 id2 RelatedTo '{"description":"Both are pets"}'
|
|
$ brainy clear --force
|
|
$ brainy generate-random-graph --noun-count 20 --verb-count 30 --clear
|
|
$ brainy generate-random-graph --noun-types Person,Thing --verb-types RelatedTo,Owns
|
|
$ brainy visualize --type Thing --limit 10
|
|
$ brainy visualize --root id1 --depth 3
|
|
$ brainy backup my-database-backup.json
|
|
$ brainy restore my-database-backup.json --clear
|
|
|
|
# Augmentation commands
|
|
$ brainy augment list
|
|
$ brainy augment info cognition
|
|
$ brainy augment test-pipeline "Test data" --data-type text --mode sequential
|
|
$ brainy augment stream-test --count 3 --interval 500
|
|
|
|
`
|
|
)
|
|
|
|
// Setup autocomplete
|
|
const completion = omelette('brainy')
|
|
|
|
// Helper function to get all noun types
|
|
const getNounTypes = () => Object.keys(NounType)
|
|
|
|
// Helper function to get all verb types
|
|
const getVerbTypes = () => Object.keys(VerbType)
|
|
|
|
// Define autocomplete handlers
|
|
completion.tree({
|
|
// First level commands - suggest all available commands
|
|
_: () => [
|
|
'add',
|
|
'addVerb',
|
|
'search',
|
|
'get',
|
|
'delete',
|
|
'getVerbs',
|
|
'status',
|
|
'clear',
|
|
'visualize',
|
|
'generate-random-graph',
|
|
'backup',
|
|
'restore',
|
|
'completion-setup',
|
|
'init',
|
|
'help',
|
|
'augment'
|
|
],
|
|
// Command-specific completions
|
|
add: {
|
|
// For the second argument of 'add' command (metadata)
|
|
_: () => {
|
|
// Generate templates for each noun type
|
|
return getNounTypes().map(
|
|
(type) => `{"noun":"${type}","category":"example"}`
|
|
)
|
|
}
|
|
},
|
|
addVerb: {
|
|
// First two arguments are IDs, third is verb type
|
|
'<sourceId>': {
|
|
'<targetId>': {
|
|
_: () => {
|
|
// Suggest all available verb types
|
|
return getVerbTypes()
|
|
}
|
|
}
|
|
}
|
|
},
|
|
// Add autocomplete for other commands
|
|
search: {},
|
|
get: {},
|
|
delete: {},
|
|
getVerbs: {},
|
|
status: {},
|
|
clear: {
|
|
_: () => ['--force']
|
|
},
|
|
backup: {
|
|
_: () => ['brainy-backup.json', 'database-backup.json']
|
|
},
|
|
restore: {
|
|
_: () => ['--clear']
|
|
},
|
|
'generate-random-graph': {
|
|
_: () => [
|
|
'--noun-count 10',
|
|
'--verb-count 20',
|
|
'--clear',
|
|
`--noun-types ${getNounTypes().join(',')}`,
|
|
`--verb-types ${getVerbTypes().join(',')}`
|
|
]
|
|
},
|
|
augment: {
|
|
_: () => ['list', 'info', 'test-pipeline', 'stream-test'],
|
|
info: {
|
|
_: () => [
|
|
'sense',
|
|
'memory',
|
|
'cognition',
|
|
'conduit',
|
|
'activation',
|
|
'perception',
|
|
'dialog',
|
|
'websocket'
|
|
]
|
|
},
|
|
'test-pipeline': {
|
|
_: () => [
|
|
'--data-type text',
|
|
'--mode sequential',
|
|
'--mode parallel',
|
|
'--mode threaded',
|
|
'--stop-on-error',
|
|
'--verbose'
|
|
]
|
|
},
|
|
'stream-test': {
|
|
_: () => ['--count 5', '--interval 1000', '--data-type text', '--verbose']
|
|
}
|
|
},
|
|
'completion-setup': {},
|
|
init: {},
|
|
help: {},
|
|
})
|
|
|
|
// Initialize autocomplete
|
|
completion.init()
|
|
|
|
// If this script is run with --completion-setup flag, set up the autocomplete
|
|
if (process.argv.includes('--completion-setup')) {
|
|
completion.setupShellInitFile()
|
|
console.log('Autocomplete setup complete. Please restart your shell.')
