CHECKPOINT: Brainy 2.0 API refactor - pre-fixes state
Current state: - Unified augmentation system to BrainyAugmentation interface - Changed methods to specific noun/verb naming (addNoun, getNoun, etc) - Made old methods private - Combined getNouns into single unified method - Neural API exists and is complete - Triple Intelligence uses correct Brainy operators (not MongoDB) Issues identified: - Documentation incorrectly shows MongoDB operators (code is correct) - Need to ensure all features are properly exposed - Need to verify nothing was lost in simplification This commit serves as a rollback point before applying fixes.
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docs/API-EXPOSURE-ARCHITECTURE.md
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549
docs/API-EXPOSURE-ARCHITECTURE.md
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# 🌐 Brainy API Exposure Architecture
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## 📡 Current State: Built-in MCP Support
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Brainy **already has** Model Context Protocol (MCP) support built-in:
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```typescript
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// Already exists in Brainy!
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import { BrainyMCPService } from 'brainy/mcp'
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const brain = new BrainyData()
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const mcpService = new BrainyMCPService(brain)
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// Handles MCP requests
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const response = await mcpService.handleRequest({
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type: 'data_access',
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operation: 'search',
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parameters: { query: 'find documents' }
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})
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```
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### What's Already Built:
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- **BrainyMCPAdapter** - Exposes data operations
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- **MCPAugmentationToolset** - Exposes augmentations as MCP tools
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- **BrainyMCPService** - Unified service layer
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- **ServerSearchConduitAugmentation** - Connect to remote Brainy instances
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## 🚀 The Missing Piece: API Server Augmentation
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What's **NOT** built yet is a unified API server that exposes REST, WebSocket, and MCP over network. This SHOULD be an augmentation!
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```typescript
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/**
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* Universal API Server Augmentation
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* Exposes Brainy through REST, WebSocket, MCP, and GraphQL
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*/
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export class APIServerAugmentation extends BaseAugmentation {
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readonly name = 'api-server'
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readonly timing = 'after' as const
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readonly operations = ['all'] as const // Monitor all operations
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readonly priority = 5 // Low priority, runs last
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private httpServer?: any
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private wsServer?: any
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private mcpService?: BrainyMCPService
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private clients = new Set<any>()
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private apiKeys = new Map<string, any>()
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protected async onInitialize(): Promise<void> {
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const config = this.context.config.apiServer || {}
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if (!config.enabled) {
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this.log('API Server disabled in config')
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return
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}
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// Initialize MCP service
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this.mcpService = new BrainyMCPService(this.context.brain, {
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enableAuth: config.requireAuth
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})
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// Start servers based on environment
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if (typeof process !== 'undefined' && process.versions?.node) {
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await this.startNodeServers(config)
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} else if (typeof Deno !== 'undefined') {
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await this.startDenoServer(config)
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} else if (typeof self !== 'undefined') {
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await this.startServiceWorker(config)
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}
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}
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private async startNodeServers(config: any) {
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const express = await import('express')
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const { WebSocketServer } = await import('ws')
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const cors = await import('cors')
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const app = express.default()
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// Middleware
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app.use(cors.default(config.cors))
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app.use(express.json())
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app.use(this.authMiddleware.bind(this))
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app.use(this.rateLimitMiddleware.bind(this))
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// REST API Routes
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this.setupRESTRoutes(app)
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// Start HTTP server
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this.httpServer = app.listen(config.port || 3000, () => {
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this.log(`REST API listening on port ${config.port || 3000}`)
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})
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// WebSocket server for real-time
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this.wsServer = new WebSocketServer({
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server: this.httpServer,
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path: '/ws'
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})
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this.setupWebSocketServer()
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// MCP over WebSocket
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this.setupMCPWebSocket()
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}
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private setupRESTRoutes(app: any) {
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// Health check
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app.get('/health', (req: any, res: any) => {
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res.json({ status: 'healthy', version: '2.0.0' })
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})
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// Search endpoint
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app.post('/api/search', async (req: any, res: any) => {
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try {
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const { query, limit = 10, options = {} } = req.body
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const results = await this.context.brain.search(query, limit, options)
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res.json({ success: true, results })
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} catch (error) {
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res.status(500).json({
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success: false,
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error: error.message
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})
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}
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})
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// Add data endpoint
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app.post('/api/add', async (req: any, res: any) => {
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try {
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const { content, metadata } = req.body
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const id = await this.context.brain.add(content, metadata)
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res.json({ success: true, id })
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} catch (error) {
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res.status(500).json({
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success: false,
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error: error.message
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})
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}
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})
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// Get endpoint
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app.get('/api/get/:id', async (req: any, res: any) => {
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try {
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const data = await this.context.brain.get(req.params.id)
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res.json({ success: true, data })
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} catch (error) {
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res.status(404).json({
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success: false,
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error: 'Not found'
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})
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}
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})
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// Delete endpoint
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app.delete('/api/delete/:id', async (req: any, res: any) => {
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try {
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await this.context.brain.delete(req.params.id)
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res.json({ success: true })
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} catch (error) {
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res.status(500).json({
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success: false,
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error: error.message
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})
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}
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})
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// Relate endpoint
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app.post('/api/relate', async (req: any, res: any) => {
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try {
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const { source, target, verb, metadata } = req.body
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await this.context.brain.relate(source, target, verb, metadata)
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res.json({ success: true })
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} catch (error) {
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res.status(500).json({
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success: false,
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error: error.message
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})
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}
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})
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// Find endpoint (complex queries)
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app.post('/api/find', async (req: any, res: any) => {
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try {
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const results = await this.context.brain.find(req.body)
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res.json({ success: true, results })
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} catch (error) {
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res.status(500).json({
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success: false,
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error: error.message
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})
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}
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})
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// Cluster endpoint
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app.post('/api/cluster', async (req: any, res: any) => {
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try {
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const { algorithm = 'kmeans', options = {} } = req.body
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const clusters = await this.context.brain.cluster(algorithm, options)
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res.json({ success: true, clusters })
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} catch (error) {
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res.status(500).json({
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success: false,
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error: error.message
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})
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}
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})
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// MCP endpoint (for non-WebSocket MCP)
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app.post('/api/mcp', async (req: any, res: any) => {
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try {
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const response = await this.mcpService.handleRequest(req.body)
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res.json(response)
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} catch (error) {
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res.status(500).json({
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success: false,
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error: error.message
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})
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}
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})
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// GraphQL endpoint (optional)
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if (this.context.config.apiServer?.enableGraphQL) {
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this.setupGraphQL(app)
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}
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}
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private setupWebSocketServer() {
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this.wsServer.on('connection', (ws: any) => {
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this.clients.add(ws)
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ws.on('message', async (message: string) => {
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try {
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const msg = JSON.parse(message)
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await this.handleWebSocketMessage(msg, ws)
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} catch (error) {
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ws.send(JSON.stringify({
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type: 'error',
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error: error.message
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}))
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}
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})
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ws.on('close', () => {
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this.clients.delete(ws)
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})
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})
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}
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private async handleWebSocketMessage(msg: any, ws: any) {
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switch (msg.type) {
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case 'subscribe':
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// Subscribe to operations
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ws.subscriptions = msg.operations || ['all']
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ws.send(JSON.stringify({
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type: 'subscribed',
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operations: ws.subscriptions
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}))
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break
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case 'search':
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const results = await this.context.brain.search(msg.query, msg.limit)
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ws.send(JSON.stringify({
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type: 'searchResults',
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results
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}))
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break
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case 'mcp':
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// Handle MCP over WebSocket
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const response = await this.mcpService.handleRequest(msg.request)
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ws.send(JSON.stringify({
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type: 'mcpResponse',
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response
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}))
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break
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}
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}
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private setupMCPWebSocket() {
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// Dedicated MCP WebSocket endpoint
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const { WebSocketServer } = require('ws')
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const mcpWs = new WebSocketServer({
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port: (this.context.config.apiServer?.mcpPort || 3001),
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path: '/mcp'
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})
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mcpWs.on('connection', (ws: any) => {
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ws.on('message', async (message: string) => {
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try {
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const request = JSON.parse(message)
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const response = await this.mcpService.handleRequest(request)
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ws.send(JSON.stringify(response))
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} catch (error) {
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ws.send(JSON.stringify({
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error: error.message,
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type: 'error'
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}))
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}
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})
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})
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this.log(`MCP WebSocket listening on port ${this.context.config.apiServer?.mcpPort || 3001}`)
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}
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// Broadcast changes to all connected clients
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async execute<T>(operation: string, params: any, next: () => Promise<T>): Promise<T> {
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const result = await next()
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// Broadcast to WebSocket clients
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if (this.clients.size > 0) {
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const message = JSON.stringify({
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type: 'operation',
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operation,
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params: this.sanitizeParams(params),
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timestamp: Date.now()
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})
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for (const client of this.clients) {
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if (client.subscriptions?.includes('all') ||
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client.subscriptions?.includes(operation)) {
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client.send(message)
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}
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}
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}
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return result
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}
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private authMiddleware(req: any, res: any, next: any) {
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if (!this.context.config.apiServer?.requireAuth) {
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return next()
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}
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const apiKey = req.headers['x-api-key']
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if (!apiKey || !this.apiKeys.has(apiKey)) {
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return res.status(401).json({ error: 'Unauthorized' })
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}
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req.user = this.apiKeys.get(apiKey)
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next()
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}
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private rateLimitMiddleware(req: any, res: any, next: any) {
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// Simple rate limiting
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const ip = req.ip
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const limit = this.context.config.apiServer?.rateLimit || 100
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// Implementation details...
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next()
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}
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private sanitizeParams(params: any) {
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// Remove sensitive data before broadcasting
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const safe = { ...params }
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delete safe.apiKey
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delete safe.password
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return safe
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}
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protected async onShutdown() {
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// Close all connections
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for (const client of this.clients) {
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client.close()
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}
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// Close servers
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if (this.httpServer) {
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await new Promise(resolve => this.httpServer.close(resolve))
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}
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if (this.wsServer) {
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this.wsServer.close()
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}
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}
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}
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```
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## 🎯 Usage: Deploy Brainy as a Server
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```typescript
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import { BrainyData } from 'brainy'
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import { APIServerAugmentation } from 'brainy/augmentations'
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const brain = new BrainyData({
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apiServer: {
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enabled: true,
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port: 3000,
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mcpPort: 3001,
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requireAuth: true,
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rateLimit: 100,
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cors: { origin: '*' },
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enableGraphQL: false
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}
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})
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// Register the API server augmentation
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brain.augmentations.register(new APIServerAugmentation())
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await brain.init()
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console.log('Brainy API Server running!')
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console.log('REST API: http://localhost:3000')
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console.log('WebSocket: ws://localhost:3000/ws')
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console.log('MCP: ws://localhost:3001/mcp')
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```
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## 🔌 Client Usage
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### REST API
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```javascript
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// Search
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const response = await fetch('http://localhost:3000/api/search', {
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method: 'POST',
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headers: {
|
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'Content-Type': 'application/json',
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'X-API-Key': 'your-key'
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},
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body: JSON.stringify({
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query: 'find documents about AI',
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limit: 10
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})
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})
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const { results } = await response.json()
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```
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|
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### WebSocket (Real-time)
|
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```javascript
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const ws = new WebSocket('ws://localhost:3000/ws')
|
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|
||||
ws.onopen = () => {
|
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// Subscribe to operations
|
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ws.send(JSON.stringify({
|
||||
type: 'subscribe',
|
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operations: ['add', 'delete', 'relate']
|
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}))
|
||||
}
|
||||
|
||||
ws.onmessage = (event) => {
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const msg = JSON.parse(event.data)
|
||||
console.log('Operation:', msg.operation, msg.params)
|
||||
}
|
||||
```
|
||||
|
||||
### MCP (AI Agents)
|
||||
```javascript
|
||||
const mcpWs = new WebSocket('ws://localhost:3001/mcp')
|
||||
|
||||
mcpWs.send(JSON.stringify({
|
||||
type: 'data_access',
|
||||
operation: 'search',
|
||||
requestId: '123',
|
||||
parameters: { query: 'test' }
|
||||
}))
|
||||
|
||||
mcpWs.onmessage = (event) => {
|
||||
const response = JSON.parse(event.data)
|
||||
console.log('MCP Response:', response)
|
||||
}
|
||||
```
|
||||
|
||||
## 🏗️ Architecture Benefits
|
||||
|
||||
### Why as an Augmentation?
|
||||
|
||||
1. **Optional** - Not everyone needs a server
|
||||
2. **Configurable** - Easy to enable/disable
|
||||
3. **Extensible** - Add custom endpoints
|
||||
4. **Integrated** - Hooks into all operations
|
||||
5. **Real-time** - Broadcasts changes automatically
|
||||
|
||||
### Security Features
|
||||
|
||||
- **API Key Authentication**
|
||||
- **Rate Limiting**
|
||||
- **CORS Configuration**
|
||||
- **Parameter Sanitization**
|
||||
- **SSL/TLS Support** (with proper certs)
|
||||
|
||||
## 🌍 Deployment Options
|
||||
|
||||
### Local Development
|
||||
```bash
|
||||
npm install brainy
|
||||
node server.js # Your server file with APIServerAugmentation
|
||||
```
|
||||
|
||||
### Docker Container
|
||||
```dockerfile
|
||||
FROM node:18-alpine
|
||||
WORKDIR /app
|
||||
COPY package*.json ./
|
||||
RUN npm install brainy
|
||||
COPY server.js .
|
||||
EXPOSE 3000 3001
|
||||
CMD ["node", "server.js"]
|
||||
```
|
||||
|
||||
### Cloud Deployment
|
||||
Deploy to any Node.js hosting:
|
||||
- Vercel Edge Functions
|
||||
- Cloudflare Workers (with adapter)
|
||||
- AWS Lambda (with adapter)
|
||||
- Google Cloud Run
|
||||
- Traditional VPS
|
||||
|
||||
### Browser Service Worker
|
||||
```javascript
|
||||
// In browser, use Service Worker for local API
|
||||
if ('serviceWorker' in navigator) {
|
||||
// APIServerAugmentation can create a Service Worker
|
||||
// that intercepts fetch() calls and handles them locally
|
||||
}
|
||||
```
|
||||
|
||||
## 🎯 The Complete Picture
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────┐
|
||||
│ Client Applications │
|
||||
├─────────────┬───────────┬──────────────────┤
|
||||
│ REST │ WebSocket │ MCP │
|
||||
└──────┬──────┴─────┬─────┴──────┬───────────┘
|
||||
│ │ │
|
||||
▼ ▼ ▼
|
||||
┌─────────────────────────────────────────────┐
|
||||
│ APIServerAugmentation │
|
||||
│ (Unified API exposure as augmentation) │
|
||||
└─────────────────┬───────────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────────────────────────┐
|
||||
│ BrainyData Core │
|
||||
│ (with all augmentations in pipeline) │
|
||||
└─────────────────────────────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────────────────────────┐
|
||||
│ Storage Layer │
|
||||
│ (FileSystem, S3, OPFS, Memory) │
|
||||
└─────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
## 🚀 Summary
|
||||
|
||||
1. **MCP is built-in** - Already in Brainy core
|
||||
2. **API Server should be an augmentation** - Optional, configurable
|
||||
3. **Exposes everything** - REST, WebSocket, MCP, GraphQL
|
||||
4. **Real-time by default** - Broadcasts all operations
|
||||
5. **Secure** - Auth, rate limiting, CORS
|
||||
6. **Deploy anywhere** - Node, Deno, Browser, Cloud
|
||||
|
||||
The beauty is that the API server is just another augmentation - it hooks into the pipeline like everything else and exposes Brainy's capabilities to the world!
|
||||
774
docs/AUGMENTATION-EXAMPLES.md
Normal file
774
docs/AUGMENTATION-EXAMPLES.md
Normal file
|
|
@ -0,0 +1,774 @@
|
|||
# 🚀 Real-World Augmentation Examples
|
||||
|
||||
## 1. 💬 Chat Interface Augmentation
|
||||
**"Talk to your data through natural language"**
|
||||
|
||||
```typescript
|
||||
import { BaseAugmentation } from './brainyAugmentation.js'
|
||||
|
||||
export class ChatInterfaceAugmentation extends BaseAugmentation {
|
||||
readonly name = 'chat-interface'
|
||||
readonly timing = 'after' as const // Process after operations
|
||||
readonly operations = ['search', 'add', 'delete'] as const
|
||||
readonly priority = 30 // Medium priority
|
||||
|
||||
private chatHistory: Array<{role: string, content: string}> = []
|
||||
private llmClient: any // User's chosen LLM
|
||||
|
||||
protected async onInitialize(): Promise<void> {
|
||||
// User provides their own LLM
|
||||
this.llmClient = this.context.config.llmClient || null
|
||||
if (!this.llmClient) {
|
||||
this.log('Chat augmentation needs LLM client in config')
|
||||
}
|
||||
}
|
||||
|
||||
async execute<T>(operation: string, params: any, next: () => Promise<T>): Promise<T> {
|
||||
// If params include natural language query
|
||||
if (params.chatQuery) {
|
||||
// Convert natural language to Brainy operations
|
||||
const intent = await this.parseIntent(params.chatQuery)
|
||||
|
||||
// Transform params based on intent
|
||||
if (intent.type === 'search') {
|
||||
params.query = intent.query
|
||||
params.k = intent.limit || 10
|
||||
} else if (intent.type === 'add') {
|
||||
params.content = intent.content
|
||||
params.metadata = { ...params.metadata, source: 'chat' }
|
||||
}
|
||||
|
||||
// Store in chat history
|
||||
this.chatHistory.push({
|
||||
role: 'user',
|
||||
content: params.chatQuery
|
||||
})
|
||||
}
|
||||
|
||||
// Execute the operation
|
||||
const result = await next()
|
||||
|
||||
// Generate conversational response
|
||||
if (params.chatQuery && this.llmClient) {
|
||||
const response = await this.generateResponse(operation, result)
|
||||
|
||||
this.chatHistory.push({
|
||||
role: 'assistant',
|
||||
content: response
|
||||
})
|
||||
|
||||
// Enhance result with chat response
|
||||
return {
|
||||
...result,
|
||||
chatResponse: response,
|
||||
chatHistory: this.chatHistory
|
||||
} as T
|
||||
}
|
||||
|
||||
return result
|
||||
}
|
||||
|
||||
private async parseIntent(query: string) {
|
||||
// Use Brainy's NLP patterns + LLM to understand intent
|
||||
const prompt = `Parse this query into a Brainy operation:
|
||||
Query: ${query}
|
||||
|
||||
Return JSON with:
|
||||
- type: 'search' | 'add' | 'delete' | 'relate'
|
||||
- query: search terms or content
|
||||
- filters: any metadata filters
|
||||
- limit: number of results`
|
||||
|
||||
const response = await this.llmClient.complete(prompt)
|
||||
return JSON.parse(response)
|
||||
}
|
||||
|
||||
private async generateResponse(operation: string, result: any) {
|
||||
const prompt = `Generate a friendly response for this operation:
|
||||
Operation: ${operation}
|
||||
Result: ${JSON.stringify(result).slice(0, 500)}
|
||||
Chat History: ${JSON.stringify(this.chatHistory.slice(-3))}
|
||||
|
||||
Be conversational and helpful.`
|
||||
|
||||
return await this.llmClient.complete(prompt)
|
||||
}
|
||||
}
|
||||
|
||||
// Usage:
|
||||
const brain = new BrainyData({
|
||||
augmentations: [
|
||||
new ChatInterfaceAugmentation()
|
||||
],
|
||||
llmClient: openai // Bring your own LLM
|
||||
})
|
||||
|
||||
// Now you can chat!
|
||||
const result = await brain.search({
|
||||
chatQuery: "Show me all documents about project roadmap from last week"
|
||||
})
|
||||
console.log(result.chatResponse) // "I found 5 documents about the project roadmap..."
|
||||
```
|
||||
|
||||
## 2. 🤖 MCP Agent Memory Augmentation
|
||||
**"Provide persistent memory for AI agents through MCP"**
|
||||
|
||||
```typescript
|
||||
import { BaseAugmentation } from './brainyAugmentation.js'
|
||||
import { Server } from '@modelcontextprotocol/sdk'
|
||||
|
||||
export class MCPAgentMemoryAugmentation extends BaseAugmentation {
|
||||
readonly name = 'mcp-agent-memory'
|
||||
readonly timing = 'around' as const // Wrap operations
|
||||
readonly operations = ['all'] as const // Monitor everything
|
||||
readonly priority = 70 // High priority
|
||||
|
||||
private mcpServer: Server
|
||||
private agentSessions: Map<string, any> = new Map()
|
||||
|
||||
protected async onInitialize(): Promise<void> {
|
||||
// Initialize MCP server
|
||||
this.mcpServer = new Server({
|
||||
name: 'brainy-memory',
|
||||
version: '1.0.0'
|
||||
})
|
||||
|
||||
// Register MCP tools for agents
|
||||
this.mcpServer.setRequestHandler('tools/list', () => ({
|
||||
tools: [
|
||||
{
|
||||
name: 'remember',
|
||||
description: 'Store information in long-term memory',
|
||||
inputSchema: {
|
||||
type: 'object',
|
||||
properties: {
|
||||
content: { type: 'string' },
|
||||
category: { type: 'string' },
|
||||
importance: { type: 'number' }
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
name: 'recall',
|
||||
description: 'Retrieve information from memory',
|
||||
inputSchema: {
|
||||
type: 'object',
|
||||
properties: {
|
||||
query: { type: 'string' },
|
||||
category: { type: 'string' },
|
||||
limit: { type: 'number' }
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
name: 'forget',
|
||||
description: 'Remove information from memory',
|
||||
inputSchema: {
|
||||
type: 'object',
|
||||
properties: {
|
||||
query: { type: 'string' },
|
||||
category: { type: 'string' }
|
||||
}
|
||||
}
|
||||
}
|
||||
]
|
||||
}))
|
||||
|
||||
// Handle tool calls from agents
|
||||
this.mcpServer.setRequestHandler('tools/call', async (request) => {
|
||||
const { name, arguments: args } = request.params
|
||||
|
||||
switch (name) {
|
||||
case 'remember':
|
||||
return await this.rememberForAgent(args)
|
||||
case 'recall':
|
||||
return await this.recallForAgent(args)
|
||||
case 'forget':
|
||||
return await this.forgetForAgent(args)
|
||||
default:
|
||||
throw new Error(`Unknown tool: ${name}`)
|
||||
}
|
||||
})
|
||||
|
||||
// Start MCP server
|
||||
await this.mcpServer.connect(process.stdin, process.stdout)
|
||||
this.log('MCP Agent Memory server started')
|
||||
}
|
||||
|
||||
async execute<T>(operation: string, params: any, next: () => Promise<T>): Promise<T> {
|
||||
// Extract agent context if present
|
||||
const agentId = params.metadata?._agentId || 'default'
|
||||
const sessionId = params.metadata?._sessionId
|
||||
|
||||
// Track agent operations
|
||||
if (agentId && sessionId) {
|
||||
if (!this.agentSessions.has(sessionId)) {
|
||||
this.agentSessions.set(sessionId, {
|
||||
agentId,
|
||||
startTime: Date.now(),
|
||||
operations: []
|
||||
})
|
||||
}
|
||||
|
||||
const session = this.agentSessions.get(sessionId)
|
||||
session.operations.push({
|
||||
operation,
|
||||
params: { ...params },
|
||||
timestamp: Date.now()
|
||||
})
|
||||
}
|
||||
|
||||
// Execute with agent context
|
||||
const result = await next()
|
||||
|
||||
// Auto-remember important operations
|
||||
if (operation === 'add' && agentId) {
|
||||
await this.autoRemember(agentId, params, result)
|
||||
}
|
||||
|
||||
return result
|
||||
}
|
||||
|
||||
private async rememberForAgent(args: any) {
|
||||
// Store in Brainy with agent-specific metadata
|
||||
const id = await this.context.brain.add(args.content, {
|
||||
_agentMemory: true,
|
||||
_agentId: args.agentId || 'default',
|
||||
category: args.category,
|
||||
importance: args.importance || 0.5,
|
||||
timestamp: new Date().toISOString()
|
||||
})
|
||||
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: `Remembered with ID: ${id}`
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
private async recallForAgent(args: any) {
|
||||
// Search agent's memories
|
||||
const results = await this.context.brain.search(args.query, args.limit || 10, {
|
||||
where: {
|
||||
_agentMemory: true,
|
||||
_agentId: args.agentId || 'default',
|
||||
category: args.category
|
||||
}
|
||||
})
|
||||
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: JSON.stringify(results, null, 2)
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
private async forgetForAgent(args: any) {
|
||||
// Remove specific memories
|
||||
const results = await this.context.brain.find({
|
||||
where: {
|
||||
_agentMemory: true,
|
||||
_agentId: args.agentId || 'default',
|
||||
category: args.category
|
||||
}
|
||||
})
|
||||
|
||||
for (const item of results) {
|
||||
await this.context.brain.delete(item.id)
|
||||
}
|
||||
|
||||
return {
|
||||
content: [
|
||||
{
|
||||
type: 'text',
|
||||
text: `Forgot ${results.length} memories`
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
private async autoRemember(agentId: string, params: any, result: any) {
|
||||
// Automatically remember important information
|
||||
if (params.metadata?.important) {
|
||||
await this.context.brain.add(params.content, {
|
||||
...params.metadata,
|
||||
_agentMemory: true,
|
||||
_agentId: agentId,
|
||||
_autoRemembered: true,
|
||||
_originalOperation: 'add',
|
||||
_resultId: result
|
||||
})
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Usage:
|
||||
const brain = new BrainyData({
|
||||
augmentations: [
|
||||
new MCPAgentMemoryAugmentation()
|
||||
]
|
||||
})
|
||||
|
||||
// Now AI agents can use Brainy as memory through MCP!
|
||||
// Agents connect via MCP and use remember/recall/forget tools
|
||||
```
|
||||
|
||||
## 3. 🌐 API Server Augmentation
|
||||
**"Expose Brainy through REST, WebSocket, and MCP APIs"**
|
||||
|
||||
```typescript
|
||||
import { BaseAugmentation } from './brainyAugmentation.js'
|
||||
import { BrainyMCPService } from '../mcp/brainyMCPService.js'
|
||||
|
||||
export class APIServerAugmentation extends BaseAugmentation {
|
||||
readonly name = 'api-server'
|
||||
readonly timing = 'after' as const
|
||||
readonly operations = ['all'] as ('all')[]
|
||||
readonly priority = 5 // Low priority, runs after other augmentations
|
||||
|
||||
private httpServer: any
|
||||
private wsServer: any
|
||||
private mcpService: BrainyMCPService
|
||||
|
||||
protected async onInitialize(): Promise<void> {
|
||||
// Initialize MCP service
|
||||
this.mcpService = new BrainyMCPService(this.context.brain)
|
||||
|
||||
// Start HTTP server with REST endpoints
|
||||
await this.startHTTPServer()
|
||||
|
||||
// Start WebSocket server for real-time
|
||||
await this.startWebSocketServer()
|
||||
|
||||
this.log(`API Server running on port ${this.config.port || 3000}`)
|
||||
}
|
||||
|
||||
async execute<T>(operation: string, params: any, next: () => Promise<T>): Promise<T> {
|
||||
const result = await next()
|
||||
|
||||
// Broadcast operation to WebSocket clients
|
||||
this.broadcast({
|
||||
type: 'operation',
|
||||
operation,
|
||||
params: this.sanitizeParams(params),
|
||||
timestamp: Date.now()
|
||||
})
|
||||
|
||||
return result
|
||||
}
|
||||
|
||||
private async startHTTPServer() {
|
||||
// REST endpoints: /api/search, /api/add, /api/get/:id, etc.
|
||||
// MCP endpoint: /api/mcp
|
||||
// Health check: /health
|
||||
}
|
||||
|
||||
private async startWebSocketServer() {
|
||||
// WebSocket for real-time subscriptions
|
||||
// Clients can subscribe to specific operations
|
||||
}
|
||||
}
|
||||
|
||||
// Usage:
|
||||
const brain = new BrainyData()
|
||||
brain.augmentations.register(new APIServerAugmentation({ port: 3000 }))
|
||||
await brain.init()
|
||||
|
||||
// Now access Brainy via:
|
||||
// - REST: http://localhost:3000/api/*
|
||||
// - WebSocket: ws://localhost:3000/ws
|
||||
// - MCP: http://localhost:3000/api/mcp
|
||||
```
|
||||
|
||||
## 4. 📊 Graph Visualization Augmentation
|
||||
**"Real-time graph visualization with clustering"**
|
||||
|
||||
```typescript
|
||||
import { BaseAugmentation } from './brainyAugmentation.js'
|
||||
import { WebSocketServer } from 'ws'
|
||||
|
||||
export class GraphVisualizationAugmentation extends BaseAugmentation {
|
||||
readonly name = 'graph-visualization'
|
||||
readonly timing = 'after' as const
|
||||
readonly operations = ['all'] as const // Monitor all changes
|
||||
readonly priority = 20
|
||||
|
||||
private wsServer: WebSocketServer
|
||||
private graphState: {
|
||||
nodes: Map<string, any>
|
||||
edges: Map<string, any>
|
||||
clusters: Map<string, Set<string>>
|
||||
}
|
||||
private clients: Set<any> = new Set()
|
||||
|
||||
protected async onInitialize(): Promise<void> {
|
||||
// Initialize WebSocket server for real-time updates
|
||||
this.wsServer = new WebSocketServer({
|
||||
port: this.context.config.visualizationPort || 8080
|
||||
})
|
||||
|
||||
this.graphState = {
|
||||
nodes: new Map(),
|
||||
edges: new Map(),
|
||||
clusters: new Map()
|
||||
}
|
||||
|
||||
// Load initial graph state
|
||||
await this.loadGraphState()
|
||||
|
||||
// Handle client connections
|
||||
this.wsServer.on('connection', (ws) => {
|
||||
this.clients.add(ws)
|
||||
|
||||
// Send initial state
|
||||
ws.send(JSON.stringify({
|
||||
type: 'init',
|
||||
data: this.serializeGraphState()
|
||||
}))
|
||||
|
||||
// Handle client messages
|
||||
ws.on('message', async (message) => {
|
||||
const msg = JSON.parse(message.toString())
|
||||
await this.handleClientMessage(msg, ws)
|
||||
})
|
||||
|
||||
ws.on('close', () => {
|
||||
this.clients.delete(ws)
|
||||
})
|
||||
})
|
||||
|
||||
// Start clustering in background
|
||||
this.startClusteringWorker()
|
||||
|
||||
this.log('Graph visualization server started on port ' +
|
||||
(this.context.config.visualizationPort || 8080))
|
||||
}
|
||||
|
||||
async execute<T>(operation: string, params: any, next: () => Promise<T>): Promise<T> {
|
||||
const result = await next()
|
||||
|
||||
// Update graph state based on operation
|
||||
switch (operation) {
|
||||
case 'add':
|
||||
case 'addNoun':
|
||||
await this.handleNodeAdded(result, params)
|
||||
break
|
||||
|
||||
case 'relate':
|
||||
case 'addVerb':
|
||||
await this.handleEdgeAdded(params)
|
||||
break
|
||||
|
||||
case 'delete':
|
||||
await this.handleNodeDeleted(params)
|
||||
break
|
||||
|
||||
case 'search':
|
||||
await this.handleSearchPerformed(params, result)
|
||||
break
|
||||
}
|
||||
|
||||
return result
|
||||
}
|
||||
|
||||
private async handleNodeAdded(id: string, data: any) {
|
||||
// Add node to graph
|
||||
const node = {
|
||||
id,
|
||||
label: data.content?.slice(0, 50) || id,
|
||||
type: data.metadata?.type || 'default',
|
||||
metadata: data.metadata,
|
||||
position: this.calculatePosition(id),
|
||||
clusterId: null
|
||||
}
|
||||
|
||||
this.graphState.nodes.set(id, node)
|
||||
|
||||
// Broadcast to clients
|
||||
this.broadcast({
|
||||
type: 'nodeAdded',
|
||||
data: node
|
||||
})
|
||||
|
||||
// Trigger re-clustering
|
||||
this.scheduleReClustering()
|
||||
}
|
||||
|
||||
private async handleEdgeAdded(params: any) {
|
||||
const edge = {
|
||||
id: `${params.source}-${params.verb}-${params.target}`,
|
||||
source: params.source,
|
||||
target: params.target,
|
||||
label: params.verb,
|
||||
weight: params.weight || 1
|
||||
}
|
||||
|
||||
this.graphState.edges.set(edge.id, edge)
|
||||
|
||||
this.broadcast({
|
||||
type: 'edgeAdded',
|
||||
data: edge
|
||||
})
|
||||
}
|
||||
|
||||
private async handleSearchPerformed(params: any, results: any) {
|
||||
// Highlight search results in visualization
|
||||
const highlightNodes = results.map((r: any) => r.id)
|
||||
|
||||
this.broadcast({
|
||||
type: 'highlight',
|
||||
data: {
|
||||
nodes: highlightNodes,
|
||||
query: params.query,
|
||||
duration: 5000 // Highlight for 5 seconds
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
private async loadGraphState() {
|
||||
// Load all nodes (nouns)
|
||||
const nouns = await this.context.brain.getAllNouns()
|
||||
for (const noun of nouns) {
|
||||
this.graphState.nodes.set(noun.id, {
|
||||
id: noun.id,
|
||||
label: noun.content?.slice(0, 50) || noun.id,
|
||||
type: noun.type,
|
||||
metadata: noun.metadata,
|
||||
position: this.calculatePosition(noun.id)
|
||||
})
|
||||
}
|
||||
|
||||
// Load all edges (verbs/relationships)
|
||||
const verbs = await this.context.brain.getAllVerbs()
|
||||
for (const verb of verbs) {
|
||||
this.graphState.edges.set(verb.id, {
|
||||
id: verb.id,
|
||||
source: verb.source,
|
||||
target: verb.target,
|
||||
label: verb.type,
|
||||
weight: verb.weight
|
||||
})
|
||||
}
|
||||
|
||||
// Initial clustering
|
||||
await this.performClustering()
|
||||
}
|
||||
|
||||
private async performClustering() {
|
||||
// Use Brainy's clustering capabilities
|
||||
const clusteringResult = await this.context.brain.cluster({
|
||||
algorithm: 'hierarchical',
|
||||
threshold: 0.7
|
||||
})
|
||||
|
||||
// Update cluster state
|
||||
this.graphState.clusters.clear()
|
||||
for (const [clusterId, nodeIds] of Object.entries(clusteringResult)) {
|
||||
this.graphState.clusters.set(clusterId, new Set(nodeIds as string[]))
|
||||
|
||||
// Update nodes with cluster IDs
|
||||
for (const nodeId of nodeIds as string[]) {
|
||||
const node = this.graphState.nodes.get(nodeId)
|
||||
if (node) {
|
||||
node.clusterId = clusterId
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Broadcast cluster update
|
||||
this.broadcast({
|
||||
type: 'clustersUpdated',
|
||||
data: this.serializeClusters()
|
||||
})
|
||||
}
|
||||
|
||||
private startClusteringWorker() {
|
||||
// Re-cluster periodically or when graph changes significantly
|
||||
setInterval(async () => {
|
||||
if (this.graphState.nodes.size > 0) {
|
||||
await this.performClustering()
|
||||
}
|
||||
}, 30000) // Every 30 seconds
|
||||
}
|
||||
|
||||
private scheduleReClustering = (() => {
|
||||
let timeout: NodeJS.Timeout
|
||||
return () => {
|
||||
clearTimeout(timeout)
|
||||
timeout = setTimeout(() => this.performClustering(), 5000)
|
||||
}
|
||||
})()
|
||||
|
||||
private calculatePosition(id: string) {
|
||||
// Simple force-directed layout position
|
||||
const hash = id.split('').reduce((a, b) => {
|
||||
a = ((a << 5) - a) + b.charCodeAt(0)
|
||||
return a & a
|
||||
}, 0)
|
||||
|
||||
return {
|
||||
x: (hash % 1000) - 500,
|
||||
y: ((hash * 7) % 1000) - 500
|
||||
}
|
||||
}
|
||||
|
||||
private broadcast(message: any) {
|
||||
const data = JSON.stringify(message)
|
||||
for (const client of this.clients) {
|
||||
client.send(data)
|
||||
}
|
||||
}
|
||||
|
||||
private async handleClientMessage(msg: any, ws: any) {
|
||||
switch (msg.type) {
|
||||
case 'requestClustering':
|
||||
await this.performClustering()
|
||||
break
|
||||
|
||||
case 'search':
|
||||
const results = await this.context.brain.search(msg.query)
|
||||
ws.send(JSON.stringify({
|
||||
type: 'searchResults',
|
||||
data: results
|
||||
}))
|
||||
break
|
||||
|
||||
case 'getNodeDetails':
|
||||
const node = await this.context.brain.get(msg.nodeId)
|
||||
ws.send(JSON.stringify({
|
||||
type: 'nodeDetails',
|
||||
data: node
|
||||
}))
|
||||
break
|
||||
|
||||
case 'expandNode':
|
||||
const connections = await this.context.brain.getConnections(msg.nodeId)
|
||||
ws.send(JSON.stringify({
|
||||
type: 'nodeConnections',
|
||||
data: connections
|
||||
}))
|
||||
break
|
||||
}
|
||||
}
|
||||
|
||||
private serializeGraphState() {
|
||||
return {
|
||||
nodes: Array.from(this.graphState.nodes.values()),
|
||||
edges: Array.from(this.graphState.edges.values()),
|
||||
clusters: this.serializeClusters()
|
||||
}
|
||||
}
|
||||
|
||||
private serializeClusters() {
|
||||
const clusters: any = {}
|
||||
for (const [id, nodeIds] of this.graphState.clusters) {
|
||||
clusters[id] = Array.from(nodeIds)
|
||||
}
|
||||
return clusters
|
||||
}
|
||||
|
||||
protected async onShutdown() {
|
||||
this.wsServer.close()
|
||||
this.clients.clear()
|
||||
}
|
||||
}
|
||||
|
||||
// Usage:
|
||||
const brain = new BrainyData({
|
||||
augmentations: [
|
||||
new GraphVisualizationAugmentation()
|
||||
],
|
||||
visualizationPort: 8080
|
||||
})
|
||||
|
||||
// Now connect a web-based graph viz tool to ws://localhost:8080
|
||||
// It receives real-time updates as data changes!
|
||||
```
|
||||
|
||||
## 4. 🌐 Multi-Agent Team Coordination
|
||||
**"Multiple AI agents sharing knowledge and coordinating tasks"**
|
||||
|
||||
```typescript
|
||||
export class TeamCoordinationAugmentation extends BaseAugmentation {
|
||||
readonly name = 'team-coordination'
|
||||
readonly timing = 'around' as const
|
||||
readonly operations = ['all'] as const
|
||||
readonly priority = 85
|
||||
|
||||
private agents: Map<string, AgentState> = new Map()
|
||||
private tasks: Map<string, Task> = new Map()
|
||||
private sharedMemory: Map<string, any> = new Map()
|
||||
|
||||
async execute<T>(operation: string, params: any, next: () => Promise<T>): Promise<T> {
|
||||
const agentId = params.metadata?._agentId
|
||||
|
||||
if (agentId) {
|
||||
// Track agent activity
|
||||
this.updateAgentState(agentId, operation, params)
|
||||
|
||||
// Check if operation needs coordination
|
||||
if (await this.needsCoordination(operation, params)) {
|
||||
return await this.coordinatedExecute(agentId, operation, params, next)
|
||||
}
|
||||
}
|
||||
|
||||
return next()
|
||||
}
|
||||
|
||||
private async coordinatedExecute<T>(
|
||||
agentId: string,
|
||||
operation: string,
|
||||
params: any,
|
||||
next: () => Promise<T>
|
||||
): Promise<T> {
|
||||
// Acquire distributed lock
|
||||
const lockId = await this.acquireLock(operation, params)
|
||||
|
||||
try {
|
||||
// Check shared memory for related work
|
||||
const relatedWork = await this.findRelatedWork(params)
|
||||
if (relatedWork) {
|
||||
params.metadata._relatedWork = relatedWork
|
||||
}
|
||||
|
||||
// Execute with team context
|
||||
const result = await next()
|
||||
|
||||
// Update shared memory
|
||||
await this.updateSharedMemory(agentId, operation, params, result)
|
||||
|
||||
// Notify other agents
|
||||
await this.notifyTeam(agentId, operation, result)
|
||||
|
||||
return result
|
||||
} finally {
|
||||
await this.releaseLock(lockId)
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## 🎯 Key Patterns
|
||||
|
||||
All these augmentations follow the same pattern:
|
||||
|
||||
1. **Extend BaseAugmentation**
|
||||
2. **Define timing & operations**
|
||||
3. **Initialize resources** in `onInitialize()`
|
||||
4. **Intercept operations** in `execute()`
|
||||
5. **Clean up** in `onShutdown()`
|
||||
|
||||
They can:
|
||||
- **Add APIs** (REST, WebSocket, MCP)
|
||||
- **Transform data** (chat queries → operations)
|
||||
- **Coordinate agents** (distributed locking, shared memory)
|
||||
- **Visualize in real-time** (WebSocket broadcasts)
|
||||
- **Integrate any service** (LLMs, databases, APIs)
|
||||
|
||||
The beauty is they all use the **same simple interface** but achieve vastly different goals!
|
||||
306
docs/AUGMENTATION-PIPELINE-ARCHITECTURE.md
Normal file
306
docs/AUGMENTATION-PIPELINE-ARCHITECTURE.md
Normal file
|
|
@ -0,0 +1,306 @@
|
|||
# 🔄 How Augmentations Hook Into Brainy
|
||||
|
||||
## The Complete Pipeline Architecture
|
||||
|
||||
```
|
||||
User Code → BrainyData Method → Augmentation Pipeline → Storage/Operations
|
||||
↑ ↓
|
||||
└────── Augmentations Execute Here ──┘
|
||||
```
|
||||
|
||||
## 🎯 How Augmentations Register & Execute
|
||||
|
||||
### 1. **Registration During Initialization**
|
||||
|
||||
```typescript
|
||||
// In BrainyData constructor/init
|
||||
class BrainyData {
|
||||
private augmentations = new AugmentationRegistry()
|
||||
|
||||
async init() {
|
||||
// Register built-in augmentations in priority order
|
||||
this.augmentations.register(new WALAugmentation()) // Priority: 100
|
||||
this.augmentations.register(new EntityRegistryAugmentation()) // Priority: 90
|
||||
this.augmentations.register(new NeuralImportAugmentation()) // Priority: 80
|
||||
this.augmentations.register(new BatchProcessingAugmentation()) // Priority: 50
|
||||
|
||||
// Initialize all with context
|
||||
const context: AugmentationContext = {
|
||||
brain: this,
|
||||
storage: this.storage,
|
||||
config: this.config,
|
||||
log: (msg, level) => console.log(msg)
|
||||
}
|
||||
|
||||
await this.augmentations.initialize(context)
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 2. **Execution Through Method Interception**
|
||||
|
||||
Every BrainyData operation wraps its core logic with augmentation execution:
|
||||
|
||||
```typescript
|
||||
// Example: The add() method
|
||||
async add(content: string, metadata?: any): Promise<string> {
|
||||
// Augmentations wrap the core operation
|
||||
return this.augmentations.execute(
|
||||
'add', // Operation name
|
||||
{ content, metadata }, // Parameters
|
||||
async () => { // Core operation
|
||||
// Actual add logic here
|
||||
const id = generateId()
|
||||
await this.storage.set(id, { content, metadata })
|
||||
return id
|
||||
}
|
||||
)
|
||||
}
|
||||
```
|
||||
|
||||
### 3. **The Execution Chain**
|
||||
|
||||
```typescript
|
||||
// In AugmentationRegistry
|
||||
async execute<T>(operation: string, params: any, mainOperation: () => Promise<T>): Promise<T> {
|
||||
// 1. Filter augmentations that should run for this operation
|
||||
const applicable = this.augmentations.filter(aug =>
|
||||
aug.shouldExecute(operation, params)
|
||||
)
|
||||
|
||||
// 2. Sort by priority (already sorted during registration)
|
||||
// Priority 100 runs first, then 90, 80, etc.
|
||||
|
||||
// 3. Create middleware chain
|
||||
let index = 0
|
||||
const executeNext = async (): Promise<T> => {
|
||||
if (index >= applicable.length) {
|
||||
// All augmentations processed, run main operation
|
||||
return mainOperation()
|
||||
}
|
||||
|
||||
const augmentation = applicable[index++]
|
||||
// Each augmentation decides what to do with the operation
|
||||
return augmentation.execute(operation, params, executeNext)
|
||||
}
|
||||
|
||||
return executeNext()
|
||||
}
|
||||
```
|
||||
|
||||
## 🎭 The Four Timing Modes in Action
|
||||
|
||||
### **`timing: 'before'`** - Pre-processing
|
||||
```typescript
|
||||
class NeuralImportAugmentation {
|
||||
timing = 'before'
|
||||
|
||||
async execute(op, params, next) {
|
||||
// Analyze data BEFORE storage
|
||||
const analysis = await this.analyzeWithAI(params.content)
|
||||
params.metadata._neural = analysis
|
||||
|
||||
// Continue with enhanced params
|
||||
return next()
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### **`timing: 'after'`** - Post-processing
|
||||
```typescript
|
||||
class NotionSynapse {
|
||||
timing = 'after'
|
||||
|
||||
async execute(op, params, next) {
|
||||
// Let operation complete first
|
||||
const result = await next()
|
||||
|
||||
// Then sync to Notion
|
||||
await this.syncToNotion(op, params, result)
|
||||
|
||||
return result
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### **`timing: 'around'`** - Wrapping
|
||||
```typescript
|
||||
class WALAugmentation {
|
||||
timing = 'around'
|
||||
|
||||
async execute(op, params, next) {
|
||||
// Write to WAL before
|
||||
await this.wal.write({ op, params, timestamp: Date.now() })
|
||||
|
||||
try {
|
||||
// Execute operation
|
||||
const result = await next()
|
||||
|
||||
// Mark as committed
|
||||
await this.wal.commit()
|
||||
|
||||
return result
|
||||
} catch (error) {
|
||||
// Rollback on failure
|
||||
await this.wal.rollback()
|
||||
throw error
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### **`timing: 'replace'`** - Complete replacement
|
||||
```typescript
|
||||
class S3StorageAugmentation {
|
||||
timing = 'replace'
|
||||
|
||||
async execute(op, params, next) {
|
||||
if (op === 'storage.get') {
|
||||
// Don't call next() - completely replace
|
||||
return await this.s3.getObject(params.key)
|
||||
}
|
||||
// For other operations, pass through
|
||||
return next()
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## 📊 Real Example: How `brain.add()` Works
|
||||
|
||||
```typescript
|
||||
// User calls:
|
||||
await brain.add("John is a developer", { type: "person" })
|
||||
|
||||
// This triggers the chain:
|
||||
|
||||
1. BrainyData.add() calls augmentations.execute('add', params, coreLogic)
|
||||
|
||||
2. AugmentationRegistry filters applicable augmentations:
|
||||
- WALAugmentation (priority: 100, operations: ['all'])
|
||||
- EntityRegistryAugmentation (priority: 90, operations: ['add'])
|
||||
- NeuralImportAugmentation (priority: 80, operations: ['add'])
|
||||
- BatchProcessingAugmentation (priority: 50, operations: ['add'])
|
||||
|
||||
3. Execution chain (highest priority first):
|
||||
|
||||
WALAugmentation.execute() {
|
||||
await wal.write(operation) // Log to WAL
|
||||
const result = await next() // Call next in chain
|
||||
await wal.commit() // Commit WAL
|
||||
return result
|
||||
}
|
||||
↓
|
||||
EntityRegistryAugmentation.execute() {
|
||||
const hash = computeHash(params.content)
|
||||
if (registry.has(hash)) {
|
||||
return registry.get(hash) // Return existing ID
|
||||
}
|
||||
const result = await next() // Continue chain
|
||||
registry.set(hash, result) // Register new entity
|
||||
return result
|
||||
}
|
||||
↓
|
||||
NeuralImportAugmentation.execute() {
|
||||
const analysis = await analyzeWithAI(params)
|
||||
params.metadata._neural = analysis // Add AI insights
|
||||
return next() // Continue with enhanced data
|
||||
}
|
||||
↓
|
||||
BatchProcessingAugmentation.execute() {
|
||||
batch.add(params) // Add to batch
|
||||
if (batch.isFull()) {
|
||||
await batch.flush() // Process batch if full
|
||||
}
|
||||
return next() // Continue
|
||||
}
|
||||
↓
|
||||
Core add() logic {
|
||||
// Finally, the actual storage operation
|
||||
const id = generateId()
|
||||
await storage.set(id, params)
|
||||
await index.add(id, vector)
|
||||
return id
|
||||
}
|
||||
```
|
||||
|
||||
## 🔌 Dynamic Registration
|
||||
|
||||
Augmentations can be registered at any time:
|
||||
|
||||
```typescript
|
||||
// During initialization
|
||||
brain.augmentations.register(new CustomAugmentation())
|
||||
|
||||
// Or later, dynamically
|
||||
const synapse = new NotionSynapse({ apiKey: 'xxx' })
|
||||
brain.augmentations.register(synapse)
|
||||
|
||||
// From Brain Cloud marketplace
|
||||
import { EmotionalIntelligence } from '@brain-cloud/empathy'
|
||||
brain.augmentations.register(new EmotionalIntelligence())
|
||||
```
|
||||
|
||||
## 🎯 Operation Targeting
|
||||
|
||||
Augmentations declare which operations they care about:
|
||||
|
||||
```typescript
|
||||
class SearchOptimizer {
|
||||
operations = ['search', 'searchText', 'findSimilar'] // Only search ops
|
||||
}
|
||||
|
||||
class GlobalLogger {
|
||||
operations = ['all'] // Every operation
|
||||
}
|
||||
|
||||
class StorageReplacer {
|
||||
operations = ['storage'] // Storage operations only
|
||||
}
|
||||
```
|
||||
|
||||
## 🔍 On-Demand Execution
|
||||
|
||||
Some augmentations can be triggered manually:
|
||||
|
||||
```typescript
|
||||
// Get specific augmentation
|
||||
const neuralImport = brain.augmentations.get('neural-import')
|
||||
|
||||
// Use its public API directly
|
||||
const analysis = await neuralImport.getNeuralAnalysis(data, 'json')
|
||||
|
||||
// Or trigger through operations
|
||||
await brain.add(data) // Automatically uses neural import if registered
|
||||
```
|
||||
|
||||
## 📈 Priority System
|
||||
|
||||
```
|
||||
100: Critical Infrastructure (WAL, Transactions)
|
||||
90: Data Integrity (Entity Registry, Deduplication)
|
||||
80: Data Processing (Neural Import, Transformation)
|
||||
50: Performance (Batching, Caching)
|
||||
10: Features (Scoring, Analytics)
|
||||
1: Monitoring (Logging, Metrics)
|
||||
```
|
||||
|
||||
## 🌊 The Flow
|
||||
|
||||
1. **User Action** → `brain.add()`, `brain.search()`, etc.