|
|
process.exit(0)
|
|
}
|
|
|
|
// Pipeline and Augmentation Commands
|
|
const augmentCommand = new Command('augment').description(
|
|
'Augmentation pipeline operations'
|
|
)
|
|
|
|
augmentCommand
|
|
.command('list')
|
|
.description(
|
|
'List all available augmentation types and registered augmentations'
|
|
)
|
|
.action(async () => {
|
|
try {
|
|
// Initialize the pipeline
|
|
await augmentationPipeline.initialize()
|
|
|
|
// Get available augmentation types
|
|
const availableTypes =
|
|
augmentationPipeline.getAvailableAugmentationTypes()
|
|
|
|
console.log('Available Augmentation Types:')
|
|
if (availableTypes.length === 0) {
|
|
console.log(' No augmentation types available')
|
|
} else {
|
|
availableTypes.forEach((type) => {
|
|
const augmentations =
|
|
augmentationPipeline.getAugmentationsByType(type)
|
|
console.log(
|
|
`\n${type.toUpperCase()} (${augmentations.length} registered):`
|
|
)
|
|
|
|
if (augmentations.length === 0) {
|
|
console.log(' No augmentations registered for this type')
|
|
} else {
|
|
augmentations.forEach((aug) => {
|
|
console.log(` - ${aug.name}: ${aug.description}`)
|
|
console.log(` Status: ${aug.enabled ? 'Enabled' : 'Disabled'}`)
|
|
})
|
|
}
|
|
})
|
|
}
|
|
|
|
// Show WebSocket augmentations separately
|
|
const webSocketAugs = augmentationPipeline.getWebSocketAugmentations()
|
|
console.log('\nWebSocket-Enabled Augmentations:')
|
|
if (webSocketAugs.length === 0) {
|
|
console.log(' No WebSocket-enabled augmentations available')
|
|
} else {
|
|
webSocketAugs.forEach((aug) => {
|
|
console.log(` - ${aug.name}: ${aug.description}`)
|
|
console.log(` Status: ${aug.enabled ? 'Enabled' : 'Disabled'}`)
|
|
})
|
|
}
|
|
} catch (error) {
|
|
console.error('Error:', (error as Error).message)
|
|
process.exit(1)
|
|
}
|
|
})
|
|
|
|
augmentCommand
|
|
.command('test-pipeline')
|
|
.description('Test the sequential pipeline with sample data')
|
|
.argument(
|
|
'[text]',
|
|
'Sample text to process through the pipeline',
|
|
'This is a test of the Brainy pipeline'
|
|
)
|
|
.option('-t, --data-type <type>', 'Type of data to process', 'text')
|
|
.option(
|
|
'-m, --mode <mode>',
|
|
'Execution mode (sequential, parallel, threaded)',
|
|
'sequential'
|
|
)
|
|
.option('-s, --stop-on-error', 'Stop execution if an error occurs', false)
|
|
.option('-v, --verbose', 'Show detailed output', false)
|
|
.action(async (text, options) => {
|
|
try {
|
|
// Initialize the pipeline
|
|
await sequentialPipeline.initialize()
|
|
|
|
console.log(`Processing data: "${text}"`)
|
|
console.log(`Data type: ${options.dataType}`)
|
|
console.log(`Execution mode: ${options.mode}`)
|
|
console.log(`Stop on error: ${options.stopOnError}`)
|
|
console.log()
|
|
|
|
// Set execution mode
|
|
let executionMode = ExecutionMode.SEQUENTIAL
|
|
switch (options.mode.toLowerCase()) {
|
|
case 'parallel':
|
|
executionMode = ExecutionMode.PARALLEL
|
|
break
|
|
case 'threaded':
|
|
executionMode = ExecutionMode.THREADED
|
|
break
|
|
default:
|
|
executionMode = ExecutionMode.SEQUENTIAL
|
|
}
|
|
|
|
// Process the data
|
|
const result = await sequentialPipeline.processData(
|
|
text,
|
|
options.dataType,
|
|
{
|
|
stopOnError: options.stopOnError,
|
|
timeout: 30000
|
|
}
|
|
)
|
|
|
|
console.log('Pipeline Execution Result:')
|
|
console.log(`Success: ${result.success}`)
|
|
|
|
if (result.error) {
|
|
console.log(`Error: ${result.error}`)
|
|
}
|
|
|
|
console.log('\nStage Results:')
|
|
|
|
// Display stage results
|
|
Object.entries(result.stageResults).forEach(([stage, stageResult]) => {
|
|
console.log(`\n${stage.toUpperCase()}:`)
|
|
console.log(` Success: ${stageResult?.success}`)
|
|
|
|
if (stageResult?.error) {
|
|
console.log(` Error: ${stageResult.error}`)
|
|