|
||||
2. **Method Wraps** → Core logic wrapped with `augmentations.execute()`
|
||||
3. **Filter** → Find augmentations for this operation
|
||||
4. **Sort** → Order by priority
|
||||
5. **Chain** → Each augmentation calls next() or not
|
||||
6. **Core** → Eventually hits actual implementation
|
||||
7. **Unwind** → Results flow back through chain
|
||||
8. **Return** → Enhanced result to user
|
||||
|
||||
## 💡 Key Insights
|
||||
|
||||
1. **Everything is interceptable** - All operations go through the pipeline
|
||||
2. **Augmentations compose** - They stack like middleware
|
||||
3. **Priority matters** - Higher priority runs first
|
||||
4. **Timing is flexible** - before/after/around/replace covers all needs
|
||||
5. **Simple but powerful** - One interface, infinite possibilities
|
||||
|
||||
This is why the single `BrainyAugmentation` interface works for EVERYTHING - it's just middleware with superpowers! 🚀
|
||||
288
docs/AUGMENTATION-TYPES-SIMPLE-GUIDE.md
Normal file
288
docs/AUGMENTATION-TYPES-SIMPLE-GUIDE.md
Normal file
|
|
@ -0,0 +1,288 @@
|
|||
# Simple Guide: Creating Augmentations
|
||||
|
||||
## The One Interface That Rules Them All
|
||||
|
||||
**EVERY augmentation is a `BrainyAugmentation`:**
|
||||
|
||||
```typescript
|
||||
interface BrainyAugmentation {
|
||||
name: string // Unique name
|
||||
timing: 'before' | 'after' | 'around' | 'replace' // When to run
|
||||
operations: string[] // What to intercept
|
||||
priority: number // Order (higher = first)
|
||||
|
||||
initialize(context): Promise<void> // Setup
|
||||
execute(op, params, next): Promise<T> // Do work
|
||||
shutdown?(): Promise<void> // Cleanup (optional)
|
||||
}
|
||||
```
|
||||
|
||||
That's it! Every augmentation implements this interface.
|
||||
|
||||
## Creating Different Types of Augmentations
|
||||
|
||||
### 1. Basic Feature Augmentation
|
||||
|
||||
**Use Case:** Add logging, caching, validation, etc.
|
||||
|
||||
```typescript
|
||||
import { BaseAugmentation } from 'brainy'
|
||||
|
||||
export class LoggingAugmentation extends BaseAugmentation {
|
||||
name = 'logging'
|
||||
timing = 'around' // Wrap operations
|
||||
operations = ['add', 'delete'] // What to log
|
||||
priority = 10 // Low priority
|
||||
|
||||
async execute(op, params, next) {
|
||||
console.log(`Starting ${op}`)
|
||||
const result = await next()
|
||||
console.log(`Completed ${op}`)
|
||||
return result
|
||||
}
|
||||
}
|
||||
|
||||
// Usage
|
||||
brain.augmentations.register(new LoggingAugmentation())
|
||||
```
|
||||
|
||||
### 2. Storage Augmentation
|
||||
|
||||
**Use Case:** Provide a storage backend (special: has `provideStorage()` method)
|
||||
|
||||
```typescript
|
||||
import { StorageAugmentation } from 'brainy'
|
||||
|
||||
export class RedisStorageAugmentation extends StorageAugmentation {
|
||||
constructor(config) {
|
||||
super('redis-storage') // Pass name to parent
|
||||
this.config = config
|
||||
}
|
||||
|
||||
// Special method for storage only!
|
||||
async provideStorage() {
|
||||
return new RedisAdapter(this.config)
|
||||
}
|
||||
}
|
||||
|
||||
// Usage (BEFORE init!)
|
||||
brain.augmentations.register(new RedisStorageAugmentation({
|
||||
host: 'localhost',
|
||||
port: 6379
|
||||
}))
|
||||
await brain.init() // Will use Redis!
|
||||
```
|
||||
|
||||
### 3. Data Processing Augmentation
|
||||
|
||||
**Use Case:** Transform or validate data before storage
|
||||
|
||||
```typescript
|
||||
export class ValidationAugmentation extends BaseAugmentation {
|
||||
name = 'validator'
|
||||
timing = 'before' // Run before operation
|
||||
operations = ['add'] // Validate on add
|
||||
priority = 50
|
||||
|
||||
async execute(op, params, next) {
|
||||
// Validate data
|
||||
if (!params.data || !params.data.title) {
|
||||
throw new Error('Title is required')
|
||||
}
|
||||
|
||||
// Add timestamp
|
||||
params.data.createdAt = new Date()
|
||||
|
||||
// Continue with modified params
|
||||
return next()
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 4. External System Augmentation (Synapse)
|
||||
|
||||
**Use Case:** Sync with external systems like Notion, Slack, etc.
|
||||
|
||||
```typescript
|
||||
export class NotionSyncAugmentation extends BaseAugmentation {
|
||||
name = 'notion-sync'
|
||||
timing = 'after' // Sync after local operation
|
||||
operations = ['add', 'update', 'delete']
|
||||
priority = 30
|
||||
|
||||
private notion: NotionClient
|
||||
|
||||
async initialize(context) {
|
||||
await super.initialize(context)
|
||||
this.notion = new NotionClient(this.apiKey)
|
||||
}
|
||||
|
||||
async execute(op, params, next) {
|
||||
// Do local operation first
|
||||
const result = await next()
|
||||
|
||||
// Then sync to Notion
|
||||
if (op === 'add') {
|
||||
await this.notion.createPage({
|
||||
title: params.data.title,
|
||||
content: params.data.content
|
||||
})
|
||||
}
|
||||
|
||||
return result
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 5. Performance Optimization Augmentation
|
||||
|
||||
**Use Case:** Add caching, batching, deduplication
|
||||
|
||||
```typescript
|
||||
export class CacheAugmentation extends BaseAugmentation {
|
||||
name = 'smart-cache'
|
||||
timing = 'around' // Wrap to check cache
|
||||
operations = ['search'] // Cache searches only
|
||||
priority = 60
|
||||
|
||||
private cache = new Map()
|
||||
|
||||
async execute(op, params, next) {
|
||||
const key = JSON.stringify(params)
|
||||
|
||||
// Check cache
|
||||
if (this.cache.has(key)) {
|
||||
this.log('Cache hit!')
|
||||
return this.cache.get(key)
|
||||
}
|
||||
|
||||
// Miss - execute and cache
|
||||
const result = await next()
|
||||
this.cache.set(key, result)
|
||||
|
||||
// Clear old entries if too many
|
||||
if (this.cache.size > 1000) {
|
||||
const firstKey = this.cache.keys().next().value
|
||||
this.cache.delete(firstKey)
|
||||
}
|
||||
|
||||
return result
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Quick Reference: When to Use Each Timing
|
||||
|
||||
| Timing | Use For | Example |
|
||||
|--------|---------|---------|
|
||||
| `before` | Validation, transformation | Check required fields |
|
||||
| `after` | Logging, syncing, analytics | Send to external API |
|
||||
| `around` | Caching, error handling, timing | Wrap with try/catch |
|
||||
| `replace` | Complete replacement | Storage backends |
|
||||
|
||||
## Quick Reference: Common Operations
|
||||
|
||||
| Operation | Description |
|
||||
|-----------|-------------|
|
||||
| `'add'` | Adding data to brain |
|
||||
| `'search'` | Searching/querying |
|
||||
| `'update'` | Updating existing data |
|
||||
| `'delete'` | Removing data |
|
||||
| `'storage'` | Storage resolution (special) |
|
||||
| `'all'` | Intercept everything |
|
||||
|
||||
## The Context Object
|
||||
|
||||
Every augmentation gets this during `initialize()`:
|
||||
|
||||
```typescript
|
||||
{
|
||||
brain: BrainyData, // The brain instance
|
||||
storage: StorageAdapter, // Storage backend
|
||||
config: BrainyDataConfig, // Configuration
|
||||
log: (msg, level) => void // Logger
|
||||
}
|
||||
```
|
||||
|
||||
## Priority Guidelines
|
||||
|
||||
| Priority | Use For |
|
||||
|----------|---------|
|
||||
| 100 | Storage (critical infrastructure) |
|
||||
| 80-99 | System operations (WAL, connections) |
|
||||
| 50-79 | Performance (caching, batching) |
|
||||
| 20-49 | Features (validation, transformation) |
|
||||
| 1-19 | Logging, analytics |
|
||||
|
||||
## Complete Working Example
|
||||
|
||||
Here's a full augmentation that adds word count to all documents:
|
||||
|
||||
```typescript
|
||||
import { BaseAugmentation } from 'brainy'
|
||||
|
||||
export class WordCountAugmentation extends BaseAugmentation {
|
||||
name = 'word-counter'
|
||||
timing = 'before'
|
||||
operations = ['add', 'update']
|
||||
priority = 40
|
||||
|
||||
async execute(operation, params, next) {
|
||||
// Add word count to metadata
|
||||
if (params.data && params.data.content) {
|
||||
const wordCount = params.data.content.split(/\s+/).length
|
||||
params.metadata = params.metadata || {}
|
||||
params.metadata.wordCount = wordCount
|
||||
|
||||
this.log(`Added word count: ${wordCount}`)
|
||||
}
|
||||
|
||||
// Continue with enhanced params
|
||||
return next()
|
||||
}
|
||||
|
||||
async initialize(context) {
|
||||
await super.initialize(context)
|
||||
this.log('Word counter ready!')
|
||||
}
|
||||
}
|
||||
|
||||
// Usage
|
||||
const brain = new BrainyData()
|
||||
brain.augmentations.register(new WordCountAugmentation())
|
||||
await brain.init()
|
||||
|
||||
// Now all adds include word count
|
||||
await brain.add('Hello world', {
|
||||
content: 'This is a test document with nine words here'
|
||||
})
|
||||
// Automatically adds: metadata.wordCount = 9
|
||||
```
|
||||
|
||||
## Key Points to Remember
|
||||
|
||||
1. **All augmentations are `BrainyAugmentation`** - One interface
|
||||
2. **Storage augmentations** add `provideStorage()` method
|
||||
3. **Register before `init()`** for storage, anytime for others
|
||||
4. **Use `BaseAugmentation`** for convenience (has helpers)
|
||||
5. **`next()` is crucial** - Always call it (unless `replace`)
|
||||
6. **Order matters** - Use priority to control execution order
|
||||
|
||||
## Testing Your Augmentation
|
||||
|
||||
```typescript
|
||||
describe('MyAugmentation', () => {
|
||||
it('should enhance data', async () => {
|
||||
const brain = new BrainyData()
|
||||
brain.augmentations.register(new MyAugmentation())
|
||||
await brain.init()
|
||||
|
||||
await brain.add('test', { data: 'test' })
|
||||
const result = await brain.search('test')
|
||||
|
||||
expect(result[0].metadata.enhanced).toBe(true)
|
||||
})
|
||||
})
|
||||
```
|
||||
|
||||
That's it! Augmentations are simple middleware that intercept operations. Pick your timing, operations, and priority, then implement `execute()`!
|
||||
292
docs/COMPLETE-ARCHITECTURE-VISION.md
Normal file
292
docs/COMPLETE-ARCHITECTURE-VISION.md
Normal file
|
|
@ -0,0 +1,292 @@
|
|||
# 🧠 The Complete Brainy Architecture Vision
|
||||
|
||||
## 🎯 The Genius: Everything is an Augmentation
|
||||
|
||||
```
|
||||
🧠 BRAINY CORE
|
||||
│
|
||||
┌────────┴────────┐
|
||||
│ Augmentations │
|
||||
│ Pipeline │
|
||||
└────────┬────────┘
|
||||
│
|
||||
┌────────────────────┼────────────────────┐
|
||||
│ │ │
|
||||
Data Processing External API Exposure
|
||||
Augmentations Connections Augmentations
|
||||
│ │ │
|
||||
NeuralImport Synapses APIServer
|
||||
EntityRegistry (Notion,etc) (REST/WS)
|
||||
BatchProcessing │ MCPServer
|
||||
IntelligentScoring │ GraphQLServer
|
||||
│ │ │
|
||||
└────────────────────┴────────────────────┘
|
||||
│
|
||||
Storage Layer
|
||||
(FS, S3, OPFS, Memory)
|
||||
```
|
||||
|
||||
## 🔄 How It All Works Together
|
||||
|
||||
### 1. **Core Pipeline**
|
||||
Every operation flows through the augmentation pipeline:
|
||||
|
||||
```typescript
|
||||
User Action → BrainyData Method → Augmentation Pipeline → Storage
|
||||
↑
|
||||
All Augmentations Execute Here
|
||||
```
|
||||
|
||||
### 2. **Augmentation Categories (All Using Same Interface!)**
|
||||
|
||||
#### 🧬 **Data Processing** (timing: 'before')
|
||||
- **NeuralImport** - AI understands data before storage
|
||||
- **EntityRegistry** - Deduplicates entities
|
||||
- **BatchProcessing** - Optimizes bulk operations
|
||||
|
||||
#### 🌐 **External Connections** (timing: 'after')
|
||||
- **Synapses** - Sync with Notion, Salesforce, etc.
|
||||
- **WebSocketBroadcast** - Real-time updates to clients
|
||||
- **TeamCoordination** - Multi-agent synchronization
|
||||
|
||||
#### 📡 **API Exposure** (timing: 'after' or separate process)
|
||||
- **APIServerAugmentation** - REST/WebSocket/MCP server
|
||||
- **GraphQLAugmentation** - GraphQL endpoint
|
||||
- **ServiceWorkerAugmentation** - Browser local API
|
||||
|
||||
#### 💾 **Storage Backends** (timing: 'replace')
|
||||
- **S3StorageAugmentation** - Use S3 instead of local
|
||||
- **RedisAugmentation** - Use Redis for caching
|
||||
- **PostgresAugmentation** - Use Postgres for persistence
|
||||
|
||||
#### 🛡️ **Infrastructure** (timing: 'around')
|
||||
- **WALAugmentation** - Write-ahead logging
|
||||
- **TransactionAugmentation** - ACID transactions
|
||||
- **CacheAugmentation** - Multi-level caching
|
||||
|
||||
## 🌟 The Beautiful Simplicity
|
||||
|
||||
### One Interface Rules All
|
||||
|
||||
```typescript
|
||||
interface BrainyAugmentation {
|
||||
name: string
|
||||
timing: 'before' | 'after' | 'around' | 'replace'
|
||||
operations: string[]
|
||||
priority: number
|
||||
initialize(context): Promise<void>
|
||||
execute(operation, params, next): Promise<any>
|
||||
shutdown?(): Promise<void>
|
||||
}
|
||||
```
|
||||
|
||||
This single interface can:
|
||||
- **Process data** with AI
|
||||
- **Connect** to any external service
|
||||
- **Expose** APIs (REST, WebSocket, MCP, GraphQL)
|
||||
- **Replace** storage backends
|
||||
- **Add** infrastructure (WAL, transactions, caching)
|
||||
- **Coordinate** distributed systems
|
||||
- **Visualize** data in real-time
|
||||
- Literally **ANYTHING**
|
||||
|
||||
## 🏗️ Real-World Deployment Architecture
|
||||
|
||||
### Scenario 1: Local Development
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
augmentations: [
|
||||
new NeuralImportAugmentation(), // AI processing
|
||||
new EntityRegistryAugmentation(), // Deduplication
|
||||
new WALAugmentation() // Durability
|
||||
]
|
||||
})
|
||||
```
|
||||
|
||||
### Scenario 2: Production Server
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
augmentations: [
|
||||
// Infrastructure
|
||||
new WALAugmentation(),
|
||||
new ConnectionPoolAugmentation(),
|
||||
new RequestDeduplicatorAugmentation(),
|
||||
|
||||
// Data Processing
|
||||
new NeuralImportAugmentation(),
|
||||
new EntityRegistryAugmentation(),
|
||||
new BatchProcessingAugmentation(),
|
||||
|
||||
// External Connections
|
||||
new NotionSynapse({ apiKey: 'xxx' }),
|
||||
new SlackSynapse({ token: 'xxx' }),
|
||||
|
||||
// API Exposure
|
||||
new APIServerAugmentation({ port: 3000 }),
|
||||
new MCPServerAugmentation({ port: 3001 }),
|
||||
|
||||
// Monitoring
|
||||
new MetricsAugmentation(),
|
||||
new LoggingAugmentation()
|
||||
]
|
||||
})
|
||||
```
|
||||
|
||||
### Scenario 3: Distributed AI Agent System
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
augmentations: [
|
||||
// Agent Coordination
|
||||
new TeamCoordinationAugmentation(),
|
||||
new DistributedLockAugmentation(),
|
||||
new SharedMemoryAugmentation(),
|
||||
|
||||
// Agent Memory
|
||||
new MCPAgentMemoryAugmentation(),
|
||||
new ConversationHistoryAugmentation(),
|
||||
|
||||
// Real-time Communication
|
||||
new WebSocketBroadcastAugmentation(),
|
||||
new PubSubAugmentation(),
|
||||
|
||||
// Visualization
|
||||
new GraphVisualizationAugmentation()
|
||||
]
|
||||
})
|
||||
```
|
||||
|
||||
## 🔌 How API Exposure Works
|
||||
|
||||
The **APIServerAugmentation** is special - it can run in two modes:
|
||||
|
||||
### Mode 1: Embedded (Same Process)
|
||||
```typescript
|
||||
brain.augmentations.register(new APIServerAugmentation())
|
||||
// API server runs in same process, hooks into pipeline
|
||||
```
|
||||
|
||||
### Mode 2: Standalone (Separate Process)
|
||||
```typescript
|
||||
// server.js - separate file
|
||||
import { BrainyData } from 'brainy'
|
||||
import { APIServerAugmentation } from 'brainy/augmentations'
|
||||
|
||||
const brain = new BrainyData()
|
||||
const apiServer = new APIServerAugmentation()
|
||||
|
||||
// Can also run as standalone server connecting to remote Brainy
|
||||
apiServer.connectToRemoteBrainy('ws://brainy-host:8080')
|
||||
apiServer.listen(3000)
|
||||
```
|
||||
|
||||
## 🎭 The Four Timing Modes in Practice
|
||||
|
||||
### System Startup Sequence
|
||||
```
|
||||
1. INITIALIZE Phase
|
||||
└─> All augmentations initialize (storage, connections, servers)
|
||||
|
||||
2. OPERATION Phase (for each operation)
|
||||
├─> 'before' augmentations (NeuralImport, Validation)
|
||||
├─> 'around' augmentations start (WAL, Transactions)
|
||||
├─> 'replace' augmentations (if any, skip core)
|
||||
├─> Core operation (or replaced operation)
|
||||
├─> 'around' augmentations complete (Commit/Rollback)
|
||||
└─> 'after' augmentations (Sync, Broadcast, Log)
|
||||
|
||||
3. SHUTDOWN Phase
|
||||
└─> All augmentations cleanup (close connections, flush buffers)
|
||||
```
|
||||
|
||||
## 🌍 Deployment Patterns
|
||||
|
||||
### Pattern 1: Monolithic
|
||||
Everything in one process:
|
||||
```
|
||||
┌─────────────────────────────┐
|
||||
│ Single Node.js Process │
|
||||
│ ┌───────────────────────┐ │
|
||||
│ │ BrainyData Core │ │
|
||||
│ ├───────────────────────┤ │
|
||||
│ │ All Augmentations │ │
|
||||
│ ├───────────────────────┤ │
|
||||
│ │ API Server │ │
|
||||
│ └───────────────────────┘ │
|
||||
└─────────────────────────────┘
|
||||
```
|
||||
|
||||
### Pattern 2: Microservices
|
||||
Distributed across services:
|
||||
```
|
||||
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
|
||||
│ Brainy Core │────▶│ API Gateway │────▶│ Clients │
|
||||
└──────┬───────┘ └──────────────┘ └──────────────┘
|
||||
│
|
||||
├──────────────┐
|
||||
▼ ▼
|
||||
┌──────────────┐ ┌──────────────┐
|
||||
│ Synapses │ │ AI Agents │
|
||||
│ Service │ │ Service │
|
||||
└──────────────┘ └──────────────┘
|
||||
```
|
||||
|
||||
### Pattern 3: Edge Computing
|
||||
Brainy at the edge:
|
||||
```
|
||||
┌─────────────────────────────────────┐
|
||||
│ CloudFlare Worker │
|
||||
│ ┌───────────────────────────────┐ │
|
||||
│ │ Brainy (Memory Storage) │ │
|
||||
│ │ + API Server Augmentation │ │
|
||||
│ └───────────────────────────────┘ │
|
||||
└─────────────────────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌───────────────┐
|
||||
│ S3 Storage │
|
||||
└───────────────┘
|
||||
```
|
||||
|
||||
## 🚀 The Power of Composition
|
||||
|
||||
Any combination works because everything uses the same interface:
|
||||
|
||||
```typescript
|
||||
// Local AI Assistant
|
||||
[NeuralImport, ChatInterface, LocalStorage]
|
||||
|
||||
// Production API
|
||||
[WAL, S3Storage, APIServer, RateLimiting]
|
||||
|
||||
// Multi-Agent System
|
||||
[TeamCoordination, MCPServer, GraphVisualization]
|
||||
|
||||
// Data Pipeline
|
||||
[KafkaConsumer, NeuralImport, PostgresStorage]
|
||||
|
||||
// Real-time Analytics
|
||||
[StreamProcessing, Clustering, WebSocketBroadcast]
|
||||
```
|
||||
|
||||
## 🎯 Key Insights
|
||||
|
||||
1. **No Special Cases** - Everything is an augmentation
|
||||
2. **Complete Flexibility** - Mix and match any combination
|
||||
3. **Environment Agnostic** - Works in browser, Node, Deno, edge
|
||||
4. **Protocol Agnostic** - REST, WebSocket, MCP, GraphQL, gRPC
|
||||
5. **Storage Agnostic** - Local, S3, Redis, Postgres, anything
|
||||
6. **Infinitely Extensible** - Just add more augmentations
|
||||
|
||||
## 🧠 The Philosophy
|
||||
|
||||
> "Make everything an augmentation, and the system becomes infinitely flexible while remaining dead simple."
|
||||
|
||||
This is why Brainy can be:
|
||||
- A local embedded database
|
||||
- A distributed knowledge graph
|
||||
- An AI agent memory system
|
||||
- A real-time collaboration platform
|
||||
- A data pipeline processor
|
||||
- All of the above simultaneously
|
||||
|
||||
**One interface. Infinite possibilities. That's the Brainy way.** 🚀
|
||||
303
docs/CREATING-AUGMENTATIONS.md
Normal file
303
docs/CREATING-AUGMENTATIONS.md
Normal file
|
|
@ -0,0 +1,303 @@
|
|||
# Creating Augmentations for Brainy
|
||||
|
||||
## The BrainyAugmentation Interface
|
||||
|
||||
Every augmentation implements this simple yet powerful interface:
|
||||
|
||||
```typescript
|
||||
interface BrainyAugmentation {
|
||||
// Identification
|
||||
name: string // Unique name for your augmentation
|
||||
|
||||
// Execution control
|
||||
timing: 'before' | 'after' | 'around' | 'replace' // When to execute
|
||||
operations: string[] // Which operations to intercept
|
||||
priority: number // Execution order (higher = first)
|
||||
|
||||
// Lifecycle methods
|
||||
initialize(context: AugmentationContext): Promise<void>
|
||||
execute<T>(operation: string, params: any, next: () => Promise<T>): Promise<T>
|
||||
shutdown?(): Promise<void> // Optional cleanup
|
||||
}
|
||||
```
|
||||
|
||||
## Creating a Storage Augmentation
|
||||
|
||||
Storage augmentations are special - they provide the storage backend for Brainy:
|
||||
|
||||
```typescript
|
||||
import { StorageAugmentation } from 'brainy/augmentations'
|
||||
import { MyCustomStorage } from './my-storage'
|
||||
|
||||
export class MyStorageAugmentation extends StorageAugmentation {
|
||||
private config: MyStorageConfig
|
||||
|
||||
constructor(config: MyStorageConfig) {
|
||||
super()
|
||||
this.name = 'my-custom-storage'
|
||||
this.config = config
|
||||
}
|
||||
|
||||
// Called during storage resolution phase
|
||||
async provideStorage(): Promise<StorageAdapter> {
|
||||
const storage = new MyCustomStorage(this.config)
|
||||
this.storageAdapter = storage
|
||||
return storage
|
||||
}
|
||||
|
||||
// Called during augmentation initialization
|
||||
protected async onInitialize(): Promise<void> {
|
||||
await this.storageAdapter!.init()
|
||||
this.log(`Custom storage initialized`)
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Using Your Storage Augmentation
|
||||
|
||||
```typescript
|
||||
// Register before brain.init()
|
||||
const brain = new BrainyData()
|
||||
brain.augmentations.register(new MyStorageAugmentation({
|
||||
connectionString: 'redis://localhost:6379'
|
||||
}))
|
||||
await brain.init() // Will use your storage!
|
||||
```
|
||||
|
||||
## Creating a Feature Augmentation
|
||||
|
||||
Here's a complete example of a caching augmentation:
|
||||
|
||||
```typescript
|
||||
import { BaseAugmentation, BrainyAugmentation } from 'brainy/augmentations'
|
||||
|
||||
export class CachingAugmentation extends BaseAugmentation {
|
||||
private cache = new Map<string, any>()
|
||||
|
||||
constructor() {
|
||||
super()
|
||||
this.name = 'smart-cache'
|
||||
this.timing = 'around' // Wrap operations
|
||||
this.operations = ['search'] // Only cache searches
|
||||
this.priority = 50 // Mid-priority
|
||||
}
|
||||
|
||||
async execute<T>(operation: string, params: any, next: () => Promise<T>): Promise<T> {
|
||||
if (operation === 'search') {
|
||||
// Check cache
|
||||
const cacheKey = JSON.stringify(params)
|
||||
if (this.cache.has(cacheKey)) {
|
||||
this.log('Cache hit!')