}
|
|
|
|
if (stageResult?.data && options.verbose) {
|
|
console.log(' Data:')
|
|
console.log(
|
|
JSON.stringify(stageResult.data, null, 2)
|
|
.split('\n')
|
|
.map((line) => ` ${line}`)
|
|
.join('\n')
|
|
)
|
|
}
|
|
})
|
|
|
|
console.log('\nFinal Result Data:')
|
|
console.log(
|
|
JSON.stringify(result.data, null, 2)
|
|
.split('\n')
|
|
.map((line) => ` ${line}`)
|
|
.join('\n')
|
|
)
|
|
} catch (error) {
|
|
console.error('Error:', (error as Error).message)
|
|
process.exit(1)
|
|
}
|
|
})
|
|
|
|
augmentCommand
|
|
.command('stream-test')
|
|
.description('Test streaming data through the pipeline (simulated)')
|
|
.option('-c, --count <number>', 'Number of data items to stream', '5')
|
|
.option(
|
|
'-i, --interval <ms>',
|
|
'Interval between data items in milliseconds',
|
|
'1000'
|
|
)
|
|
.option('-t, --data-type <type>', 'Type of data to process', 'text')
|
|
.option('-v, --verbose', 'Show detailed output', false)
|
|
.action(async (options) => {
|
|
try {
|
|
// Initialize the pipeline
|
|
await sequentialPipeline.initialize()
|
|
|
|
const count = parseInt(options.count, 10)
|
|
const interval = parseInt(options.interval, 10)
|
|
|
|
console.log(
|
|
`Simulating stream of ${count} data items at ${interval}ms intervals`
|
|
)
|
|
console.log(`Data type: ${options.dataType}`)
|
|
console.log()
|
|
|
|
// Create a handler function similar to what would be used with WebSockets
|
|
const handler = (data: string) => {
|
|
// Process the data asynchronously without blocking
|
|
sequentialPipeline
|
|
.processData(data, options.dataType, { stopOnError: false })
|
|
.then((result) => {
|
|
console.log(`\nProcessed: "${data}"`)
|
|
console.log(`Success: ${result.success}`)
|
|
|
|
if (options.verbose) {
|
|
console.log('Stage Results:')
|
|
Object.entries(result.stageResults).forEach(
|
|
([stage, stageResult]) => {
|
|
if (stageResult?.success) {
|
|
console.log(` ${stage}: Success`)
|
|
} else {
|
|
console.log(
|
|
` ${stage}: Failed - ${stageResult?.error || 'Unknown error'}`
|
|
)
|
|
}
|
|
}
|
|
)
|
|
}
|
|
|
|
if (result.data) {
|
|
console.log('Result Data:')
|
|
console.log(
|
|
JSON.stringify(result.data, null, 2)
|
|
.split('\n')
|
|
.map((line) => ` ${line}`)
|
|
.join('\n')
|
|
)
|
|
}
|
|
})
|
|
.catch((error) => {
|
|
console.error(`Error processing "${data}":`, error.message)
|
|
})
|
|
}
|
|
|
|
// Generate sample data items
|
|
const sampleTexts = [
|
|
'The quick brown fox jumps over the lazy dog',
|
|
'Artificial intelligence is transforming how we interact with data',
|
|
'Vector databases enable semantic search capabilities',
|
|
'Graph relationships connect entities in meaningful ways',
|
|
'Streaming data requires efficient real-time processing',
|
|
'WebSockets provide bidirectional communication channels',
|
|
'Augmentations extend the functionality of the core system',
|
|
'Sequential pipelines process data in defined stages',
|
|
'Parallel execution improves throughput for large datasets',
|
|
'Threaded operations utilize multiple CPU cores efficiently'
|
|
]
|
|
|
|
// Simulate streaming data
|
|
console.log('Starting simulated data stream...')
|
|
|
|
for (let i = 0; i < count; i++) {
|
|
// Use modulo to cycle through sample texts if count > samples
|
|
const text = sampleTexts[i % sampleTexts.length]
|
|
|
|
// Wait for the specified interval
|
|
if (i > 0) {
|
|
await new Promise((resolve) => setTimeout(resolve, interval))
|
|
}
|
|
|
|
console.log(`\nStreaming item ${i + 1}/${count}: "${text}"`)
|
|
|
|
// Process the data
|
|
handler(text)
|
|
}
|
|
|
|
console.log(
|
|
'\nSimulated stream complete. Some processing may still be ongoing.'
|
|
)
|
|
console.log(
|
|
'In a real WebSocket scenario, the connection would remain open for continuous data.'