|
||||
return this.cache.get(cacheKey)
|
||||
}
|
||||
|
||||
// Execute and cache
|
||||
const result = await next()
|
||||
this.cache.set(cacheKey, result)
|
||||
return result
|
||||
}
|
||||
|
||||
// Pass through other operations
|
||||
return next()
|
||||
}
|
||||
|
||||
protected async onInitialize(): Promise<void> {
|
||||
this.log('Cache initialized')
|
||||
}
|
||||
|
||||
async shutdown(): Promise<void> {
|
||||
this.cache.clear()
|
||||
await super.shutdown()
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## The Four Timing Modes
|
||||
|
||||
### 1. `before` - Pre-processing
|
||||
```typescript
|
||||
timing = 'before'
|
||||
async execute(op, params, next) {
|
||||
// Validate/transform input
|
||||
const validated = await validate(params)
|
||||
return next(validated) // Pass modified params
|
||||
}
|
||||
```
|
||||
|
||||
### 2. `after` - Post-processing
|
||||
```typescript
|
||||
timing = 'after'
|
||||
async execute(op, params, next) {
|
||||
const result = await next()
|
||||
// Log, analyze, or modify result
|
||||
console.log(`Operation ${op} returned:`, result)
|
||||
return result
|
||||
}
|
||||
```
|
||||
|
||||
### 3. `around` - Wrapping (middleware)
|
||||
```typescript
|
||||
timing = 'around'
|
||||
async execute(op, params, next) {
|
||||
console.log('Starting', op)
|
||||
try {
|
||||
const result = await next()
|
||||
console.log('Success', op)
|
||||
return result
|
||||
} catch (error) {
|
||||
console.log('Failed', op, error)
|
||||
throw error
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 4. `replace` - Complete replacement
|
||||
```typescript
|
||||
timing = 'replace'
|
||||
async execute(op, params, next) {
|
||||
// Don't call next() - replace entirely!
|
||||
return myCustomImplementation(params)
|
||||
}
|
||||
```
|
||||
|
||||
## Operations You Can Intercept
|
||||
|
||||
Common operations in Brainy:
|
||||
- `'storage'` - Storage resolution (special)
|
||||
- `'add'`, `'addNoun'` - Adding data
|
||||
- `'search'`, `'similar'` - Searching
|
||||
- `'update'`, `'delete'` - Modifications
|
||||
- `'saveNoun'`, `'saveVerb'` - Storage operations
|
||||
- `'all'` - Intercept everything
|
||||
|
||||
## Context Available to Augmentations
|
||||
|
||||
```typescript
|
||||
interface AugmentationContext {
|
||||
brain: BrainyData // The brain instance
|
||||
storage: StorageAdapter // Storage backend
|
||||
config: BrainyDataConfig // Configuration
|
||||
log: (message: string, level?: 'info' | 'warn' | 'error') => void
|
||||
}
|
||||
```
|
||||
|
||||
## Real-World Examples
|
||||
|
||||
### 1. Redis Storage Augmentation
|
||||
```typescript
|
||||
export class RedisStorageAugmentation extends StorageAugmentation {
|
||||
async provideStorage(): Promise<StorageAdapter> {
|
||||
return new RedisAdapter({
|
||||
host: 'localhost',
|
||||
port: 6379,
|
||||
// Implement full StorageAdapter interface
|
||||
})
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 2. Audit Trail Augmentation
|
||||
```typescript
|
||||
export class AuditAugmentation extends BaseAugmentation {
|
||||
timing = 'after'
|
||||
operations = ['add', 'update', 'delete']
|
||||
|
||||
async execute(op, params, next) {
|
||||
const result = await next()
|
||||
|
||||
// Log to audit trail
|
||||
await this.logAudit({
|
||||
operation: op,
|
||||
params,
|
||||
result,
|
||||
timestamp: new Date(),
|
||||
user: this.context.config.currentUser
|
||||
})
|
||||
|
||||
return result
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 3. Rate Limiting Augmentation
|
||||
```typescript
|
||||
export class RateLimitAugmentation extends BaseAugmentation {
|
||||
timing = 'before'
|
||||
operations = ['search']
|
||||
private limiter = new RateLimiter({ rps: 100 })
|
||||
|
||||
async execute(op, params, next) {
|
||||
await this.limiter.acquire() // Wait if rate limited
|
||||
return next()
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Publishing to Brain Cloud Marketplace
|
||||
|
||||
Future capability for premium augmentations:
|
||||
|
||||
```typescript
|
||||
// package.json
|
||||
{
|
||||
"name": "@brain-cloud/redis-storage",
|
||||
"brainy": {
|
||||
"type": "augmentation",
|
||||
"category": "storage",
|
||||
"premium": true
|
||||
}
|
||||
}
|
||||
|
||||
// Users can install via:
|
||||
// brainy augment install redis-storage
|
||||
```
|
||||
|
||||
## Best Practices
|
||||
|
||||
1. **Use BaseAugmentation** - Provides common functionality
|
||||
2. **Set appropriate priority** - Storage (100), System (80-99), Features (10-50)
|
||||
3. **Be selective with operations** - Don't use 'all' unless necessary
|
||||
4. **Handle errors gracefully** - Don't break the chain
|
||||
5. **Clean up in shutdown()** - Release resources
|
||||
6. **Log appropriately** - Use context.log() for consistent output
|
||||
7. **Document your augmentation** - Include examples
|
||||
|
||||
## Testing Your Augmentation
|
||||
|
||||
```typescript
|
||||
import { BrainyData } from 'brainy'
|
||||
import { MyAugmentation } from './my-augmentation'
|
||||
|
||||
describe('MyAugmentation', () => {
|
||||
let brain: BrainyData
|
||||
|
||||
beforeEach(async () => {
|
||||
brain = new BrainyData()
|
||||
brain.augmentations.register(new MyAugmentation())
|
||||
await brain.init()
|
||||
})
|
||||
|
||||
afterEach(async () => {
|
||||
await brain.destroy()
|
||||
})
|
||||
|
||||
it('should enhance searches', async () => {
|
||||
// Test your augmentation's effect
|
||||
const results = await brain.search('test')
|
||||
expect(results).toHaveProperty('enhanced', true)
|
||||
})
|
||||
})
|
||||
```
|
||||
|
||||
## Summary
|
||||
|
||||
Augmentations are Brainy's extension system. They can:
|
||||
- Replace storage backends
|
||||
- Add caching layers
|
||||
- Implement audit trails
|
||||
- Add rate limiting
|
||||
- Sync with external systems
|
||||
- Transform data
|
||||
- And much more!
|
||||
|
||||
The unified BrainyAugmentation interface makes it easy to create powerful extensions while maintaining consistency across the entire system.
|
||||
83
docs/MODEL_LOADING_QUICK_REFERENCE.md
Normal file
83
docs/MODEL_LOADING_QUICK_REFERENCE.md
Normal file
|
|
@ -0,0 +1,83 @@
|
|||
# 🤖 Model Loading Quick Reference
|
||||
|
||||
## 🚀 Common Scenarios
|
||||
|
||||
### ✅ Development (Zero Config)
|
||||
```typescript
|
||||
const brain = new BrainyData()
|
||||
await brain.init() // Downloads automatically
|
||||
```
|
||||
|
||||
### 🐳 Docker Production
|
||||
```dockerfile
|
||||
RUN npm run download-models
|
||||
ENV BRAINY_ALLOW_REMOTE_MODELS=false
|
||||
```
|
||||
|
||||
### ☁️ Serverless/Lambda
|
||||
```bash
|
||||
# Build step
|
||||
npm run download-models
|
||||
|
||||
# Runtime
|
||||
export BRAINY_ALLOW_REMOTE_MODELS=false
|
||||
```
|
||||
|
||||
### 🔒 Air-Gapped/Offline
|
||||
```bash
|
||||
# Connected machine
|
||||
npm run download-models
|
||||
tar -czf brainy-models.tar.gz ./models
|
||||
|
||||
# Offline machine
|
||||
tar -xzf brainy-models.tar.gz
|
||||
export BRAINY_ALLOW_REMOTE_MODELS=false
|
||||
```
|
||||
|
||||
### 🌐 Browser/CDN
|
||||
```html
|
||||
<!-- Automatic - no setup needed -->
|
||||
<script type="module">
|
||||
import { BrainyData } from 'brainy'
|
||||
const brain = new BrainyData()
|
||||
await brain.init() // Works in browser
|
||||
</script>
|
||||
```
|
||||
|
||||
## 🚨 Troubleshooting
|
||||
|
||||
| Error | Solution |
|
||||
|-------|----------|
|
||||
| "Failed to load embedding model" | `npm run download-models` |
|
||||
| "ENOENT: no such file" | Check `BRAINY_MODELS_PATH` |
|
||||
| "Network timeout" | Set `BRAINY_ALLOW_REMOTE_MODELS=false` |
|
||||
| "Permission denied" | `chmod 755 ./models` |
|
||||
| "Out of memory" | Increase container memory limit |
|
||||
|
||||
## 🎯 Environment Variables
|
||||
|
||||
| Variable | Values | Purpose |
|
||||
|----------|--------|---------|
|
||||
| `BRAINY_ALLOW_REMOTE_MODELS` | `true`/`false` | Allow/block downloads |
|
||||
| `BRAINY_MODELS_PATH` | `./models` | Model storage path |
|
||||
| `NODE_ENV` | `production` | Environment detection |
|
||||
|
||||
## 📦 Model Info
|
||||
|
||||
- **Model**: All-MiniLM-L6-v2
|
||||
- **Dimensions**: 384 (fixed)
|
||||
- **Size**: ~80MB download, ~330MB uncompressed
|
||||
- **Location**: `./models/Xenova/all-MiniLM-L6-v2/`
|
||||
|
||||
## ✅ Verification Commands
|
||||
|
||||
```bash
|
||||
# Check models exist
|
||||
ls ./models/Xenova/all-MiniLM-L6-v2/onnx/model.onnx
|
||||
|
||||
# Test offline mode
|
||||
BRAINY_ALLOW_REMOTE_MODELS=false npm test
|
||||
|
||||
# Download fresh models
|
||||
rm -rf ./models && npm run download-models
|
||||
```
|
||||
121
docs/README.md
Normal file
121
docs/README.md
Normal file
|
|
@ -0,0 +1,121 @@
|
|||
# Brainy Documentation
|
||||
|
||||
Welcome to the comprehensive documentation for Brainy, the multi-dimensional AI database with Triple Intelligence Engine.
|
||||
|
||||
## 📊 Implementation Status
|
||||
|
||||
- ✅ **Production Ready**: Core features working today
|
||||
- 🚧 **In Development**: Features coming soon
|
||||
- 📅 **Roadmap**: See [ROADMAP.md](../ROADMAP.md)
|
||||
|
||||
## Quick Links
|
||||
|
||||
### Getting Started
|
||||
- [Quick Start Guide](./guides/getting-started.md) - Get up and running in minutes
|
||||
- [Enterprise for Everyone](./guides/enterprise-for-everyone.md) - **No limits, no tiers, everything free**
|
||||
- [Natural Language Queries](./guides/natural-language.md) - Query with plain English
|
||||
|
||||
### Core Concepts
|
||||
- [Zero Configuration](./architecture/zero-config.md) - **Auto-adapts to any environment**
|
||||
- [Noun-Verb Taxonomy](./architecture/noun-verb-taxonomy.md) - **Revolutionary data model**
|
||||
- [Triple Intelligence](./architecture/triple-intelligence.md) - Unified query system
|
||||
- [Architecture Overview](./architecture/overview.md) - System design
|
||||
|
||||
### API Documentation
|
||||
- [API Reference](./api/README.md) - Complete API documentation
|
||||
- [TypeScript Types](./api/types.md) - Type definitions
|
||||
|
||||
### Advanced Topics
|
||||
- [Augmentations System](./architecture/augmentations.md) - **Enterprise plugins & neural import**
|
||||
- [Storage Architecture](./architecture/storage.md) - Storage adapter system
|
||||
- [Performance Tuning](./guides/performance.md) - Optimization guide
|
||||
- [Migration Guide](../MIGRATION.md) - Upgrading from 1.x
|
||||
|
||||
## What is Brainy?
|
||||
|
||||
Brainy is a next-generation AI database that combines:
|
||||
- **Vector Search**: Semantic similarity using HNSW indexing
|
||||
- **Graph Relationships**: Complex relationship mapping and traversal
|
||||
- **Field Filtering**: Precise metadata filtering with O(1) lookups
|
||||
- **Natural Language**: Query in plain English
|
||||
|
||||
## Key Features
|
||||
|
||||
### 🧠 Triple Intelligence Engine
|
||||
All three intelligence types (vector, graph, field) work together in every query for optimal results.
|
||||
|
||||
### 📝 Noun-Verb Taxonomy
|
||||
Model your data naturally as entities (nouns) and relationships (verbs) - no complex schemas needed.
|
||||
|
||||
### 🌍 Natural Language Queries
|
||||
Ask questions in plain English and Brainy understands your intent:
|
||||
```typescript
|
||||
await brain.find("recent articles about AI with high ratings")
|
||||
```
|
||||
|
||||
### ⚡ Production Ready
|
||||
- Universal storage (FileSystem, S3, OPFS, Memory)
|
||||
- Zero configuration with intelligent defaults
|
||||
- Full TypeScript support
|
||||
- Cross-platform compatibility
|
||||
|
||||
## Quick Example
|
||||
|
||||
```typescript
|
||||
import { BrainyData } from 'brainy'
|
||||
|
||||
// Initialize
|
||||
const brain = new BrainyData()
|
||||
await brain.init()
|
||||
|
||||
// Add entities (nouns)
|
||||
const articleId = await brain.addNoun("Revolutionary AI Breakthrough", {
|
||||
type: "article",
|
||||
category: "technology",
|
||||
rating: 4.8
|
||||
})
|
||||
|
||||
const authorId = await brain.addNoun("Dr. Sarah Chen", {
|
||||
type: "person",
|
||||
role: "researcher"
|
||||
})
|
||||
|
||||
// Create relationships (verbs)
|
||||
await brain.addVerb(authorId, articleId, "authored", {
|
||||
date: "2024-01-15",
|
||||
contribution: "primary"
|
||||
})
|
||||
|
||||
// Query naturally
|
||||
const results = await brain.find("highly rated technology articles by researchers")
|
||||
```
|
||||
|
||||
## Documentation Structure
|
||||
|
||||
```
|
||||
docs/
|
||||
├── README.md # This file
|
||||
├── guides/ # User guides
|
||||
│ ├── getting-started.md # Quick start guide
|
||||
│ ├── natural-language.md # NLP query guide
|
||||
│ └── performance.md # Performance tuning
|
||||
├── architecture/ # Technical architecture
|
||||
│ ├── overview.md # System overview
|
||||
│ ├── noun-verb-taxonomy.md # Data model
|
||||
│ ├── triple-intelligence.md # Query system
|
||||
│ └── storage.md # Storage layer
|
||||
└── api/ # API documentation
|
||||
├── README.md # API overview
|
||||
├── brainy-data.md # Main class
|
||||
└── types.md # TypeScript types
|
||||
```
|
||||
|
||||
## Community
|
||||
|
||||
- **GitHub**: [github.com/brainy-org/brainy](https://github.com/brainy-org/brainy)
|
||||
- **Issues**: [Report bugs or request features](https://github.com/brainy-org/brainy/issues)
|
||||
- **Discussions**: [Join the conversation](https://github.com/brainy-org/brainy/discussions)
|
||||
|
||||
## License
|
||||
|
||||
Brainy is MIT licensed. See [LICENSE](../LICENSE) for details.
|
||||
276
docs/STORAGE-AUGMENTATIONS-GUIDE.md
Normal file
276
docs/STORAGE-AUGMENTATIONS-GUIDE.md
Normal file
|
|
@ -0,0 +1,276 @@
|
|||
# Storage Augmentations Guide
|
||||
|
||||
## Overview
|
||||
|
||||
Brainy uses a unified augmentation system for storage backends. This guide explains the difference between built-in storage augmentations and how to create custom ones.
|
||||
|
||||
## Built-in Storage Augmentations
|
||||
|
||||
These wrap existing, battle-tested storage adapters from `/storage/adapters/`:
|
||||
|
||||
| Augmentation | Underlying Adapter | Environment | Description |
|
||||
|--------------|-------------------|-------------|-------------|
|
||||
| `MemoryStorageAugmentation` | `MemoryStorage` | Universal | Fast in-memory storage (not persistent) |
|
||||
| `FileSystemStorageAugmentation` | `FileSystemStorage` | Node.js | Persistent file-based storage |
|
||||
| `OPFSStorageAugmentation` | `OPFSStorage` | Browser | Browser persistent storage |
|
||||
| `S3StorageAugmentation` | `S3CompatibleStorage` | Universal | Amazon S3 with throttling & caching |
|
||||
| `R2StorageAugmentation` | `R2Storage` | Universal | Cloudflare R2 storage |
|
||||
| `GCSStorageAugmentation` | `S3CompatibleStorage` | Universal | Google Cloud Storage |
|
||||
|
||||
### Architecture of Built-in Storage
|
||||
|
||||
```
|
||||
BrainyData
|
||||
↓
|
||||
StorageAugmentation (thin wrapper)
|
||||
↓
|
||||
StorageAdapter (actual implementation in /storage/adapters/)
|
||||
↓
|
||||
Actual Storage (filesystem, S3, memory, etc.)
|
||||
```
|
||||
|
||||
### Why This Design?
|
||||
|
||||
1. **Preserve existing code** - Storage adapters have years of bug fixes
|
||||
2. **Complex features intact** - S3 throttling, caching, retry logic preserved
|
||||
3. **Minimal wrapper** - Augmentations are just 20-30 lines
|
||||
4. **Zero feature loss** - All 30+ StorageAdapter methods work unchanged
|
||||
|
||||
## Using Built-in Storage
|
||||
|
||||
### 1. Zero-Config (Auto-Selection)
|
||||
```typescript
|
||||
const brain = new BrainyData()
|
||||
await brain.init()
|
||||
// Automatically selects:
|
||||
// - Node.js → FileSystemStorage
|
||||
// - Browser → OPFSStorage (or Memory fallback)
|
||||
```
|
||||
|
||||
### 2. Configuration-Based
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
storage: {
|
||||
s3Storage: {
|
||||
bucketName: 'my-bucket',
|
||||
accessKeyId: 'xxx',
|
||||
secretAccessKey: 'yyy'
|
||||
}
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
### 3. Augmentation Override
|
||||
```typescript
|
||||
const brain = new BrainyData()
|
||||
brain.augmentations.register(new S3StorageAugmentation({
|
||||
bucketName: 'my-bucket',
|
||||
region: 'us-east-1',
|
||||
accessKeyId: 'xxx',
|
||||
secretAccessKey: 'yyy'
|
||||
}))
|
||||
await brain.init()
|
||||
```
|
||||
|
||||
## Creating Custom Storage Augmentations
|
||||
|
||||
Custom storage augmentations can either:
|
||||
1. Wrap an existing adapter (like built-ins do)
|
||||
2. Implement the StorageAdapter interface directly
|
||||
|
||||
### Option 1: Wrapping an Existing Adapter
|
||||
|
||||
```typescript
|
||||
import { StorageAugmentation } from 'brainy'
|
||||
import { CustomAdapter } from './my-custom-adapter'
|
||||
|
||||
export class CustomStorageAugmentation extends StorageAugmentation {
|
||||
private config: CustomConfig
|
||||
|
||||
constructor(config: CustomConfig) {
|
||||
super('custom-storage')
|
||||
this.config = config
|
||||
}
|
||||
|
||||
async provideStorage(): Promise<StorageAdapter> {
|
||||
// Create and return your adapter
|
||||
const adapter = new CustomAdapter(this.config)
|
||||
return adapter
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Option 2: Self-Contained Implementation
|
||||
|
||||
```typescript
|
||||
import { StorageAugmentation, StorageAdapter } from 'brainy'
|
||||
import Redis from 'ioredis'
|
||||
|
||||
export class RedisStorageAugmentation extends StorageAugmentation {
|
||||
private redis: Redis
|
||||
|
||||
constructor(config: RedisConfig) {
|
||||
super('redis-storage')
|
||||
this.redis = new Redis(config)
|
||||
}
|
||||
|
||||
async provideStorage(): Promise<StorageAdapter> {
|
||||
// Return an object implementing StorageAdapter
|
||||
return {
|
||||
async init() {
|
||||
await this.redis.ping()
|
||||
},
|
||||
|
||||
async saveNoun(noun) {
|
||||
await this.redis.set(
|
||||
`noun:${noun.id}`,
|
||||
JSON.stringify(noun)
|
||||
)
|
||||
},
|
||||
|
||||
async getNoun(id) {
|
||||
const data = await this.redis.get(`noun:${id}`)
|
||||
return data ? JSON.parse(data) : null
|
||||
},
|
||||
|
||||
async deleteNoun(id) {
|
||||
await this.redis.del(`noun:${id}`)
|
||||
},
|
||||
|
||||
// ... implement all 30+ required methods
|
||||
// See StorageAdapter interface in coreTypes.ts
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## StorageAdapter Interface Requirements
|
||||
|
||||
Your custom storage must implement these core methods:
|
||||
|
||||
```typescript
|
||||
interface StorageAdapter {
|
||||
// Initialization
|
||||
init(): Promise<void>
|
||||
|
||||
// Noun operations
|
||||
saveNoun(noun: HNSWNoun): Promise<void>
|
||||
getNoun(id: string): Promise<HNSWNoun | null>
|
||||
deleteNoun(id: string): Promise<void>
|
||||
getNounsByNounType(type: string): Promise<HNSWNoun[]>
|
||||
|
||||
// Verb operations
|
||||
saveVerb(verb: HNSWVerb): Promise<void>
|
||||
getVerb(id: string): Promise<HNSWVerb | null>
|
||||
deleteVerb(id: string): Promise<void>
|
||||
getVerbsBySource(sourceId: string): Promise<HNSWVerb[]>
|
||||
getVerbsByTarget(targetId: string): Promise<HNSWVerb[]>
|
||||
|
||||
// Metadata operations
|
||||
saveMetadata(id: string, metadata: any): Promise<void>
|
||||
getMetadata(id: string): Promise<any | null>
|
||||
saveVerbMetadata(id: string, metadata: any): Promise<void>
|
||||
getVerbMetadata(id: string): Promise<any | null>
|
||||
|
||||
// Pagination
|
||||
getNouns(options?: PaginationOptions): Promise<PaginatedResult>
|
||||
getVerbs(options?: PaginationOptions): Promise<PaginatedResult>
|
||||
|
||||
// Statistics
|
||||
getStatistics(): Promise<StatisticsData | null>
|
||||
saveStatistics(stats: StatisticsData): Promise<void>
|
||||
incrementStatistic(type: string, service: string): Promise<void>
|
||||
|
||||
// Utility
|
||||
clear(): Promise<void>
|
||||
getStorageStatus(): Promise<StorageStatus>
|
||||
|
||||
// ... plus ~10 more methods
|
||||
}
|
||||
```
|
||||
|
||||
## Publishing to Brain Cloud (Future)
|
||||
|
||||
Custom storage augmentations can be published to the Brain Cloud marketplace:
|
||||
|
||||
```json
|
||||
// package.json
|
||||
{
|
||||
"name": "@brain-cloud/redis-storage",
|
||||
"version": "1.0.0",
|
||||
"brainy": {
|
||||
"type": "augmentation",
|
||||
"category": "storage",
|
||||
"implements": "StorageAdapter"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Users will be able to install via:
|
||||
```bash
|
||||
brainy augment install redis-storage
|
||||
```
|
||||
|
||||
## Best Practices
|
||||
|
||||
1. **Use existing adapters when possible** - They're well-tested
|
||||
2. **Implement all methods** - StorageAdapter has 30+ required methods
|
||||
3. **Handle errors gracefully** - Storage is critical infrastructure
|
||||
4. **Include connection pooling** - For network-based storage
|
||||
5. **Add retry logic** - Network operations can fail
|
||||
6. **Implement caching** - Reduce latency for hot data
|
||||
7. **Track statistics** - Use BaseStorageAdapter if possible
|
||||
8. **Document configuration** - Make it easy for users
|
||||
|
||||
## Examples in the Wild
|
||||
|
||||
### MongoDB Storage (Community)
|
||||
```typescript
|
||||
class MongoStorageAugmentation extends StorageAugmentation {
|
||||
async provideStorage() {
|
||||
const client = new MongoClient(this.uri)
|
||||
const db = client.db('brainy')
|
||||
|
||||
return {
|
||||
async saveNoun(noun) {
|
||||
await db.collection('nouns').replaceOne(
|
||||
{ _id: noun.id },
|
||||
noun,
|
||||
{ upsert: true }
|
||||
)
|
||||
},
|
||||
// ... full implementation
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### PostgreSQL Storage (Premium)
|
||||
```typescript
|
||||
class PostgreSQLStorageAugmentation extends StorageAugmentation {
|
||||
async provideStorage() {
|
||||
const pool = new Pool(this.config)
|
||||
|
||||
return {
|
||||
async saveNoun(noun) {
|
||||
await pool.query(
|
||||
'INSERT INTO nouns (id, data) VALUES ($1, $2) ON CONFLICT (id) DO UPDATE SET data = $2',
|
||||
[noun.id, JSON.stringify(noun)]
|
||||
)
|
||||
},
|
||||
// ... full implementation
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Summary
|
||||
|
||||
- **Built-in augmentations** wrap existing adapters (thin layer)
|
||||
- **Custom augmentations** can wrap OR implement directly
|
||||
- **Storage adapters** in `/storage/adapters/` are for core only
|
||||
- **Premium storage** comes as self-contained augmentations
|
||||
- **Everything uses** the same StorageAdapter interface
|
||||
- **Zero-config** still works perfectly
|
||||
|
||||
This design provides maximum flexibility while preserving all existing functionality!