|
|
)
|
|
} catch (error) {
|
|
console.error('Error:', (error as Error).message)
|
|
process.exit(1)
|
|
}
|
|
})
|
|
|
|
augmentCommand
|
|
.command('info')
|
|
.description('Get detailed information about a specific augmentation type')
|
|
.argument(
|
|
'<type>',
|
|
'Augmentation type (sense, memory, cognition, conduit, activation, perception, dialog, websocket)'
|
|
)
|
|
.action(async (typeArg) => {
|
|
try {
|
|
// Initialize the pipeline
|
|
await augmentationPipeline.initialize()
|
|
|
|
// Resolve the augmentation type
|
|
let augType: AugmentationType | undefined
|
|
|
|
// Convert input to proper enum value
|
|
const normalizedType = typeArg.toLowerCase()
|
|
switch (normalizedType) {
|
|
case 'sense':
|
|
augType = AugmentationType.SENSE
|
|
break
|
|
case 'memory':
|
|
augType = AugmentationType.MEMORY
|
|
break
|
|
case 'cognition':
|
|
augType = AugmentationType.COGNITION
|
|
break
|
|
case 'conduit':
|
|
augType = AugmentationType.CONDUIT
|
|
break
|
|
case 'activation':
|
|
augType = AugmentationType.ACTIVATION
|
|
break
|
|
case 'perception':
|
|
augType = AugmentationType.PERCEPTION
|
|
break
|
|
case 'dialog':
|
|
augType = AugmentationType.DIALOG
|
|
break
|
|
case 'websocket':
|
|
augType = AugmentationType.WEBSOCKET
|
|
break
|
|
default:
|
|
console.error(`Unknown augmentation type: ${typeArg}`)
|
|
console.log(
|
|
'Available types: sense, memory, cognition, conduit, activation, perception, dialog, websocket'
|
|
)
|
|
process.exit(1)
|
|
}
|
|
|
|
// Get augmentations of the specified type
|
|
const augmentations = augmentationPipeline.getAugmentationsByType(augType)
|
|
|
|
console.log(`\n${augType.toUpperCase()} Augmentation Details:`)
|
|
|
|
if (augmentations.length === 0) {
|
|
console.log(' No augmentations registered for this type')
|
|
} else {
|
|
// Display information about each augmentation
|
|
augmentations.forEach((aug, index) => {
|
|
console.log(`\n${index + 1}. ${aug.name}`)
|
|
console.log(` Description: ${aug.description}`)
|
|
console.log(` Status: ${aug.enabled ? 'Enabled' : 'Disabled'}`)
|
|
|
|
// List available methods
|
|
console.log(' Available Methods:')
|
|
|
|
// Get all methods that aren't from Object.prototype
|
|
const methods = Object.getOwnPropertyNames(
|
|
Object.getPrototypeOf(aug)
|
|
).filter(
|
|
(method) =>
|
|
method !== 'constructor' &&
|
|
typeof (aug as any)[method] === 'function' &&
|
|
!['initialize', 'shutDown', 'getStatus'].includes(method)
|
|
)
|
|
|
|
if (methods.length === 0) {
|
|
console.log(' No custom methods available')
|
|
} else {
|
|
methods.forEach((method) => {
|
|
console.log(` - ${method}`)
|
|
})
|
|
}
|
|
})
|
|
}
|
|
|
|
// Show pipeline order information
|
|
console.log('\nPipeline Execution Order:')
|
|
console.log(
|
|
' 1. SENSE - Process raw data into structured nouns and verbs'
|
|
)
|
|
console.log(' 2. MEMORY - Store and retrieve data')
|
|
console.log(' 3. COGNITION - Analyze and reason about data')
|
|
console.log(' 4. CONDUIT - Exchange data with external systems')
|
|
console.log(' 5. ACTIVATION - Trigger actions based on data')
|
|
console.log(' 6. PERCEPTION - Interpret and visualize data')
|
|
console.log(' 7. DIALOG - Process natural language interactions')
|
|
console.log(
|
|
' * WEBSOCKET - Enable real-time communication (can be combined with other types)'
|
|
)
|
|
} catch (error) {
|
|
console.error('Error:', (error as Error).message)
|
|
process.exit(1)
|
|
}
|
|
})
|
|
|
|
// Add the augment command to the program
|
|
program.addCommand(augmentCommand)
|
|
|
|
// Add a command for setting up autocomplete
|
|
program
|
|
.command('completion-setup')
|
|
.description('Setup shell autocomplete for the Brainy CLI')
|
|
.action(() => {
|
|
completion.setupShellInitFile()
|
|
console.log('Autocomplete setup complete. Please restart your shell.')
|
|
})
|
|
|
|
|
|
// Parse command line arguments
|
|
|
|
|
|
program.parse()
|