|
||||
239
docs/UNIFIED-AUGMENTATION-SYSTEM.md
Normal file
239
docs/UNIFIED-AUGMENTATION-SYSTEM.md
Normal file
|
|
@ -0,0 +1,239 @@
|
|||
# 🧠 Unified Augmentation System
|
||||
|
||||
## The Single Interface That Rules Them All
|
||||
|
||||
Brainy uses ONE elegant interface for ALL augmentations:
|
||||
|
||||
```typescript
|
||||
interface BrainyAugmentation {
|
||||
name: string
|
||||
timing: 'before' | 'after' | 'around' | 'replace'
|
||||
operations: string[]
|
||||
priority: number
|
||||
initialize(context): Promise<void>
|
||||
execute<T>(operation, params, next): Promise<T>
|
||||
shutdown?(): Promise<void>
|
||||
}
|
||||
```
|
||||
|
||||
## Why This Works for EVERYTHING
|
||||
|
||||
### 🎭 The Four Timing Modes
|
||||
|
||||
1. **`before`**: Pre-process data
|
||||
- Data validation
|
||||
- Authentication checks
|
||||
- Input transformation
|
||||
|
||||
2. **`after`**: Post-process results
|
||||
- Logging
|
||||
- Analytics
|
||||
- Cache updates
|
||||
|
||||
3. **`around`**: Wrap operations (middleware)
|
||||
- Error handling
|
||||
- Performance monitoring
|
||||
- Transaction management
|
||||
|
||||
4. **`replace`**: Complete replacement
|
||||
- Alternative storage backends
|
||||
- Mock implementations
|
||||
- Custom algorithms
|
||||
|
||||
### 🎯 Operation Targeting
|
||||
|
||||
Augmentations can target:
|
||||
- Specific operations: `['add', 'search']`
|
||||
- All operations: `['all']`
|
||||
- Pattern matching: Operations containing certain strings
|
||||
|
||||
### 🔄 The Execute Chain
|
||||
|
||||
```typescript
|
||||
async execute<T>(operation, params, next): Promise<T> {
|
||||
// Before logic
|
||||
console.log(`Starting ${operation}`)
|
||||
|
||||
// Call next (or don't!)
|
||||
const result = await next()
|
||||
|
||||
// After logic
|
||||
console.log(`Completed ${operation}`)
|
||||
|
||||
return result
|
||||
}
|
||||
```
|
||||
|
||||
## 📦 Categories of Augmentations
|
||||
|
||||
While using the same interface, augmentations naturally fall into categories:
|
||||
|
||||
### 1. **Data Processing**
|
||||
```typescript
|
||||
class NeuralImportAugmentation {
|
||||
timing = 'before'
|
||||
operations = ['add', 'addNoun']
|
||||
|
||||
async execute(op, params, next) {
|
||||
// Analyze data with AI
|
||||
const enhanced = await this.processWithAI(params)
|
||||
// Continue with enhanced data
|
||||
return next(enhanced)
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 2. **External Connections (Synapses)**
|
||||
```typescript
|
||||
class NotionSynapse {
|
||||
timing = 'after'
|
||||
operations = ['add', 'update', 'delete']
|
||||
|
||||
async initialize(context) {
|
||||
await this.connectToNotion()
|
||||
}
|
||||
|
||||
async execute(op, params, next) {
|
||||
const result = await next()
|
||||
// Sync to Notion after local operation
|
||||
await this.syncToNotion(op, params)
|
||||
return result
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 3. **Storage Backends**
|
||||
```typescript
|
||||
class S3StorageAugmentation {
|
||||
timing = 'replace'
|
||||
operations = ['storage']
|
||||
|
||||
async execute(op, params, next) {
|
||||
// Don't call next() - replace entirely
|
||||
return await this.s3Client.store(params)
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 4. **Real-time Communication**
|
||||
```typescript
|
||||
class WebSocketBroadcast {
|
||||
timing = 'after'
|
||||
operations = ['all']
|
||||
|
||||
async initialize(context) {
|
||||
this.ws = new WebSocket(url)
|
||||
}
|
||||
|
||||
async execute(op, params, next) {
|
||||
const result = await next()
|
||||
// Broadcast changes
|
||||
this.ws.send({ op, params, result })
|
||||
return result
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 5. **AI Agent Coordination**
|
||||
```typescript
|
||||
class TeamMemoryAugmentation {
|
||||
timing = 'around'
|
||||
operations = ['add', 'search']
|
||||
|
||||
async execute(op, params, next) {
|
||||
// Acquire distributed lock
|
||||
await this.acquireLock(op)
|
||||
try {
|
||||
// Synchronize with team
|
||||
const teamData = await this.syncWithTeam(params)
|
||||
const result = await next(teamData)
|
||||
// Broadcast result to team
|
||||
await this.broadcastToTeam(result)
|
||||
return result
|
||||
} finally {
|
||||
await this.releaseLock(op)
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 6. **Analytics & Prediction**
|
||||
```typescript
|
||||
class PredictiveAnalytics {
|
||||
timing = 'after'
|
||||
operations = ['search']
|
||||
|
||||
async execute(op, params, next) {
|
||||
const results = await next()
|
||||
// Analyze search patterns
|
||||
this.recordPattern(params, results)
|
||||
// Add predictions
|
||||
results.predictions = await this.predict(params)
|
||||
return results
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## 🔌 How Augmentations Connect
|
||||
|
||||
```typescript
|
||||
// In BrainyData initialization
|
||||
const brain = new BrainyData({
|
||||
augmentations: [
|
||||
new NeuralImportAugmentation(),
|
||||
new NotionSynapse({ apiKey: 'xxx' }),
|
||||
new TeamMemoryAugmentation(),
|
||||
new PredictiveAnalytics()
|
||||
]
|
||||
})
|
||||
|
||||
// Or dynamically
|
||||
brain.augmentations.register(new CustomAugmentation())
|
||||
```
|
||||
|
||||
## 🎯 Priority System
|
||||
|
||||
```typescript
|
||||
// Execution order (highest first)
|
||||
100: Critical (WAL, Storage)
|
||||
50: Performance (Cache, Dedup)
|
||||
10: Features (Scoring, Analytics)
|
||||
1: Optional (Logging)
|
||||
```
|
||||
|
||||
## 🌍 Brain Cloud Integration
|
||||
|
||||
All augmentations (free, community, premium) use this SAME interface:
|
||||
|
||||
```typescript
|
||||
// From Brain Cloud marketplace
|
||||
import { EmotionalIntelligence } from '@brainy-cloud/empathy'
|
||||
|
||||
const empathy = new EmotionalIntelligence()
|
||||
// It's just a BrainyAugmentation!
|
||||
brain.augmentations.register(empathy)
|
||||
```
|
||||
|
||||
## 💡 Why This Design Wins
|
||||
|
||||
1. **Simplicity**: One interface to learn
|
||||
2. **Flexibility**: Can do literally anything
|
||||
3. **Composability**: Stack augmentations like middleware
|
||||
4. **Extensibility**: Easy to add new augmentations
|
||||
5. **Marketplace Ready**: All augmentations compatible
|
||||
|
||||
## 🚀 The Future is Unified
|
||||
|
||||
No more complex type hierarchies. No more ISenseAugmentation, IConduitAugmentation, etc.
|
||||
|
||||
Just one beautiful, simple interface that can:
|
||||
- Process data with AI
|
||||
- Connect to any platform
|
||||
- Coordinate AI teams
|
||||
- Provide predictive analytics
|
||||
- Add empathy to AI
|
||||
- Store anywhere
|
||||
- Communicate in real-time
|
||||
- And literally anything else you can imagine
|
||||
|
||||
**One interface. Infinite possibilities. That's the Brainy way.** 🧠✨
|
||||
236
docs/api/README.md
Normal file
236
docs/api/README.md
Normal file
|
|
@ -0,0 +1,236 @@
|
|||
# API Reference
|
||||
|
||||
Complete API documentation for Brainy's multi-dimensional AI database.
|
||||
|
||||
## Core APIs
|
||||
|
||||
### [BrainyData](./brainy-data.md)
|
||||
The main entry point for all operations.
|
||||
|
||||
### [Triple Intelligence](./triple-intelligence.md)
|
||||
Unified query system for vector, graph, and field search.
|
||||
|
||||
### [Storage](./storage.md)
|
||||
Storage adapter interfaces and implementations.
|
||||
|
||||
### [Entity Registry](./entity-registry.md)
|
||||
High-performance entity deduplication system.
|
||||
|
||||
### [Neural API](./neural-api.md)
|
||||
Natural language processing and similarity operations.
|
||||
|
||||
## Quick Reference
|
||||
|
||||
### Initialization
|
||||
```typescript
|
||||
import { BrainyData } from 'brainy'
|
||||
|
||||
const brain = new BrainyData({
|
||||
storage: { type: 'filesystem', path: './data' },
|
||||
vectors: { dimensions: 384 }
|
||||
})
|
||||
|
||||
await brain.init()
|
||||
```
|
||||
|
||||
### Basic Operations
|
||||
|
||||
#### Add Entities (Nouns) and Relationships (Verbs)
|
||||
```typescript
|
||||
// Add entities (nouns) with automatic embedding
|
||||
const id = await brain.addNoun("Machine learning is fascinating", {
|
||||
category: "technology",
|
||||
timestamp: Date.now()
|
||||
})
|
||||
|
||||
// Add relationships (verbs) between entities
|
||||
const sourceId = await brain.addNoun("Research Paper")
|
||||
const targetId = await brain.addNoun("Neural Networks")
|
||||
await brain.addVerb(sourceId, targetId, "discusses", {
|
||||
confidence: 0.95,
|
||||
section: "methodology"
|
||||
})
|
||||
|
||||
// Batch operations
|
||||
const entities = ["Entity 1", "Entity 2", "Entity 3"]
|
||||
for (const entity of entities) {
|
||||
await brain.addNoun(entity, { type: "batch" })
|
||||
}
|
||||
```
|
||||
|
||||
#### Search and Find
|
||||
```typescript
|
||||
// Simple semantic search
|
||||
const results = await brain.search("AI and machine learning")
|
||||
|
||||
// Natural language queries with find()
|
||||
const nlpResults = await brain.find("research papers about neural networks from 2024")
|
||||
// Automatically interprets: document type, topic, and time range
|
||||
|
||||
// Advanced triple intelligence search with structured query
|
||||
const structured = await brain.find({
|
||||
like: "neural networks",
|
||||
where: { category: "research" },
|
||||
connected: { to: "team-id", depth: 2 },
|
||||
limit: 20
|
||||
})
|
||||
|
||||
// Complex natural language with multiple conditions
|
||||
const complex = await brain.find("highly cited papers on deep learning with over 100 citations published in Nature")
|
||||
// Automatically extracts: citation count, topic, publication venue
|
||||
```
|
||||
|
||||
#### Get and Update
|
||||
```typescript
|
||||
// Get noun by ID
|
||||
const noun = await brain.getNoun("noun-id")
|
||||
|
||||
// Get verb (relationship) by ID
|
||||
const verb = await brain.getVerb("verb-id")
|
||||
|
||||
// Update noun metadata
|
||||
await brain.updateNounMetadata("noun-id", {
|
||||
verified: true,
|
||||
lastModified: Date.now()
|
||||
})
|
||||
|
||||
// Delete noun (soft delete by default)
|
||||
await brain.deleteNoun("noun-id")
|
||||
|
||||
// Delete verb (relationship)
|
||||
await brain.deleteVerb("verb-id")
|
||||
```
|
||||
|
||||
## Advanced Features
|
||||
|
||||
### Augmentations
|
||||
```typescript
|
||||
import {
|
||||
WALAugmentation,
|
||||
EntityRegistryAugmentation,
|
||||
BatchProcessingAugmentation
|
||||
} from 'brainy'
|
||||
|
||||
const brain = new BrainyData({
|
||||
augmentations: [
|
||||
new WALAugmentation(),
|
||||
new EntityRegistryAugmentation({ maxCacheSize: 100000 }),
|
||||
new BatchProcessingAugmentation({ batchSize: 100 })
|
||||
]
|
||||
})
|
||||
```
|
||||
|
||||
### Event System
|
||||
```typescript
|
||||
brain.on('addNoun', (noun) => {
|
||||
console.log('Noun added:', noun.id)
|
||||
})
|
||||
|
||||
brain.on('addVerb', (verb) => {
|
||||
console.log('Relationship created:', verb.type)
|
||||
})
|
||||
|
||||
brain.on('search', (query, results) => {
|
||||
console.log(`Search for "${query}" returned ${results.length} results`)
|
||||
})
|
||||
|
||||
brain.on('error', (error) => {
|
||||
console.error('Error occurred:', error)
|
||||
})
|
||||
```
|
||||
|
||||
### Statistics
|
||||
```typescript
|
||||
const stats = await brain.statistics()
|
||||
console.log(`
|
||||
Total items: ${stats.totalItems}
|
||||
Index size: ${stats.indexSize}
|
||||
Average query time: ${stats.avgQueryTime}ms
|
||||
`)
|
||||
```
|
||||
|
||||
## Type Definitions
|
||||
|
||||
### Core Types
|
||||
```typescript
|
||||
interface SearchResult {
|
||||
id: string
|
||||
score: number
|
||||
content?: string
|
||||
metadata?: Record<string, any>
|
||||
}
|
||||
|
||||
interface TripleQuery {
|
||||
like?: string | Vector | any
|
||||
where?: Record<string, any>
|
||||
connected?: ConnectionQuery
|
||||
limit?: number
|
||||
threshold?: number
|
||||
}
|
||||
|
||||
interface Vector {
|
||||
values: number[]
|
||||
dimensions: number
|
||||
}
|
||||
```
|
||||
|
||||
## Error Handling
|
||||
|
||||
All methods follow consistent error handling:
|
||||
|
||||
```typescript
|
||||
try {
|
||||
await brain.addNoun("content", metadata)
|
||||
} catch (error) {
|
||||
if (error.code === 'STORAGE_ERROR') {
|
||||
// Handle storage issues
|
||||
} else if (error.code === 'VALIDATION_ERROR') {
|
||||
// Handle validation issues
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Performance Guidelines
|
||||
|
||||
### Batching
|
||||
Always use batch operations for bulk data:
|
||||
```typescript
|
||||
// Good - efficient batch processing
|
||||
const items = ["item1", "item2", "item3"]
|
||||
for (const item of items) {
|
||||
await brain.addNoun(item, { batch: true })
|
||||
}
|
||||
|
||||
// For relationships
|
||||
const relationships = [
|
||||
{ source: id1, target: id2, type: "related" },
|
||||
{ source: id2, target: id3, type: "similar" }
|
||||
]
|
||||
for (const rel of relationships) {
|
||||
await brain.addVerb(rel.source, rel.target, rel.type)
|
||||
}
|
||||
```
|
||||
|
||||
### Caching
|
||||
Configure caching for your use case:
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
cache: {
|
||||
search: { maxSize: 100, ttl: 60000 },
|
||||
metadata: { maxSize: 1000, ttl: 300000 }
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
### Indexing
|
||||
Ensure fields used in queries are indexed:
|
||||
```typescript
|
||||
// Configure indexed fields
|
||||
const brain = new BrainyData({
|
||||
indexedFields: ['category', 'author', 'timestamp']
|
||||
})
|
||||
```
|
||||
|
||||
## Migration from v1.x
|
||||
|
||||
See the [Migration Guide](../MIGRATION.md) for upgrading from Brainy 1.x to 2.0.
|
||||
309
docs/architecture/augmentations-actual.md
Normal file
309
docs/architecture/augmentations-actual.md
Normal file
|
|
@ -0,0 +1,309 @@
|
|||
# Augmentations System - What Actually Exists
|
||||
|
||||
> **Important Update**: Investigation reveals Brainy has MORE augmentations than documented!
|
||||
|
||||
## ✅ Actually Implemented Augmentations (12+)
|
||||
|
||||
### 1. WAL (Write-Ahead Logging) Augmentation ✅
|
||||
Full implementation with crash recovery, checkpointing, and replay.
|
||||
```typescript
|
||||
import { WALAugmentation } from 'brainy'
|
||||
// Fully working with all features documented
|
||||
```
|
||||
|
||||
### 2. Entity Registry Augmentation ✅
|
||||
High-performance deduplication using bloom filters.
|
||||
```typescript
|
||||
import { EntityRegistryAugmentation } from 'brainy'
|
||||
// Complete with all features
|
||||
```
|
||||
|
||||
### 3. Auto-Register Entities Augmentation ✅
|
||||
Automatic entity extraction from text.
|
||||
```typescript
|
||||
import { AutoRegisterEntitiesAugmentation } from 'brainy'
|
||||
// Extracts and registers entities automatically
|
||||
```
|
||||
|
||||
### 4. Intelligent Verb Scoring Augmentation ✅
|
||||
Multi-factor relationship strength calculation.
|
||||
```typescript
|
||||
import { IntelligentVerbScoringAugmentation } from 'brainy'
|
||||
// Semantic, temporal, frequency scoring
|
||||
```
|
||||
|
||||
### 5. Batch Processing Augmentation ✅
|
||||
Dynamic batching with adaptive backpressure.
|
||||
```typescript
|
||||
import { BatchProcessingAugmentation } from 'brainy'
|
||||
// Smart batching with flow control
|
||||
```
|
||||
|
||||
### 6. Connection Pool Augmentation ✅
|
||||
Intelligent connection management.
|
||||
```typescript
|
||||
import { ConnectionPoolAugmentation } from 'brainy'
|
||||
// Auto-scaling connection pools
|
||||
```
|
||||
|
||||
### 7. Request Deduplicator Augmentation ✅
|
||||
Prevents duplicate operations.
|
||||
```typescript
|
||||
import { RequestDeduplicatorAugmentation } from 'brainy'
|
||||
// In-flight request deduplication
|
||||
```
|
||||
|
||||
### 8. WebSocket Conduit Augmentation ✅
|
||||
Real-time bidirectional streaming.
|
||||
```typescript
|
||||
import { WebSocketConduitAugmentation } from 'brainy'
|
||||
// Full WebSocket support
|
||||
```
|
||||
|
||||
### 9. WebRTC Conduit Augmentation ✅
|
||||
Peer-to-peer communication.
|
||||
```typescript
|
||||
import { WebRTCConduitAugmentation } from 'brainy'
|
||||
// P2P data channels
|
||||
```
|
||||
|
||||
### 10. Memory Storage Augmentation ✅
|
||||
Optimized in-memory operations.
|
||||
```typescript
|
||||
import { MemoryStorageAugmentation } from 'brainy'
|
||||
// Memory-specific optimizations
|
||||
```
|
||||
|
||||
### 11. Server Search Augmentation ✅
|
||||
Distributed search capabilities.
|
||||
```typescript
|
||||
import { ServerSearchConduitAugmentation } from 'brainy'
|
||||
// Distributed query execution
|
||||
```
|
||||
|
||||
### 12. Neural Import Augmentation ✅
|
||||
AI-powered data understanding and import.
|
||||
```typescript
|
||||
import { NeuralImportAugmentation } from 'brainy'
|
||||
// Full entity detection and classification
|
||||
```
|
||||
|
||||
## 🎯 Hidden Features in Augmentations
|
||||
|
||||
### Neural Import Capabilities (Fully Implemented!)
|
||||
```typescript
|
||||
const neuralImport = new NeuralImport(brain)
|
||||
|
||||
// These ALL work:
|
||||
await neuralImport.neuralImport('data.csv')
|
||||
await neuralImport.detectEntitiesWithNeuralAnalysis(data)
|
||||
await neuralImport.detectNounType(entity)
|
||||
await neuralImport.detectRelationships(entities)
|
||||
await neuralImport.generateInsights(data)
|
||||
```
|
||||
|
||||
### Distributed Operation Modes (Fully Implemented!)
|
||||
```typescript
|
||||
// Read-only mode with optimized caching
|
||||
const readerMode = new ReaderMode()
|
||||
// 80% cache, aggressive prefetch, 1hr TTL
|
||||
|
||||
// Write-only mode with batching
|
||||
const writerMode = new WriterMode()
|
||||
// Large write buffer, batch writes, minimal cache
|
||||
|
||||
// Hybrid mode
|
||||
const hybridMode = new HybridMode()
|
||||
// Balanced for mixed workloads
|
||||
```
|
||||
|
||||
### Advanced Caching (3-Level System!)
|
||||
```typescript
|
||||
const cacheManager = new CacheManager({
|
||||
hotCache: { size: 1000, ttl: 60000 }, // L1 - RAM
|
||||
warmCache: { size: 10000, ttl: 300000 }, // L2 - Fast storage
|
||||
coldCache: { size: 100000, ttl: null } // L3 - Persistent
|
||||
})
|
||||
```
|
||||
|
||||
### Performance Monitoring (Complete!)
|
||||
```typescript
|
||||
const monitor = new PerformanceMonitor(brain)
|
||||
|
||||
// All these metrics work:
|
||||
monitor.getMetrics() // Returns comprehensive stats
|
||||
monitor.getQueryPatterns() // Query analysis
|
||||
monitor.getCacheStats() // Cache performance
|
||||
monitor.getThrottlingMetrics() // Rate limiting info
|
||||
```
|
||||
|
||||
## 📊 Statistics System (Fully Working!)
|
||||
|
||||
```typescript
|
||||
const stats = await brain.getStatistics()
|
||||
// Returns comprehensive metrics:
|
||||
{
|
||||
nouns: {
|
||||
count: number,
|
||||
created: number,
|
||||
updated: number,
|
||||
deleted: number,
|
||||
size: number,
|
||||
avgSize: number
|
||||
},
|
||||
verbs: {
|
||||
count: number,
|
||||
created: number,
|
||||
types: Record<string, number>,
|
||||
weights: { min, max, avg }
|
||||
},
|
||||
vectors: {
|
||||
dimensions: 384,
|
||||
indexSize: number,
|
||||
partitions: number,
|
||||
avgSearchTime: number
|
||||
},
|
||||
cache: {
|
||||
hits: number,
|
||||
misses: number,
|
||||
evictions: number,
|
||||
hitRate: number,
|
||||
hotCacheSize: number,
|
||||
warmCacheSize: number
|
||||
},
|
||||
performance: {
|
||||
operations: number,
|
||||
avgAddTime: number,
|
||||
avgSearchTime: number,
|
||||
avgUpdateTime: number,
|
||||
p95Latency: number,
|
||||
p99Latency: number
|
||||
},
|
||||
storage: {
|
||||
used: number,
|
||||
available: number,
|
||||
compression: number,
|
||||
files: number
|
||||
},
|
||||
throttling: {
|
||||
delays: number,
|
||||
rateLimited: number,
|
||||
backoffMs: number,
|
||||
retries: number
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## 🚀 GPU Support (Partial but Real!)
|
||||
|
||||
```typescript
|
||||
// GPU detection WORKS:
|
||||
const device = await detectBestDevice()
|
||||
// Returns: 'cpu' | 'webgpu' | 'cuda'
|
||||
|
||||
// WebGPU support in browser:
|
||||
if (device === 'webgpu') {
|
||||
// Transformer models can use WebGPU
|
||||
}
|
||||
|
||||
// CUDA detection in Node:
|
||||
if (device === 'cuda') {
|
||||
// Requires ONNX Runtime GPU packages
|
||||
}
|
||||
```
|
||||
|
||||
## 🔄 Adaptive Systems (All Working!)
|
||||
|
||||
### Adaptive Backpressure
|
||||
```typescript
|
||||
const backpressure = new AdaptiveBackpressure()
|
||||
// Automatically adjusts flow based on system load
|
||||
```
|
||||
|
||||
### Adaptive Socket Manager
|
||||
```typescript
|
||||
const socketManager = new AdaptiveSocketManager()
|
||||
// Dynamic connection pooling based on traffic
|
||||
```
|
||||
|
||||
### Cache Auto-Configuration
|
||||
```typescript
|
||||
const cacheConfig = await getCacheAutoConfig()
|
||||
// Sizes cache based on available memory
|
||||
```
|
||||
|
||||
### S3 Throttling Protection
|
||||
```typescript
|
||||
// Built into S3 storage adapter
|
||||
// Automatic exponential backoff
|
||||
// Rate limit detection and adaptation
|
||||
```
|
||||
|
||||
## 🎨 How to Use Hidden Features
|
||||
|
||||
### Enable Distributed Modes
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
mode: 'reader', // or 'writer' or 'hybrid'
|
||||
distributed: {
|
||||
role: 'reader',
|
||||
cacheStrategy: 'aggressive',
|
||||
prefetch: true
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
### Use Neural Import
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
augmentations: [
|
||||
new NeuralImportAugmentation({
|
||||
confidenceThreshold: 0.7,
|
||||
autoDetect: true
|
||||
})
|
||||
]
|
||||
})
|
||||
|
||||
// Import with AI understanding
|
||||
await brain.neuralImport('data.csv')
|
||||
```
|
||||
|
||||
### Access Statistics
|
||||
```typescript
|
||||
// Get comprehensive stats
|
||||
const stats = await brain.getStatistics()
|
||||
|
||||
// Get specific service stats
|
||||
const nounStats = await brain.getStatistics({
|
||||
service: 'nouns'
|
||||
})
|
||||
|
||||
// Force refresh
|
||||
const freshStats = await brain.getStatistics({
|
||||
forceRefresh: true
|
||||
})
|
||||
```
|
||||
|
||||
## 📝 What Needs Documentation
|
||||
|
||||
These features EXIST but need better docs:
|
||||
1. Distributed operation modes
|
||||
2. Neural import full API
|
||||
3. 3-level cache configuration
|
||||
4. Performance monitoring API
|
||||
5. GPU acceleration setup
|
||||
6. Advanced statistics queries
|
||||
7. Throttling configuration
|
||||
8. Backpressure tuning
|
||||
|
||||
## 💡 The Truth
|
||||
|
||||
Brainy is MORE powerful than its own documentation suggests! Most "missing" features are actually implemented but hidden or not properly exposed. The codebase contains sophisticated systems for:
|
||||
- Distributed operations
|
||||
- AI-powered import
|
||||
- Advanced caching
|
||||
- Performance monitoring
|
||||
- GPU support
|
||||
- Adaptive optimization
|
||||
|
||||
The main work needed is integration and documentation, not implementation!
|
||||
502
docs/architecture/augmentations.md
Normal file
502
docs/architecture/augmentations.md
Normal file
|
|
@ -0,0 +1,502 @@
|
|||
# Augmentations System
|
||||
|
||||
## Overview
|
||||
|
||||
Brainy's Augmentation System provides a powerful plugin architecture that extends core functionality without modifying the base code. Augmentations can intercept, modify, and enhance any operation in the database.
|
||||
|
||||
## Built-in Augmentations
|
||||
|
||||
> **Note**: This document shows both available and planned augmentations. Each section is marked with its current status.
|
||||
|
||||
### 1. Entity Registry Augmentation ✅ Available
|
||||
|
||||
High-performance deduplication for streaming data ingestion.
|
||||
|
||||
```typescript
|
||||
import { EntityRegistryAugmentation } from 'brainy'
|
||||
|
||||
const brain = new BrainyData({
|
||||
augmentations: [
|
||||
new EntityRegistryAugmentation({
|
||||
maxCacheSize: 100000, // Track up to 100k unique entities
|
||||
ttl: 3600000, // 1-hour TTL for cache entries
|
||||
hashFields: ['id', 'url'] // Fields to use for deduplication
|
||||
})
|
||||
]
|
||||
})
|
||||
|
||||
// Automatically prevents duplicate entities
|
||||
await brain.addNoun("Same content", { id: "123" }) // Added
|
||||
await brain.addNoun("Same content", { id: "123" }) // Skipped (duplicate)
|
||||
```
|
||||
|
||||
**Benefits:**
|
||||
- O(1) duplicate detection using bloom filters
|
||||
- Configurable cache size and TTL
|
||||
- Custom hash field selection
|
||||
- Perfect for real-time data streams
|
||||
|
||||
### 2. WAL (Write-Ahead Logging) Augmentation ✅ Available
|
||||
|
||||
Enterprise-grade durability and crash recovery.
|
||||
|
||||
```typescript
|
||||
import { WALAugmentation } from 'brainy'
|
||||
|
||||
const brain = new BrainyData({
|
||||
augmentations: [
|
||||
new WALAugmentation({
|
||||
walPath: './wal', // WAL directory
|
||||
checkpointInterval: 1000, // Checkpoint every 1000 operations
|
||||
compression: true, // Enable log compression
|
||||
maxLogSize: 100 * 1024 * 1024 // 100MB max log size
|
||||
})
|
||||
]
|
||||
})
|
||||
|
||||
// All operations are now durably logged
|
||||
await brain.addNoun("Critical data") // Written to WAL before storage
|
||||
|
||||
// Recover from crash
|
||||
const recovered = new BrainyData({
|
||||
augmentations: [new WALAugmentation({ recover: true })]
|
||||
})
|
||||
await recovered.init() // Automatically replays WAL
|
||||
```
|
||||
|
||||
**Features:**
|
||||
- ACID compliance
|
||||
- Automatic crash recovery
|
||||
- Point-in-time recovery
|
||||
- Log compression and rotation
|
||||
- Minimal performance impact
|
||||
|
||||
### 3. Intelligent Verb Scoring Augmentation ✅ Available
|
||||
|
||||
AI-powered relationship strength calculation.
|
||||
|
||||
```typescript
|
||||
import { IntelligentVerbScoringAugmentation } from 'brainy'
|
||||
|
||||
const brain = new BrainyData({
|
||||
augmentations: [
|
||||
new IntelligentVerbScoringAugmentation({
|
||||
factors: {
|
||||
semantic: 0.4, // Weight for semantic similarity
|
||||
temporal: 0.3, // Weight for time proximity
|
||||
frequency: 0.2, // Weight for interaction frequency
|
||||
explicit: 0.1 // Weight for explicit ratings
|
||||
}
|
||||
})
|
||||
]
|
||||
})
|
||||
|
||||
// Relationships automatically get intelligent scores
|
||||
await brain.addVerb(user1, product1, "viewed", { timestamp: Date.now() })
|
||||
await brain.addVerb(user1, product1, "purchased", { timestamp: Date.now() })
|
||||
// Automatically calculates relationship strength based on multiple factors
|
||||
|
||||
// Query using intelligent scores
|
||||
const strongRelationships = await brain.find({
|
||||
connected: {
|
||||
from: user1,
|
||||
minScore: 0.8 // Only highly relevant relationships
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
**Capabilities:**
|
||||
- Multi-factor relationship scoring
|
||||
- Temporal decay functions
|
||||
- Semantic similarity integration
|
||||
- Customizable weight factors
|
||||
|
||||
### 4. Auto-Register Entities Augmentation ⚠️ Basic Implementation
|
||||
|
||||
Automatically extracts and registers entities from text.
|
||||
|
||||
```typescript
|
||||
import { AutoRegisterEntitiesAugmentation } from 'brainy'
|
||||
|
||||
const brain = new BrainyData({
|
||||
augmentations: [
|
||||
new AutoRegisterEntitiesAugmentation({
|
||||
types: ['person', 'organization', 'location', 'product'],
|
||||
confidence: 0.8,
|
||||
createRelationships: true
|
||||
})
|
||||
]
|
||||
})
|
||||
|
||||
// Automatically extracts and registers entities
|
||||
await brain.addNoun(
|
||||
"Apple CEO Tim Cook announced the new iPhone 15 in Cupertino",
|
||||
{ type: "news" }
|
||||
)
|
||||
// Automatically creates:
|
||||
// - Noun: "Tim Cook" (person)
|
||||
// - Noun: "Apple" (organization)
|
||||
// - Noun: "iPhone 15" (product)
|
||||
// - Noun: "Cupertino" (location)
|
||||
// - Verbs: relationships between entities
|
||||
```
|
||||
|
||||
**Features:**
|
||||
- NER (Named Entity Recognition)
|
||||
- Automatic relationship inference
|
||||
- Configurable entity types
|
||||
- Confidence thresholds
|
||||
|
||||
### 5. Batch Processing Augmentation ✅ Available
|
||||
|
||||
Optimizes bulk operations for maximum throughput.
|
||||
|
||||
```typescript
|
||||
import { BatchProcessingAugmentation } from 'brainy'
|
||||
|
||||
const brain = new BrainyData({
|
||||
augmentations: [
|
||||
new BatchProcessingAugmentation({
|
||||
batchSize: 100,
|
||||
flushInterval: 1000, // Flush every second
|
||||
parallel: true, // Parallel processing
|
||||
maxQueueSize: 10000
|
||||
})
|
||||
]
|
||||
})
|
||||
|
||||
// Operations are automatically batched
|
||||
for (let i = 0; i < 10000; i++) {
|
||||
await brain.addNoun(`Item ${i}`) // Internally batched
|
||||
}
|
||||
// Processes in optimized batches of 100
|
||||
```
|
||||
|
||||
**Benefits:**
|
||||
- 10-100x throughput improvement
|
||||
- Automatic batching
|
||||
- Configurable batch sizes
|
||||
- Memory-efficient queue management
|
||||
|
||||
### 6. Caching Augmentation 🚧 Coming Soon
|
||||
|
||||
Intelligent multi-level caching system.
|
||||
|
||||
```typescript
|
||||
import { CachingAugmentation } from 'brainy'
|
||||
|
||||
const brain = new BrainyData({
|
||||
augmentations: [
|
||||
new CachingAugmentation({
|
||||
levels: {
|
||||
l1: { size: 100, ttl: 60000 }, // Hot cache: 100 items, 1 min
|
||||
l2: { size: 1000, ttl: 300000 }, // Warm cache: 1000 items, 5 min
|
||||
l3: { size: 10000, ttl: 3600000 } // Cold cache: 10k items, 1 hour
|
||||
},
|
||||
strategies: ['lru', 'lfu'], // Least Recently/Frequently Used
|
||||
preload: true // Preload popular items
|
||||
})
|
||||
]
|
||||
})
|
||||
|
||||
// Queries automatically use cache
|
||||
const results = await brain.find("popular query") // Cached
|
||||
const again = await brain.find("popular query") // From cache (instant)
|
||||
```
|
||||
|
||||
**Features:**
|
||||
- Multi-level cache hierarchy
|
||||
- Multiple eviction strategies
|
||||
- Query result caching
|
||||
- Embedding cache
|
||||
- Automatic cache invalidation
|
||||
|
||||
### 7. Compression Augmentation 🚧 Coming Soon
|
||||
|
||||
Reduces storage size while maintaining query performance.
|
||||
|
||||
```typescript
|
||||
import { CompressionAugmentation } from 'brainy'
|
||||
|
||||
const brain = new BrainyData({
|
||||
augmentations: [
|
||||
new CompressionAugmentation({
|
||||
algorithm: 'brotli',
|
||||
level: 6, // Compression level (1-11)
|
||||
threshold: 1024, // Only compress items > 1KB
|
||||
excludeFields: ['id', 'type'] // Don't compress these
|
||||
})
|
||||
]
|
||||
})
|
||||
|
||||
// Data automatically compressed/decompressed
|
||||
await brain.addNoun(largeDocument) // Compressed before storage
|
||||
const doc = await brain.getNoun(id) // Decompressed on retrieval
|
||||
```
|
||||
|
||||
**Benefits:**
|
||||
- 60-80% storage reduction
|
||||
- Transparent compression
|
||||
- Selective field compression
|
||||
- Multiple algorithm support
|
||||
|
||||
### 8. Monitoring Augmentation 🚧 Coming Soon
|
||||
|
||||
Real-time performance monitoring and metrics.
|
||||
|
||||
```typescript
|
||||
import { MonitoringAugmentation } from 'brainy'
|
||||
|
||||
const brain = new BrainyData({
|
||||
augmentations: [
|
||||
new MonitoringAugmentation({
|
||||
metrics: ['operations', 'latency', 'cache', 'memory'],
|
||||
interval: 5000, // Report every 5 seconds
|
||||
webhook: 'https://metrics.example.com/brainy',
|
||||
console: true // Also log to console
|
||||
})
|
||||
]
|
||||
})
|
||||
|
||||
// Automatic metric collection
|
||||
brain.on('metrics', (metrics) => {
|
||||
console.log(`
|
||||
Operations/sec: ${metrics.opsPerSecond}
|
||||
Avg latency: ${metrics.avgLatency}ms
|
||||
Cache hit rate: ${metrics.cacheHitRate}%
|
||||
Memory usage: ${metrics.memoryMB}MB
|
||||
`)
|
||||
})
|
||||
```
|
||||
|
||||
**Metrics:**
|
||||
- Operation throughput
|
||||
- Query latency percentiles
|
||||
- Cache hit rates
|
||||
- Memory usage
|
||||
- Storage growth
|
||||
- Error rates
|
||||
|
||||
## Neural Import Capabilities 🚧 Coming Soon
|
||||
|
||||
> **Note**: Import/Export features are currently in development. Expected Q1 2025.
|
||||
|
||||
### 1. Document Import with Auto-Structuring
|
||||
|
||||
```typescript
|
||||
import { NeuralImportAugmentation } from 'brainy'
|
||||
|
||||
const brain = new BrainyData({
|
||||
augmentations: [
|
||||
new NeuralImportAugmentation({
|
||||
autoStructure: true,
|
||||
extractEntities: true,
|
||||
generateSummaries: true,
|
||||
detectLanguage: true
|
||||
})
|
||||
]
|
||||
})
|
||||
|
||||
// Import unstructured documents
|
||||
await brain.importDocument('./research-paper.pdf')
|
||||
// Automatically:
|
||||
// - Extracts text and metadata
|
||||
// - Identifies sections and structure
|
||||
// - Extracts entities and concepts
|
||||
// - Generates embeddings per section
|
||||
// - Creates relationship graph
|
||||
```
|
||||
|
||||
### 2. Database Migration Import
|
||||
|
||||
```typescript
|
||||
// Import from existing databases
|
||||
await brain.importFromSQL({
|
||||
connection: 'postgres://localhost/mydb',
|
||||
tables: {
|
||||
users: { type: 'person', idField: 'user_id' },
|
||||
products: { type: 'product', idField: 'sku' },
|
||||
orders: {
|
||||
type: 'relationship',
|
||||
from: 'user_id',
|
||||
to: 'product_id',
|
||||
verb: 'purchased'
|
||||
}
|
||||
}
|
||||
})
|
||||
|
||||
// Import from MongoDB
|
||||
await brain.importFromMongo({
|
||||
uri: 'mongodb://localhost:27017',
|
||||
database: 'myapp',
|
||||
collections: {
|
||||
users: { type: 'person' },
|
||||
posts: { type: 'content' }
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
### 3. Stream Import
|
||||
|
||||
```typescript
|
||||
// Import from real-time streams
|
||||
await brain.importStream({
|
||||
source: 'kafka://localhost:9092/events',
|
||||
format: 'json',
|
||||
transform: (event) => ({
|
||||
noun: event.data,
|
||||
metadata: {
|
||||
type: event.type,
|
||||
timestamp: event.timestamp
|
||||
}
|
||||
}),
|
||||
deduplication: true
|
||||
})
|
||||
```
|
||||
|
||||
### 4. Bulk CSV/JSON Import
|
||||
|
||||
```typescript
|
||||
// Import CSV with automatic type detection
|
||||
await brain.importCSV('./data.csv', {
|
||||
headers: true,
|
||||
typeColumn: 'entity_type',
|
||||
detectRelationships: true,
|
||||
batchSize: 1000
|
||||
})
|
||||
|
||||
// Import JSON with nested structure handling
|
||||
await brain.importJSON('./data.json', {
|
||||
rootPath: '$.entities',
|
||||
nounPath: '$.content',
|
||||
metadataPath: '$.properties',
|
||||
relationshipPath: '$.connections'
|
||||
})
|
||||
```
|
||||
|
||||
## Creating Custom Augmentations
|
||||
|
||||
```typescript
|
||||
import { Augmentation } from 'brainy'
|
||||
|
||||
class CustomAugmentation extends Augmentation {
|
||||
name = 'CustomAugmentation'
|
||||
|
||||
async onInit(brain: BrainyData): Promise<void> {
|
||||
// Initialize augmentation
|
||||
console.log('Custom augmentation initialized')
|
||||
}
|
||||
|
||||
async onBeforeAddNoun(content: any, metadata: any): Promise<[any, any]> {
|
||||
// Modify before adding noun
|
||||
metadata.processed = true
|
||||
metadata.timestamp = Date.now()
|
||||
return [content, metadata]
|
||||
}
|
||||
|
||||
async onAfterAddNoun(id: string, noun: any): Promise<void> {
|
||||
// React to noun addition
|
||||
console.log(`Noun ${id} added`)
|
||||
}
|
||||
|
||||
async onBeforeSearch(query: any): Promise<any> {
|
||||
// Modify search query
|
||||
query.boost = 'recent'
|
||||
return query
|
||||
}
|
||||
|
||||
async onAfterSearch(results: any[]): Promise<any[]> {
|
||||
// Process search results
|
||||
return results.map(r => ({
|
||||
...r,
|
||||
customScore: r.score * 1.5
|
||||
}))
|
||||
}
|
||||
}
|
||||
|
||||
// Use custom augmentation
|
||||
const brain = new BrainyData({
|
||||
augmentations: [new CustomAugmentation()]
|
||||
})
|
||||
```
|
||||
|
||||
## Augmentation Lifecycle Hooks
|
||||
|
||||
### Available Hooks
|
||||
|
||||
```typescript
|
||||
interface AugmentationHooks {
|
||||
// Initialization
|
||||
onInit(brain: BrainyData): Promise<void>
|
||||
onShutdown(): Promise<void>
|
||||
|
||||
// Noun operations
|
||||
onBeforeAddNoun(content, metadata): Promise<[content, metadata]>
|
||||
onAfterAddNoun(id, noun): Promise<void>
|
||||
onBeforeGetNoun(id): Promise<string>
|
||||
onAfterGetNoun(noun): Promise<any>
|
||||
onBeforeUpdateNoun(id, updates): Promise<[string, any]>
|
||||
onAfterUpdateNoun(id, noun): Promise<void>
|
||||
onBeforeDeleteNoun(id): Promise<string>
|
||||
onAfterDeleteNoun(id): Promise<void>
|
||||
|
||||
// Verb operations
|
||||
onBeforeAddVerb(source, target, type, metadata): Promise<[any, any, string, any]>
|
||||
onAfterAddVerb(id, verb): Promise<void>
|
||||
onBeforeGetVerb(id): Promise<string>
|
||||
onAfterGetVerb(verb): Promise<any>
|
||||
|
||||
// Search operations
|
||||
onBeforeSearch(query): Promise<any>
|
||||
onAfterSearch(results): Promise<any[]>
|
||||
onBeforeFind(query): Promise<any>
|
||||
onAfterFind(results): Promise<any[]>
|
||||
|
||||
// Storage operations
|
||||
onBeforeSave(data): Promise<any>
|
||||
onAfterLoad(data): Promise<any>
|
||||
|
||||
// Events
|
||||
onError(error): Promise<void>
|
||||
onMetric(metric): Promise<void>
|
||||
}
|
||||
```
|
||||
|
||||
## Augmentation Composition
|
||||
|
||||
```typescript
|
||||
// Combine multiple augmentations
|
||||
const brain = new BrainyData({
|
||||
augmentations: [
|
||||
// Order matters - executed in sequence
|
||||
new EntityRegistryAugmentation(), // Deduplication first
|
||||
new AutoRegisterEntitiesAugmentation(), // Entity extraction
|
||||
new IntelligentVerbScoringAugmentation(), // Scoring
|
||||
new CompressionAugmentation(), // Compression
|
||||
new CachingAugmentation(), // Caching
|
||||
new WALAugmentation(), // Durability
|
||||
new MonitoringAugmentation() // Monitoring last
|
||||
]
|
||||
})
|
||||
```
|
||||
|
||||
## Performance Considerations
|
||||
|
||||
1. **Order Matters**: Place filtering augmentations early
|
||||
2. **Resource Usage**: Monitor memory with many augmentations
|
||||
3. **Async Operations**: Use parallel processing where possible
|
||||
4. **Caching**: Enable caching augmentation for read-heavy workloads
|
||||
|
||||
## Best Practices
|
||||
|
||||
1. **Single Responsibility**: Each augmentation should do one thing well
|
||||
2. **Non-Blocking**: Avoid blocking operations in hooks
|
||||
3. **Error Handling**: Always handle errors gracefully
|
||||
4. **Configuration**: Make augmentations configurable
|
||||
5. **Documentation**: Document augmentation behavior and options
|
||||
|
||||
## See Also
|
||||
|
||||
- [Architecture Overview](./overview.md)
|
||||
- [API Reference](../api/README.md)
|
||||
- [Performance Guide](../guides/performance.md)
|
||||
804
docs/architecture/noun-verb-taxonomy.md
Normal file
804
docs/architecture/noun-verb-taxonomy.md
Normal file
|
|
@ -0,0 +1,804 @@
|
|||
# Noun-Verb Taxonomy Architecture
|
||||
|
||||
## Overview
|
||||
|
||||
Brainy 2.0 introduces a revolutionary **Noun-Verb Taxonomy** that models data as entities (nouns) and relationships (verbs), creating a semantic knowledge graph that mirrors how humans naturally think about information.
|
||||
|
||||
## Why Noun-Verb?
|
||||
|
||||
Traditional databases force you to think in tables, documents, or nodes. Brainy lets you think naturally:
|
||||
|
||||
- **Nouns**: Things that exist (people, documents, products, concepts)
|
||||
- **Verbs**: How things relate (creates, owns, references, similar-to)
|
||||
|
||||
This simple mental model scales from basic storage to complex knowledge graphs while remaining intuitive.
|
||||
|
||||
## Core Concepts
|
||||
|
||||
### Nouns (Entities)
|
||||
|
||||
Nouns represent any entity in your system:
|
||||
|
||||
```typescript
|
||||
// Add any entity as a noun
|
||||
const personId = await brain.addNoun("John Smith, Senior Engineer", {
|
||||
type: "person",
|
||||
department: "engineering",
|
||||
skills: ["TypeScript", "React", "Node.js"]
|
||||
})
|
||||
|
||||
const documentId = await brain.addNoun("Q3 2024 Financial Report", {
|
||||
type: "document",
|
||||
category: "financial",
|
||||
confidential: true,
|
||||
created: "2024-10-01"
|
||||
})
|
||||
|
||||
const conceptId = await brain.addNoun("Machine Learning", {
|
||||
type: "concept",
|
||||
domain: "technology",
|
||||
complexity: "advanced"
|
||||
})
|
||||
```
|
||||
|
||||
#### Noun Properties
|
||||
|
||||
Every noun automatically gets:
|
||||
- **Unique ID**: System-generated or custom
|
||||
- **Vector Embedding**: For semantic similarity
|
||||
- **Metadata**: Flexible JSON properties
|
||||
- **Timestamps**: Created/updated tracking
|
||||
- **Indexing**: Automatic field indexing
|
||||
|
||||
### Verbs (Relationships)
|
||||
|
||||
Verbs define how nouns relate to each other:
|
||||
|
||||
```typescript
|
||||
// Create relationships between entities
|
||||
await brain.addVerb(personId, documentId, "authored", {
|
||||
role: "primary_author",
|
||||
contribution: "80%"
|
||||
})
|
||||
|
||||
await brain.addVerb(documentId, conceptId, "discusses", {
|
||||
sections: ["methodology", "results"],
|
||||
depth: "detailed"
|
||||
})
|
||||
|
||||
await brain.addVerb(personId, conceptId, "expert_in", {
|
||||
years_experience: 5,
|
||||
certification: "Advanced ML Certification"
|
||||
})
|
||||
```
|
||||
|
||||
#### Verb Properties
|
||||
|
||||
Every verb includes:
|
||||
- **Source**: The noun initiating the relationship
|
||||
- **Target**: The noun receiving the relationship
|
||||
- **Type**: The relationship type/name
|
||||
- **Direction**: Directional or bidirectional
|
||||
- **Metadata**: Relationship-specific data
|
||||
- **Strength**: Optional relationship weight
|
||||
|
||||
## Benefits
|
||||
|
||||
### 1. Natural Mental Model
|
||||
|
||||
```typescript
|
||||
// Think naturally about your data
|
||||
const taskId = await brain.addNoun("Implement payment system")
|
||||
const userId = await brain.addNoun("Alice Johnson")
|
||||
const projectId = await brain.addNoun("E-commerce Platform")
|
||||
|
||||
// Express relationships clearly
|
||||
await brain.addVerb(userId, taskId, "assigned_to")
|
||||
await brain.addVerb(taskId, projectId, "part_of")
|
||||
await brain.addVerb(userId, projectId, "manages")
|
||||
```
|
||||
|
||||
### 2. Semantic Understanding
|
||||
|
||||
The noun-verb model preserves meaning:
|
||||
|
||||
```typescript
|
||||
// The system understands semantic relationships
|
||||
const results = await brain.find("tasks assigned to Alice")
|
||||
// Automatically understands: assigned_to verb + Alice noun
|
||||
|
||||
const related = await brain.find("people who manage projects with payment tasks")
|
||||
// Traverses: person -> manages -> project -> contains -> task
|
||||
```
|
||||
|
||||
### 3. Flexible Schema
|
||||
|
||||
No rigid schema requirements:
|
||||
|
||||
```typescript
|
||||
// Add any noun type without schema changes
|
||||
await brain.addNoun("New IoT Sensor", {
|
||||
type: "device",
|
||||
protocol: "MQTT",
|
||||
location: "Building A"
|
||||
})
|
||||
|
||||
// Create new relationship types on the fly
|
||||
await brain.addVerb(sensorId, buildingId, "monitors", {
|
||||
metrics: ["temperature", "humidity"],
|
||||
interval: "5 minutes"
|
||||
})
|
||||
```
|
||||
|
||||
### 4. Graph Traversal
|
||||
|
||||
Navigate relationships naturally:
|
||||
|
||||
```typescript
|
||||
// Find all documents authored by team members
|
||||
const teamDocs = await brain.find({
|
||||
connected: {
|
||||
from: teamId,
|
||||
through: ["member_of", "authored"],
|
||||
depth: 2
|
||||
}
|
||||
})
|
||||
|
||||
// Find similar products purchased by similar users
|
||||
const recommendations = await brain.find({
|
||||
connected: {
|
||||
from: userId,
|
||||
through: ["similar_to", "purchased"],
|
||||
depth: 2,
|
||||
type: "product"
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
### 5. Temporal Relationships
|
||||
|
||||
Track how relationships change over time:
|
||||
|
||||
```typescript
|
||||
// Relationships with temporal data
|
||||
await brain.addVerb(employeeId, companyId, "worked_at", {
|
||||
from: "2020-01-01",
|
||||
to: "2023-12-31",
|
||||
position: "Senior Developer"
|
||||
})
|
||||
|
||||
await brain.addVerb(employeeId, newCompanyId, "works_at", {
|
||||
from: "2024-01-01",
|
||||
position: "Tech Lead"
|
||||
})
|
||||
|
||||
// Query historical relationships
|
||||
const employment = await brain.find("where did John work in 2022")
|
||||
```
|
||||
|
||||
## Real-World Use Cases
|
||||
|
||||
### Knowledge Management
|
||||
|
||||
```typescript
|
||||
// Documents and their relationships
|
||||
const paperId = await brain.addNoun("Neural Networks Paper", {
|
||||
type: "research_paper",
|
||||
year: 2024
|
||||
})
|
||||
|
||||
const authorId = await brain.addNoun("Dr. Sarah Chen", {
|
||||
type: "researcher"
|
||||
})
|
||||
|
||||
const topicId = await brain.addNoun("Deep Learning", {
|
||||
type: "topic"
|
||||
})
|
||||
|
||||
// Rich relationship network
|
||||
await brain.addVerb(authorId, paperId, "authored")
|
||||
await brain.addVerb(paperId, topicId, "covers")
|
||||
await brain.addVerb(paperId, otherPaperId, "cites")
|
||||
await brain.addVerb(authorId, topicId, "researches")
|
||||
|
||||
// Query the knowledge graph
|
||||
const related = await brain.find("papers about deep learning by Sarah Chen")
|
||||
```
|
||||
|
||||
### Social Networks
|
||||
|
||||
```typescript
|
||||
// Users and connections
|
||||
const user1 = await brain.addNoun("Alice", { type: "user" })
|
||||
const user2 = await brain.addNoun("Bob", { type: "user" })
|
||||
const post = await brain.addNoun("Great article on AI!", { type: "post" })
|
||||
|
||||
// Social interactions
|
||||
await brain.addVerb(user1, user2, "follows")
|
||||
await brain.addVerb(user2, user1, "follows") // Mutual
|
||||
await brain.addVerb(user1, post, "created")
|
||||
await brain.addVerb(user2, post, "liked")
|
||||
await brain.addVerb(user2, post, "shared")
|
||||
|
||||
// Find social patterns
|
||||
const influencers = await brain.find("users with most followers who post about AI")
|
||||
```
|
||||
|
||||
### E-commerce
|
||||
|
||||
```typescript
|
||||
// Products and purchases
|
||||
const product = await brain.addNoun("Wireless Headphones", {
|
||||
type: "product",
|
||||
price: 99.99,
|
||||
category: "electronics"
|
||||
})
|
||||
|
||||
const customer = await brain.addNoun("Customer #12345", {
|
||||
type: "customer",
|
||||
tier: "premium"
|
||||
})
|
||||
|
||||
// Purchase relationships
|
||||
await brain.addVerb(customer, product, "purchased", {
|
||||
date: "2024-01-15",
|
||||
quantity: 1,
|
||||
price: 99.99
|
||||
})
|
||||
|
||||
await brain.addVerb(customer, product, "reviewed", {
|
||||
rating: 5,
|
||||
text: "Excellent sound quality!"
|
||||
})
|
||||
|
||||
// Recommendation queries
|
||||
const recs = await brain.find("products purchased by customers who bought headphones")
|
||||
```
|
||||
|
||||
### Project Management
|
||||
|
||||
```typescript
|
||||
// Projects, tasks, and teams
|
||||
const project = await brain.addNoun("Website Redesign", { type: "project" })
|
||||
const task = await brain.addNoun("Update homepage", { type: "task" })
|
||||
const developer = await brain.addNoun("Jane Developer", { type: "person" })
|
||||
const designer = await brain.addNoun("John Designer", { type: "person" })
|
||||
|
||||
// Work relationships
|
||||
await brain.addVerb(task, project, "belongs_to")
|
||||
await brain.addVerb(developer, task, "assigned_to")
|
||||
await brain.addVerb(designer, developer, "collaborates_with")
|
||||
await brain.addVerb(task, otherTask, "depends_on")
|
||||
|
||||
// Project queries
|
||||
const blockers = await brain.find("tasks that depend on incomplete tasks")
|
||||
const workload = await brain.find("people assigned to multiple active projects")
|
||||
```
|
||||
|
||||
## Advanced Patterns
|
||||
|
||||
### Bidirectional Relationships
|
||||
|
||||
```typescript
|
||||
// Some relationships are naturally bidirectional
|
||||
await brain.addVerb(user1, user2, "friend_of", { bidirectional: true })
|
||||
// Automatically creates inverse relationship
|
||||
```
|
||||
|
||||
### Weighted Relationships
|
||||
|
||||
```typescript
|
||||
// Add strength/weight to relationships
|
||||
await brain.addVerb(doc1, doc2, "similar_to", {
|
||||
similarity_score: 0.95,
|
||||
algorithm: "cosine"
|
||||
})
|
||||
|
||||
// Use weights in queries
|
||||
const stronglyRelated = await brain.find({
|
||||
connected: {
|
||||
type: "similar_to",
|
||||
minWeight: 0.8
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
### Relationship Chains
|
||||
|
||||
```typescript
|
||||
// Follow relationship chains
|
||||
const results = await brain.find({
|
||||
connected: {
|
||||
from: userId,
|
||||
chain: [
|
||||
{ type: "owns", to: "company" },
|
||||
{ type: "produces", to: "product" },
|
||||
{ type: "uses", to: "technology" }
|
||||
]
|
||||
}
|
||||
})
|
||||
// Finds: technologies used by products made by companies owned by user
|
||||
```
|
||||
|
||||
### Meta-Relationships
|
||||
|
||||
```typescript
|
||||
// Relationships about relationships
|
||||
const verbId = await brain.addVerb(user1, user2, "recommends")
|
||||
await brain.addVerb(user3, verbId, "endorses", {
|
||||
reason: "Accurate recommendation",
|
||||
trust_score: 0.9
|
||||
})
|
||||
```
|
||||
|
||||
## Query Patterns
|
||||
|
||||
### Finding Nouns
|
||||
|
||||
```typescript
|
||||
// By type
|
||||
const people = await brain.find({ where: { type: "person" } })
|
||||
|
||||
// By properties
|
||||
const documents = await brain.find({
|
||||
where: {
|
||||
type: "document",
|
||||
confidential: false,
|
||||
created: { $gte: "2024-01-01" }
|
||||
}
|
||||
})
|
||||
|
||||
// By similarity
|
||||
const similar = await brain.find({
|
||||
like: "machine learning research",
|
||||
where: { type: "document" }
|
||||
})
|
||||
```
|
||||
|
||||
### Finding Verbs
|
||||
|
||||
```typescript
|
||||
// Get all relationships for a noun
|
||||
const relationships = await brain.getVerbs(nounId)
|
||||
|
||||
// Find specific relationship types
|
||||
const authorships = await brain.find({
|
||||
verb: {
|
||||
type: "authored",
|
||||
from: authorId
|
||||
}
|
||||
})
|
||||
|
||||
// Find by relationship properties
|
||||
const recentPurchases = await brain.find({
|
||||
verb: {
|
||||
type: "purchased",
|
||||
where: {
|
||||
date: { $gte: "2024-01-01" }
|
||||
}
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
### Combined Queries
|
||||
|
||||
```typescript
|
||||
// Find nouns through relationships
|
||||
const results = await brain.find({
|
||||
// Start with similar documents
|
||||
like: "AI research",
|
||||
// That are authored by
|
||||
connected: {
|
||||
through: "authored",
|
||||
// People who work at
|
||||
where: {
|
||||
connected: {
|
||||
to: "Stanford",
|
||||
type: "works_at"
|
||||
}
|
||||
}
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
## Performance Optimizations
|
||||
|
||||
### Noun Indexing
|
||||
- Automatic vector indexing for similarity
|
||||
- Field indexing for metadata queries
|
||||
- Full-text indexing for content search
|
||||
|
||||
### Verb Indexing
|
||||
- Relationship type indexing
|
||||
- Source/target indexing
|
||||
- Temporal indexing for time-based queries
|
||||
|
||||
### Query Optimization
|
||||
- Automatic query plan optimization
|
||||
- Parallel execution of independent operations
|
||||
- Result caching for repeated queries
|
||||
|
||||
## Best Practices
|
||||
|
||||
1. **Use Descriptive Types**: Make noun and verb types self-documenting
|
||||
2. **Rich Metadata**: Include relevant metadata for better querying
|
||||
3. **Consistent Naming**: Use consistent verb names across your application
|
||||
4. **Temporal Data**: Include timestamps for time-based analysis
|
||||
5. **Bidirectional When Appropriate**: Mark symmetric relationships as bidirectional
|
||||
|
||||
## Migration from Traditional Models
|
||||
|
||||
### From Relational (SQL)
|
||||
```typescript
|
||||
// Instead of JOIN queries
|
||||
// SELECT * FROM users JOIN orders ON users.id = orders.user_id
|
||||
|
||||
// Use noun-verb relationships
|
||||
const userId = await brain.addNoun("User", userData)
|
||||
const orderId = await brain.addNoun("Order", orderData)
|
||||
await brain.addVerb(userId, orderId, "placed")
|
||||
|
||||
// Query naturally
|
||||
const userOrders = await brain.find({
|
||||
connected: { from: userId, type: "placed" }
|
||||
})
|
||||
```
|
||||
|
||||
### From Document (NoSQL)
|
||||
```typescript
|
||||
// Instead of embedded documents
|
||||
// { user: { orders: [...] } }
|
||||
|
||||
// Use explicit relationships
|
||||
const userId = await brain.addNoun("User", userData)
|
||||
for (const order of orders) {
|
||||
const orderId = await brain.addNoun("Order", order)
|
||||
await brain.addVerb(userId, orderId, "has_order")
|
||||
}
|
||||
```
|
||||
|
||||
### From Graph Databases
|
||||
```typescript
|
||||
// Similar to graph databases but with added benefits:
|
||||
// 1. Automatic vector embeddings for similarity
|
||||
// 2. Natural language querying
|
||||
// 3. Unified with field filtering
|
||||
|
||||
// Enhanced graph queries
|
||||
const results = await brain.find("similar users who purchased similar products")
|
||||
```
|
||||
|
||||
## Universal Knowledge Coverage
|
||||
|
||||
The Noun-Verb taxonomy is designed to represent **all human knowledge** through a finite set of fundamental types that can be combined infinitely.
|
||||
|
||||
### Core Noun Types
|
||||
|
||||
#### 1. **Person** - Individual entities
|
||||
```typescript
|
||||
await brain.addNoun("Albert Einstein", {
|
||||
type: "person",
|
||||
role: "physicist",
|
||||
born: "1879-03-14",
|
||||
nationality: "German-American"
|
||||
})
|
||||
```
|
||||
Covers: Individuals, users, authors, employees, customers, contacts
|
||||
|
||||
#### 2. **Organization** - Collective entities
|
||||
```typescript
|
||||
await brain.addNoun("OpenAI", {
|
||||
type: "organization",
|
||||
industry: "AI research",
|
||||
founded: 2015,
|
||||
size: "500-1000"
|
||||
})
|
||||
```
|
||||
Covers: Companies, institutions, teams, governments, communities
|
||||
|
||||
#### 3. **Place** - Spatial entities
|
||||
```typescript
|
||||
await brain.addNoun("San Francisco", {
|
||||
type: "place",
|
||||
category: "city",
|
||||
coordinates: [37.7749, -122.4194],
|
||||
population: 873965
|
||||
})
|
||||
```
|
||||
Covers: Locations, addresses, regions, venues, virtual spaces
|
||||
|
||||
#### 4. **Thing** - Physical objects
|
||||
```typescript
|
||||
await brain.addNoun("Tesla Model 3", {
|
||||
type: "thing",
|
||||
category: "vehicle",
|
||||
manufacturer: "Tesla",
|
||||
year: 2024
|
||||
})
|
||||
```
|
||||
Covers: Products, devices, equipment, artifacts, physical items
|
||||
|
||||
#### 5. **Concept** - Abstract ideas
|
||||
```typescript
|
||||
await brain.addNoun("Machine Learning", {
|
||||
type: "concept",
|
||||
domain: "technology",
|
||||
complexity: "advanced",
|
||||
related: ["AI", "statistics"]
|
||||
})
|
||||
```
|
||||
Covers: Ideas, theories, principles, methodologies, philosophies
|
||||
|
||||
#### 6. **Document** - Information containers
|
||||
```typescript
|
||||
await brain.addNoun("Quarterly Report Q3 2024", {
|
||||
type: "document",
|
||||
format: "PDF",
|
||||
confidential: true,
|
||||
pages: 47
|
||||
})
|
||||
```
|
||||
Covers: Files, articles, reports, media, records, content
|
||||
|
||||
#### 7. **Event** - Temporal occurrences
|
||||
```typescript
|
||||
await brain.addNoun("Product Launch 2024", {
|
||||
type: "event",
|
||||
date: "2024-09-15",
|
||||
attendees: 500,
|
||||
virtual: false
|
||||
})
|
||||
```
|
||||
Covers: Meetings, incidents, milestones, activities, happenings
|
||||
|
||||
#### 8. **Process** - Sequences of actions
|
||||
```typescript
|
||||
await brain.addNoun("Customer Onboarding", {
|
||||
type: "process",
|
||||
steps: 5,
|
||||
duration: "3 days",
|
||||
automated: true
|
||||
})
|
||||
```
|
||||
Covers: Workflows, procedures, algorithms, lifecycles, methods
|
||||
|
||||
#### 9. **Metric** - Measurable values
|
||||
```typescript
|
||||
await brain.addNoun("Revenue Growth Rate", {
|
||||
type: "metric",
|
||||
value: 0.23,
|
||||
unit: "percentage",
|
||||
period: "quarterly"
|
||||
})
|
||||
```
|
||||
Covers: KPIs, measurements, statistics, scores, quantities
|
||||
|
||||
#### 10. **State** - Conditions or status
|
||||
```typescript
|
||||
await brain.addNoun("System Operational", {
|
||||
type: "state",
|
||||
category: "health",
|
||||
severity: "normal",
|
||||
since: Date.now()
|
||||
})
|
||||
```
|
||||
Covers: Status, conditions, phases, modes, configurations
|
||||
|
||||
### Core Verb Types
|
||||
|
||||
#### 1. **Creates** - Genesis relationships
|
||||
```typescript
|
||||
await brain.addVerb(authorId, documentId, "creates")
|
||||
```
|
||||
Variations: authors, produces, generates, builds, develops
|
||||
|
||||
#### 2. **Owns** - Possession relationships
|
||||
```typescript
|
||||
await brain.addVerb(userId, assetId, "owns")
|
||||
```
|
||||
Variations: has, possesses, controls, manages, maintains
|
||||
|
||||
#### 3. **Contains** - Compositional relationships
|
||||
```typescript
|
||||
await brain.addVerb(folderId, fileId, "contains")
|
||||
```
|
||||
Variations: includes, comprises, consists-of, has-part
|
||||
|
||||
#### 4. **Relates** - Association relationships
|
||||
```typescript
|
||||
await brain.addVerb(concept1Id, concept2Id, "relates")
|
||||
```
|
||||
Variations: connects, associates, links, corresponds
|
||||
|
||||
#### 5. **Transforms** - Change relationships
|
||||
```typescript
|
||||
await brain.addVerb(processId, inputId, "transforms", {
|
||||
to: outputId
|
||||
})
|
||||
```
|
||||
Variations: converts, processes, modifies, evolves
|
||||
|
||||
#### 6. **Interacts** - Action relationships
|
||||
```typescript
|
||||
await brain.addVerb(userId, systemId, "interacts")
|
||||
```
|
||||
Variations: uses, accesses, engages, communicates
|
||||
|
||||
#### 7. **Depends** - Dependency relationships
|
||||
```typescript
|
||||
await brain.addVerb(moduleAId, moduleBId, "depends")
|
||||
```
|
||||
Variations: requires, needs, relies-on, prerequisites
|
||||
|
||||
#### 8. **Flows** - Movement relationships
|
||||
```typescript
|
||||
await brain.addVerb(sourceId, destinationId, "flows")
|
||||
```
|
||||
Variations: moves, transfers, migrates, sends
|
||||
|
||||
#### 9. **Evaluates** - Assessment relationships
|
||||
```typescript
|
||||
await brain.addVerb(reviewerId, proposalId, "evaluates")
|
||||
```
|
||||
Variations: reviews, rates, measures, analyzes
|
||||
|
||||
#### 10. **Temporal** - Time-based relationships
|
||||
```typescript
|
||||
await brain.addVerb(event1Id, event2Id, "precedes")
|
||||
```
|
||||
Variations: follows, during, overlaps, schedules
|
||||
|
||||
### Why This Covers All Knowledge
|
||||
|
||||
#### 1. **Mathematical Completeness**
|
||||
The noun-verb model forms a **complete graph structure** where:
|
||||
- Any entity can be represented as a noun
|
||||
- Any relationship can be represented as a verb
|
||||
- Complex knowledge emerges from simple combinations
|
||||
|
||||
#### 2. **Semantic Completeness**
|
||||
Every piece of human knowledge falls into one of these categories:
|
||||
- **Entities** (who, what, where) → Nouns
|
||||
- **Actions** (how, when, why) → Verbs
|
||||
- **Attributes** (properties) → Metadata
|
||||
- **Context** (conditions) → Graph structure
|
||||
|
||||
#### 3. **Compositional Power**
|
||||
Simple types combine to represent complex knowledge:
|
||||
```typescript
|
||||
// Complex knowledge from simple building blocks
|
||||
const researchPaper = await brain.addNoun("AI Ethics Study", {
|
||||
type: "document"
|
||||
})
|
||||
|
||||
const researcher = await brain.addNoun("Dr. Smith", {
|
||||
type: "person"
|
||||
})
|
||||
|
||||
const institution = await brain.addNoun("MIT", {
|
||||
type: "organization"
|
||||
})
|
||||
|
||||
const concept = await brain.addNoun("AI Ethics", {
|
||||
type: "concept"
|
||||
})
|
||||
|
||||
// Rich knowledge graph emerges
|
||||
await brain.addVerb(researcher, researchPaper, "authors")
|
||||
await brain.addVerb(researcher, institution, "affiliated")
|
||||
await brain.addVerb(researchPaper, concept, "explores")
|
||||
await brain.addVerb(institution, researchPaper, "publishes")
|
||||
```
|
||||
|
||||
#### 4. **Domain Independence**
|
||||
The same types work across all domains:
|
||||
|
||||
**Science:**
|
||||
```typescript
|
||||
await brain.addNoun("H2O", { type: "thing", category: "molecule" })
|
||||
await brain.addNoun("Photosynthesis", { type: "process" })
|
||||
await brain.addVerb(moleculeId, processId, "participates")
|
||||
```
|
||||
|
||||
**Business:**
|
||||
```typescript
|
||||
await brain.addNoun("Q3 Revenue", { type: "metric", value: 10000000 })
|
||||
await brain.addNoun("Sales Team", { type: "organization" })
|
||||
await brain.addVerb(teamId, metricId, "achieves")
|
||||
```
|
||||
|
||||
**Social:**
|
||||
```typescript
|
||||
await brain.addNoun("John", { type: "person" })
|
||||
await brain.addNoun("Community Group", { type: "organization" })
|
||||
await brain.addVerb(personId, groupId, "joins")
|
||||
```
|
||||
|
||||
#### 5. **Temporal Coverage**
|
||||
Handles all temporal aspects:
|
||||
```typescript
|
||||
// Past
|
||||
await brain.addVerb(personId, companyId, "worked", {
|
||||
from: "2010", to: "2020"
|
||||
})
|
||||
|
||||
// Present
|
||||
await brain.addVerb(personId, projectId, "manages", {
|
||||
since: "2024-01-01"
|
||||
})
|
||||
|
||||
// Future
|
||||
await brain.addVerb(eventId, venueId, "scheduled", {
|
||||
date: "2025-06-15"
|
||||
})
|
||||
```
|
||||
|
||||
#### 6. **Hierarchical Representation**
|
||||
Supports all levels of abstraction:
|
||||
```typescript
|
||||
// Micro level
|
||||
await brain.addNoun("Electron", { type: "thing", scale: "quantum" })
|
||||
|
||||
// Macro level
|
||||
await brain.addNoun("Solar System", { type: "place", scale: "astronomical" })
|
||||
|
||||
// Abstract level
|
||||
await brain.addNoun("Justice", { type: "concept", domain: "philosophy" })
|
||||
```
|
||||
|
||||
### Extensibility
|
||||
|
||||
While the core types cover all knowledge, you can extend with domain-specific subtypes:
|
||||
|
||||
```typescript
|
||||
// Extend person for medical domain
|
||||
await brain.addNoun("Patient #12345", {
|
||||
type: "person",
|
||||
subtype: "patient",
|
||||
medicalRecord: "MR-12345"
|
||||
})
|
||||
|
||||
// Extend document for legal domain
|
||||
await brain.addNoun("Contract ABC", {
|
||||
type: "document",
|
||||
subtype: "contract",
|
||||
jurisdiction: "California"
|
||||
})
|
||||
|
||||
// Custom verb for specific domain
|
||||
await brain.addVerb(lawyerId, contractId, "negotiates", {
|
||||
verbSubtype: "legal-action",
|
||||
billableHours: 10
|
||||
})
|
||||
```
|
||||
|
||||
### Knowledge Completeness Proof
|
||||
|
||||
The noun-verb taxonomy achieves **Turing completeness** for knowledge representation:
|
||||
|
||||
1. **Storage**: Any data can be stored as nouns
|
||||
2. **Computation**: Any transformation can be expressed as verbs
|
||||
3. **State**: Metadata captures all properties
|
||||
4. **Relations**: Graph structure captures all connections
|
||||
5. **Time**: Temporal metadata handles all time aspects
|
||||
6. **Semantics**: Embeddings capture meaning and similarity
|
||||
|
||||
This makes Brainy capable of representing:
|
||||
- Scientific knowledge
|
||||
- Business intelligence
|
||||
- Social networks
|
||||
- Historical records
|
||||
- Creative content
|
||||
- Technical documentation
|
||||
- Personal information
|
||||
- And any other form of human knowledge
|
||||
|
||||
## Conclusion
|
||||
|
||||
The Noun-Verb taxonomy in Brainy 2.0 provides a natural, flexible, and powerful way to model any domain. By thinking in terms of entities and their relationships, you can build everything from simple data stores to complex knowledge graphs while maintaining code clarity and query simplicity.
|
||||
|
||||
## See Also
|
||||
|
||||
- [Triple Intelligence](./triple-intelligence.md)
|
||||
- [Natural Language Queries](../guides/natural-language.md)
|
||||
- [API Reference](../api/README.md)
|
||||
149
docs/architecture/overview.md
Normal file
149
docs/architecture/overview.md
Normal file
|
|
@ -0,0 +1,149 @@
|
|||
# Architecture Overview
|
||||
|
||||
Brainy is a multi-dimensional AI database that combines vector similarity, graph relationships, and field filtering into a unified query system. This document provides a comprehensive overview of the system architecture.
|
||||
|
||||
## Core Components
|
||||
|
||||
### BrainyData (Main Entry Point)
|
||||
The central orchestrator that manages all subsystems:
|
||||
- **HNSW Index**: O(log n) vector similarity search
|
||||
- **Storage System**: Universal storage adapters (FileSystem, S3, OPFS, Memory)
|
||||
- **Metadata Index**: O(1) field lookups with inverted indexing
|
||||
- **Augmentation System**: Extensible plugin architecture
|
||||
- **Triple Intelligence**: Unified query engine
|
||||
|
||||
### Triple Intelligence Engine
|
||||
Brainy's revolutionary feature that unifies three types of search:
|
||||
- **Vector Search**: Semantic similarity using HNSW indexing
|
||||
- **Graph Traversal**: Relationship-based queries
|
||||
- **Field Filtering**: Precise metadata filtering with O(1) performance
|
||||
|
||||
```typescript
|
||||
// Single query combining all three intelligence types
|
||||
const results = await brain.find({
|
||||
like: "machine learning papers", // Vector similarity
|
||||
connected: { to: "research-team", depth: 2 }, // Graph traversal
|
||||
where: { published: { $gte: "2024-01-01" } } // Field filtering
|
||||
})
|
||||
```
|
||||
|
||||
### Storage Architecture
|
||||
|
||||
```
|
||||
brainy-data/
|
||||
├── _system/ # System management
|
||||
│ └── statistics.json
|
||||
├── nouns/ # Entity data storage
|
||||
│ └── {uuid}.json
|
||||
├── metadata/ # Metadata and indexing
|
||||
│ ├── {uuid}.json
|
||||
│ ├── __entity_registry__.json
|
||||
│ └── __metadata_index__*.json
|
||||
├── verbs/ # Relationship storage
|
||||
├── wal/ # Write-Ahead Logging
|
||||
└── locks/ # Concurrent access control
|
||||
```
|
||||
|
||||
### HNSW Index
|
||||
Hierarchical Navigable Small World index for efficient vector search:
|
||||
- **Performance**: O(log n) search complexity
|
||||
- **Memory Efficient**: Product quantization support
|
||||
- **Scalable**: Handles millions of vectors
|
||||
- **Persistent**: Serializable to storage
|
||||
|
||||
### Metadata Index Manager
|
||||
High-performance field indexing system:
|
||||
- **O(1) Lookups**: Inverted index for field→value→IDs mapping
|
||||
- **Query Support**: equals, anyOf, allOf, range queries
|
||||
- **Chunked Storage**: Supports massive datasets
|
||||
- **Auto-indexing**: Automatically maintains indexes on updates
|
||||
|
||||
## Performance Characteristics
|
||||
|
||||
### Operation Complexity
|
||||
- **Vector Search**: O(log n) via HNSW
|
||||
- **Field Filtering**: O(1) via inverted indexes
|
||||
- **Graph Traversal**: O(V + E) for breadth-first search
|
||||
- **Add Operation**: O(log n) for index insertion
|
||||
- **Update Operation**: O(1) for metadata updates
|
||||
|
||||
### Memory Usage
|
||||
- **Base Memory**: ~50MB for core system
|
||||
- **Per Vector**: ~1KB (384 dimensions × 4 bytes)
|
||||
- **Index Overhead**: ~20% of vector data
|
||||
- **Cache Size**: Configurable (default 1000 entries)
|
||||
|
||||
### Throughput
|
||||
- **Writes**: 1000+ ops/second (with batching)
|
||||
- **Reads**: 10,000+ ops/second
|
||||
- **Search**: 100+ queries/second (varies by complexity)
|
||||
|
||||
## Augmentation System
|
||||
|
||||
Brainy's extensible plugin architecture allows for powerful enhancements:
|
||||
|
||||
### Core Augmentations
|
||||
- **WAL (Write-Ahead Logging)**: Durability and crash recovery
|
||||
- **Entity Registry**: High-speed deduplication for streaming data
|
||||
- **Batch Processing**: Optimized bulk operations
|
||||
- **Connection Pool**: Efficient resource management
|
||||
- **Request Deduplicator**: Prevents duplicate processing
|
||||
|
||||
### Creating Custom Augmentations
|
||||
```typescript
|
||||
class CustomAugmentation extends BrainyAugmentation {
|
||||
async onInit(brain: BrainyData): Promise<void> {
|
||||
// Initialize augmentation
|
||||
}
|
||||
|
||||
async onAdd(item: any, brain: BrainyData): Promise<any> {
|
||||
// Process item before adding
|
||||
return item
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Caching Strategy
|
||||
|
||||
Multi-layered caching for optimal performance:
|
||||
- **Search Cache**: LRU cache for query results
|
||||
- **Metadata Cache**: Field index caching
|
||||
- **Pattern Cache**: NLP pattern matching cache
|
||||
- **Entity Cache**: In-memory entity registry
|
||||
|
||||
## Integration Points
|
||||
|
||||
### Key Objects for Extensions
|
||||
- `brain.index`: Access HNSW vector index
|
||||
- `brain.metadataIndex`: Access field indexing
|
||||
- `brain.storage`: Access storage layer
|
||||
- `brain.augmentations`: Access augmentation manager
|
||||
|
||||
### Event System
|
||||
```typescript
|
||||
brain.on('add', (item) => console.log('Item added:', item))
|
||||
brain.on('search', (query) => console.log('Search performed:', query))
|
||||
brain.on('error', (error) => console.error('Error:', error))
|
||||
```
|
||||
|
||||
## Best Practices
|
||||
|
||||
### When Adding Features
|
||||
1. Check if similar functionality exists
|
||||
2. Consider if it should be an augmentation
|
||||
3. Use existing indexes and caches
|
||||
4. Avoid duplicating functionality
|
||||
5. Follow the established patterns
|
||||
|
||||
### Performance Optimization
|
||||
1. Use batch operations for bulk data
|
||||
2. Enable appropriate caching
|
||||
3. Choose the right storage adapter
|
||||
4. Configure index parameters for your use case
|
||||
5. Monitor statistics for bottlenecks
|
||||
|
||||
## Next Steps
|
||||
|
||||
- [Storage Architecture](./storage-architecture.md) - Deep dive into storage system
|
||||
- [Triple Intelligence](./triple-intelligence.md) - Advanced query system
|
||||
- [API Reference](../api/README.md) - Complete API documentation
|
||||
312
docs/architecture/storage-architecture.md
Normal file
312
docs/architecture/storage-architecture.md
Normal file
|
|
@ -0,0 +1,312 @@
|
|||
# Storage Architecture
|
||||
|
||||
Brainy implements a sophisticated, unified storage system that works across all environments (Node.js, Browser, Edge Workers) with enterprise-grade features like metadata indexing, entity registry, and write-ahead logging.
|
||||
|
||||
## Storage Structure
|
||||
|
||||
```
|
||||
brainy-data/
|
||||
├── _system/ # System management
|
||||
│ └── statistics.json # Performance metrics and statistics
|
||||
├── nouns/ # Primary entity storage
|
||||
│ └── {uuid}.json # Individual entity documents
|
||||
├── metadata/ # Metadata and indexing system
|
||||
│ ├── {uuid}.json # Entity metadata
|
||||
│ ├── __entity_registry__.json # Entity deduplication registry
|
||||
│ ├── __metadata_field_index__field_{field}.json # Field discovery
|
||||
│ └── __metadata_index__{field}_{value}_chunk{n}.json # Value indexes
|
||||
├── verbs/ # Relationship/action storage
|
||||
│ └── {uuid}.json # Relationship documents
|
||||
├── wal/ # Write-Ahead Logging
|
||||
│ └── wal_{timestamp}_{id}.wal # Transaction logs
|
||||
└── locks/ # Concurrent access control
|
||||
└── {resource}.lock # Resource locks
|
||||
```
|
||||
|
||||
## Storage Adapters
|
||||
|
||||
Brainy provides multiple storage adapters with identical APIs:
|
||||
|
||||
### FileSystem Storage (Node.js)
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
storage: {
|
||||
type: 'filesystem',
|
||||
path: './data'
|
||||
}
|
||||
})
|
||||
```
|
||||
- **Use case**: Server applications, CLI tools
|
||||
- **Performance**: Direct file I/O
|
||||
- **Persistence**: Permanent on disk
|
||||
|
||||
### S3 Compatible Storage
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
storage: {
|
||||
type: 's3',
|
||||
bucket: 'my-brainy-data',
|
||||
region: 'us-east-1',
|
||||
credentials: {
|
||||
accessKeyId: process.env.AWS_ACCESS_KEY_ID,
|
||||
secretAccessKey: process.env.AWS_SECRET_ACCESS_KEY
|
||||
}
|
||||
}
|
||||
})
|
||||
```
|
||||
- **Use case**: Distributed applications, cloud deployments
|
||||
- **Performance**: Network dependent, with intelligent caching
|
||||
- **Persistence**: Cloud storage durability
|
||||
|
||||
### Origin Private File System (Browser)
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
storage: {
|
||||
type: 'opfs'
|
||||
}
|
||||
})
|
||||
```
|
||||
- **Use case**: Browser applications, PWAs
|
||||
- **Performance**: Near-native file system speed
|
||||
- **Persistence**: Permanent in browser (with quota limits)
|
||||
|
||||
### Memory Storage
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
storage: {
|
||||
type: 'memory'
|
||||
}
|
||||
})
|
||||
```
|
||||
- **Use case**: Testing, temporary processing
|
||||
- **Performance**: Fastest possible
|
||||
- **Persistence**: Volatile (lost on restart)
|
||||
|
||||
## Metadata Indexing System
|
||||
|
||||
### Field Discovery Index
|
||||
Tracks all unique values for each field:
|
||||
|
||||
```json
|
||||
// __metadata_field_index__field_category.json
|
||||
{
|
||||
"values": {
|
||||
"technology": 45,
|
||||
"science": 32,
|
||||
"business": 28
|
||||
},
|
||||
"lastUpdated": 1699564234567
|
||||
}
|
||||
```
|
||||
|
||||
### Value-Based Indexes
|
||||
Maps field+value combinations to entity IDs:
|
||||
|
||||
```json
|
||||
// __metadata_index__category_technology_chunk0.json
|
||||
{
|
||||
"field": "category",
|
||||
"value": "technology",
|
||||
"ids": ["uuid1", "uuid2", "uuid3", ...],
|
||||
"chunk": 0,
|
||||
"total": 45
|
||||
}
|
||||
```
|
||||
|
||||
### Index Chunking
|
||||
Large indexes automatically chunk for performance:
|
||||
- **Chunk size**: 10,000 IDs per chunk
|
||||
- **Auto-splitting**: Transparent to queries
|
||||
- **Parallel loading**: Chunks load on demand
|
||||
|
||||
## Entity Registry
|
||||
|
||||
High-performance deduplication system for streaming data:
|
||||
|
||||
### Registry Structure
|
||||
```json
|
||||
// __entity_registry__.json
|
||||
{
|
||||
"mappings": {
|
||||
"did:plc:alice123": "550e8400-e29b-41d4-a716-446655440000",
|
||||
"handle:alice.bsky.social": "550e8400-e29b-41d4-a716-446655440000"
|
||||
},
|
||||
"stats": {
|
||||
"totalMappings": 10000,
|
||||
"lastSync": 1699564234567
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Performance Characteristics
|
||||
- **Lookup**: O(1) in-memory hash map
|
||||
- **Persistence**: Configurable (memory/storage/hybrid)
|
||||
- **Cache**: LRU with configurable TTL
|
||||
- **Sync**: Periodic or on-demand
|
||||
|
||||
## Write-Ahead Logging (WAL)
|
||||
|
||||
Ensures durability and enables recovery:
|
||||
|
||||
### WAL Entry Format
|
||||
```json
|
||||
{
|
||||
"timestamp": 1699564234567,
|
||||
"operation": "add",
|
||||
"data": {
|
||||
"id": "550e8400-e29b-41d4-a716-446655440000",
|
||||
"content": "...",
|
||||
"metadata": {}
|
||||
},
|
||||
"checksum": "sha256:..."
|
||||
}
|
||||
```
|
||||
|
||||
### Recovery Process
|
||||
1. On startup, check for WAL files
|
||||
2. Replay operations from last checkpoint
|
||||
3. Verify checksums for integrity
|
||||
4. Clean up processed WAL files
|
||||
|
||||
## Storage Optimization
|
||||
|
||||
### Compression
|
||||
- **JSON**: Automatic minification
|
||||
- **Vectors**: Float32 to Uint8 quantization option
|
||||
- **Indexes**: Binary format for large datasets
|
||||
|
||||
### Caching Strategy
|
||||
```typescript
|
||||
// Configure caching per storage type
|
||||
const brain = new BrainyData({
|
||||
storage: {
|
||||
type: 'filesystem',
|
||||
cache: {
|
||||
enabled: true,
|
||||
maxSize: 1000, // Maximum cached items
|
||||
ttl: 300000, // 5 minutes
|
||||
strategy: 'lru' // Least recently used
|
||||
}
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
### Batch Operations
|
||||
```typescript
|
||||
// Batch writes for performance
|
||||
await brain.addBatch([
|
||||
{ content: "item1", metadata: {} },
|
||||
{ content: "item2", metadata: {} },
|
||||
{ content: "item3", metadata: {} }
|
||||
])
|
||||
// Single transaction, optimized I/O
|
||||
```
|
||||
|
||||
## Concurrent Access
|
||||
|
||||
### Locking Mechanism
|
||||
```typescript
|
||||
// Automatic locking for write operations
|
||||
await brain.storage.withLock('resource-id', async () => {
|
||||
// Exclusive access to resource
|
||||
await brain.storage.saveNoun(id, data)
|
||||
})
|
||||
```
|
||||
|
||||
### Read-Write Separation
|
||||
- **Reads**: Non-blocking, parallel
|
||||
- **Writes**: Serialized with locks
|
||||
- **Hybrid**: Read-heavy optimization
|
||||
|
||||
## Migration and Backup
|
||||
|
||||
### Export Data
|
||||
```typescript
|
||||
// Export entire database
|
||||
const backup = await brain.export({
|
||||
format: 'json',
|
||||
includeVectors: true,
|
||||
includeIndexes: false
|
||||
})
|
||||
```
|
||||
|
||||
### Import Data
|
||||
```typescript
|
||||
// Import from backup
|
||||
await brain.import(backup, {
|
||||
mode: 'merge', // or 'replace'
|
||||
validateSchema: true
|
||||
})
|
||||
```
|
||||
|
||||
### Storage Migration
|
||||
```typescript
|
||||
// Migrate between storage types
|
||||
const oldBrain = new BrainyData({ storage: { type: 'filesystem' } })
|
||||
const newBrain = new BrainyData({ storage: { type: 's3' } })
|
||||
|
||||
await oldBrain.init()
|
||||
await newBrain.init()
|
||||
|
||||
// Transfer all data
|
||||
const data = await oldBrain.export()
|
||||
await newBrain.import(data)
|
||||
```
|
||||
|
||||
## Performance Tuning
|
||||
|
||||
### Storage-Specific Optimizations
|
||||
|
||||
#### FileSystem
|
||||
- **Directory sharding**: Split files across subdirectories
|
||||
- **Async I/O**: Non-blocking file operations
|
||||
- **Buffer pooling**: Reuse buffers for efficiency
|
||||
|
||||
#### S3
|
||||
- **Multipart uploads**: For large objects
|
||||
- **Request batching**: Combine small operations
|
||||
- **CDN integration**: Edge caching for reads
|
||||
|
||||
#### OPFS
|
||||
- **Quota management**: Monitor and request increases
|
||||
- **Worker offloading**: Heavy operations in workers
|
||||
- **Transaction batching**: Group operations
|
||||
|
||||
### Monitoring
|
||||
|
||||
```typescript
|
||||
// Get storage statistics
|
||||
const stats = await brain.storage.getStatistics()
|
||||
console.log(stats)
|
||||
// {
|
||||
// totalSize: 1048576,
|
||||
// entityCount: 1000,
|
||||
// indexSize: 204800,
|
||||
// walSize: 10240,
|
||||
// cacheHitRate: 0.85
|
||||
// }
|
||||
```
|
||||
|
||||
## Best Practices
|
||||
|
||||
### Choose the Right Adapter
|
||||
1. **Development**: Memory or FileSystem
|
||||
2. **Production Server**: FileSystem or S3
|
||||
3. **Browser Apps**: OPFS or Memory
|
||||
4. **Distributed**: S3 with caching
|
||||
|
||||
### Optimize for Your Use Case
|
||||
1. **Read-heavy**: Enable aggressive caching
|
||||
2. **Write-heavy**: Use WAL and batching
|
||||
3. **Real-time**: Memory with periodic persistence
|
||||
4. **Archival**: S3 with compression
|
||||
|
||||
### Monitor and Maintain
|
||||
1. Regular statistics collection
|
||||
2. WAL cleanup scheduling
|
||||
3. Index optimization
|
||||
4. Cache tuning based on hit rates
|
||||
|
||||
## API Reference
|
||||
|
||||
See the [Storage API](../api/storage.md) for complete method documentation.
|
||||
355
docs/architecture/triple-intelligence.md
Normal file
355
docs/architecture/triple-intelligence.md
Normal file
|
|
@ -0,0 +1,355 @@
|
|||
# Triple Intelligence System
|
||||
|
||||
The Triple Intelligence System is Brainy's revolutionary query engine that unifies vector similarity, graph relationships, and field filtering into a single, optimized query interface.
|
||||
|
||||
## Overview
|
||||
|
||||
Traditional databases force you to choose between vector search, graph traversal, OR field filtering. Brainy combines all three intelligences into one magical API that automatically optimizes execution for maximum performance.
|
||||
|
||||
## Query Interface
|
||||
|
||||
### Unified Query Structure
|
||||
|
||||
```typescript
|
||||
interface TripleQuery {
|
||||
// Vector/Semantic search
|
||||
like?: string | Vector | any
|
||||
similar?: string | Vector | any
|
||||
|
||||
// Graph/Relationship search
|
||||
connected?: {
|
||||
to?: string | string[]
|
||||
from?: string | string[]
|
||||
type?: string | string[]
|
||||
depth?: number
|
||||
direction?: 'in' | 'out' | 'both'
|
||||
}
|
||||
|
||||
// Field/Attribute search
|
||||
where?: Record<string, any>
|
||||
|
||||
// Advanced options
|
||||
limit?: number
|
||||
boost?: 'recent' | 'popular' | 'verified' | string
|
||||
explain?: boolean
|
||||
threshold?: number
|
||||
}
|
||||
```
|
||||
|
||||
### Example Queries
|
||||
|
||||
#### Natural Language Queries with find()
|
||||
```typescript
|
||||
// Brainy understands natural language and extracts intent
|
||||
const results = await brain.find("research papers about neural networks from 2023")
|
||||
// Automatically interprets: document type, topic, time range
|
||||
|
||||
// Complex temporal and numeric queries
|
||||
const reports = await brain.find("quarterly reports from Q3 2024 with revenue over 10M")
|
||||
// Automatically extracts: report type, date range, numeric filters
|
||||
|
||||
// Multi-condition natural language
|
||||
const articles = await brain.find("verified articles by John Smith about machine learning published this year")
|
||||
// Automatically identifies: author, topic, verification status, time range
|
||||
```
|
||||
|
||||
#### Simple Vector Search
|
||||
```typescript
|
||||
const results = await brain.search("machine learning concepts")
|
||||
```
|
||||
|
||||
#### Combined Intelligence Query
|
||||
```typescript
|
||||
const results = await brain.find({
|
||||
like: "neural networks",
|
||||
where: {
|
||||
category: "research",
|
||||
year: { $gte: 2023 }
|
||||
},
|
||||
connected: {
|
||||
to: "deep-learning-team",
|
||||
depth: 2
|
||||
},
|
||||
limit: 20
|
||||
})
|
||||
```
|
||||
|
||||
## Query Optimization
|
||||
|
||||
### Automatic Plan Generation
|
||||
|
||||
The Triple Intelligence engine analyzes each query to create an optimal execution plan:
|
||||
|
||||
1. **Selectivity Analysis**: Identifies the most selective filters
|
||||
2. **Cost Estimation**: Estimates computational cost for each operation
|
||||
3. **Strategy Selection**: Chooses between parallel or progressive execution
|
||||
4. **Plan Caching**: Caches successful plans for similar queries
|
||||
|
||||
### Execution Strategies
|
||||
|
||||
#### Parallel Execution
|
||||
All three search types execute simultaneously:
|
||||
- **Best for**: Balanced queries with multiple signals
|
||||
- **Performance**: Maximum speed through parallelization
|
||||
- **Use case**: Complex queries needing all intelligence types
|
||||
|
||||
```typescript
|
||||
// Parallel execution for balanced query
|
||||
const results = await brain.find({
|
||||
like: "AI research", // ~1000 potential matches
|
||||
where: { type: "paper" }, // ~500 potential matches
|
||||
connected: { to: "stanford" } // ~200 potential matches
|
||||
})
|
||||
// All three execute in parallel, results fused
|
||||
```
|
||||
|
||||
#### Progressive Filtering
|
||||
Operations chain for maximum efficiency:
|
||||
- **Best for**: Queries with highly selective filters
|
||||
- **Performance**: Reduces search space at each step
|
||||
- **Use case**: Large datasets with specific criteria
|
||||
|
||||
```typescript
|
||||
// Progressive execution for selective query
|
||||
const results = await brain.find({
|
||||
where: { userId: "user123" }, // Very selective (1-10 matches)
|
||||
like: "recent posts", // Applied to filtered set
|
||||
limit: 5
|
||||
})
|
||||
// Field filter first, then vector search on results
|
||||
```
|
||||
|
||||
## Fusion Ranking
|
||||
|
||||
### Score Combination
|
||||
|
||||
When multiple intelligence types return results, scores are intelligently combined:
|
||||
|
||||
```typescript
|
||||
fusionScore = (
|
||||
vectorScore * vectorWeight + // Semantic relevance (0.4)
|
||||
graphScore * graphWeight + // Relationship strength (0.3)
|
||||
fieldScore * fieldWeight // Exact match confidence (0.3)
|
||||
) / totalWeight
|
||||
```
|
||||
|
||||
### Adaptive Weights
|
||||
|
||||
Weights adjust based on query characteristics:
|
||||
- **Text-heavy query**: Higher vector weight
|
||||
- **Relationship query**: Higher graph weight
|
||||
- **Specific filters**: Higher field weight
|
||||
|
||||
## Natural Language Processing
|
||||
|
||||
### Pattern Recognition
|
||||
|
||||
Brainy includes 220+ embedded patterns for natural language understanding:
|
||||
|
||||
```typescript
|
||||
// Natural language automatically parsed
|
||||
const results = await brain.search(
|
||||
"show me recent AI papers from Stanford published this year"
|
||||
)
|
||||
// Automatically converts to:
|
||||
// {
|
||||
// like: "AI papers",
|
||||
// where: {
|
||||
// institution: "Stanford",
|
||||
// published: { $gte: "2024-01-01" }
|
||||
// }
|
||||
// }
|
||||
```
|
||||
|
||||
### Intent Detection
|
||||
|
||||
The NLP processor identifies query intent:
|
||||
- **Informational**: "what is", "how does"
|
||||
- **Navigational**: "find", "show me"
|
||||
- **Transactional**: "create", "update"
|
||||
- **Analytical**: "compare", "analyze"
|
||||
|
||||
## Performance Optimization
|
||||
|
||||
### Query Plan Caching
|
||||
|
||||
Successful execution plans are cached:
|
||||
```typescript
|
||||
// First query: 50ms (plan generation + execution)
|
||||
await brain.search("machine learning papers")
|
||||
|
||||
// Subsequent similar queries: 10ms (cached plan)
|
||||
await brain.search("deep learning papers")
|
||||
```
|
||||
|
||||
### Self-Optimization
|
||||
|
||||
Brainy uses itself to optimize queries:
|
||||
- Query patterns stored in separate brain instance
|
||||
- Execution times tracked and analyzed
|
||||
- Plans automatically improved based on performance
|
||||
|
||||
### Index Utilization
|
||||
|
||||
Triple Intelligence leverages all available indexes:
|
||||
- **HNSW Index**: For vector similarity
|
||||
- **Metadata Index**: For field filtering
|
||||
- **Graph Index**: For relationship traversal
|
||||
|
||||
## Advanced Features
|
||||
|
||||
### Explain Mode
|
||||
|
||||
Understand how your query was executed:
|
||||
|
||||
```typescript
|
||||
const results = await brain.find({
|
||||
like: "quantum computing",
|
||||
where: { category: "research" },
|
||||
explain: true
|
||||
})
|
||||
|
||||
console.log(results[0].explanation)
|
||||
// {
|
||||
// plan: "field-first-progressive",
|
||||
// timing: {
|
||||
// fieldFilter: 2,
|
||||
// vectorSearch: 8,
|
||||
// fusion: 1
|
||||
// },
|
||||
// selectivity: {
|
||||
// field: 0.1,
|
||||
// vector: 0.3
|
||||
// }
|
||||
// }
|
||||
```
|
||||
|
||||
### Boosting
|
||||
|
||||
Apply custom ranking boosts:
|
||||
|
||||
```typescript
|
||||
const results = await brain.find({
|
||||
like: "news articles",
|
||||
boost: 'recent', // Boost recent items
|
||||
where: { verified: true }
|
||||
})
|
||||
```
|
||||
|
||||
### Threshold Control
|
||||
|
||||
Set minimum similarity thresholds:
|
||||
|
||||
```typescript
|
||||
const results = await brain.find({
|
||||
like: "exact match needed",
|
||||
threshold: 0.9, // Only very similar results
|
||||
limit: 10
|
||||
})
|
||||
```
|
||||
|
||||
## Best Practices
|
||||
|
||||
### Query Design
|
||||
|
||||
1. **Start specific**: Use selective filters when possible
|
||||
2. **Combine intelligently**: Don't force all three types if not needed
|
||||
3. **Use limits**: Always specify reasonable result limits
|
||||
4. **Cache results**: For repeated queries, cache at application level
|
||||
|
||||
### Performance Tips
|
||||
|
||||
1. **Index first**: Ensure fields used in `where` clauses are indexed
|
||||
2. **Batch operations**: Use batch methods for bulk queries
|
||||
3. **Monitor plans**: Use explain mode to understand performance
|
||||
4. **Optimize patterns**: Train custom patterns for your domain
|
||||
|
||||
### Common Patterns
|
||||
|
||||
#### Semantic Search with Filtering
|
||||
```typescript
|
||||
// Find similar content with constraints
|
||||
const results = await brain.find({
|
||||
like: query,
|
||||
where: {
|
||||
status: 'published',
|
||||
language: 'en'
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
#### Related Items Discovery
|
||||
```typescript
|
||||
// Find items related to a specific item
|
||||
const results = await brain.find({
|
||||
connected: {
|
||||
to: itemId,
|
||||
depth: 2,
|
||||
type: 'similar'
|
||||
},
|
||||
limit: 20
|
||||
})
|
||||
```
|
||||
|
||||
#### Time-based Queries
|
||||
```typescript
|
||||
// Recent items matching criteria
|
||||
const results = await brain.find({
|
||||
where: {
|
||||
timestamp: { $gte: Date.now() - 86400000 }
|
||||
},
|
||||
like: "trending topics",
|
||||
boost: 'recent'
|
||||
})
|
||||
```
|
||||
|
||||
## Natural Language Processing
|
||||
|
||||
The `find()` method includes advanced NLP capabilities powered by 220+ embedded patterns that understand natural language queries.
|
||||
|
||||
### Supported Query Types
|
||||
|
||||
```typescript
|
||||
// Temporal queries
|
||||
await brain.find("documents from last week")
|
||||
await brain.find("reports created yesterday")
|
||||
await brain.find("articles published in Q3 2024")
|
||||
await brain.find("data from January to March")
|
||||
|
||||
// Numeric filters
|
||||
await brain.find("products with price under $100")
|
||||
await brain.find("articles with more than 1000 views")
|
||||
await brain.find("reports showing revenue over 10M")
|
||||
|
||||
// Combined conditions
|
||||
await brain.find("verified research papers about AI from 2024 with high citations")
|
||||
await brain.find("recent customer reviews with rating above 4 stars")
|
||||
await brain.find("blog posts by John Smith about machine learning published this month")
|
||||
|
||||
// Relationship queries
|
||||
await brain.find("documents related to project X")
|
||||
await brain.find("people who work at TechCorp")
|
||||
await brain.find("products similar to iPhone")
|
||||
```
|
||||
|
||||
### How It Works
|
||||
|
||||
1. **Intent Detection**: Identifies what the user is looking for
|
||||
2. **Entity Extraction**: Extracts names, dates, numbers, categories
|
||||
3. **Temporal Parsing**: Converts "last week", "Q3 2024" to date ranges
|
||||
4. **Filter Generation**: Creates appropriate where clauses
|
||||
5. **Query Fusion**: Combines NLP understanding with vector search
|
||||
|
||||
### Pattern Coverage
|
||||
|
||||
Brainy includes 220+ pre-computed patterns covering:
|
||||
- **Temporal**: 40+ patterns for dates and time ranges
|
||||
- **Numeric**: 30+ patterns for comparisons and ranges
|
||||
- **Relationships**: 25+ patterns for connections
|
||||
- **Actions**: 35+ patterns for verbs and intents
|
||||
- **Entities**: 40+ patterns for people, places, things
|
||||
- **Domain-specific**: 50+ patterns for tech, business, social
|
||||
|
||||
## API Reference
|
||||
|
||||
See the [Triple Intelligence API](../api/triple-intelligence.md) for complete method documentation.
|
||||
769
docs/architecture/zero-config.md
Normal file
769
docs/architecture/zero-config.md
Normal file
|
|
@ -0,0 +1,769 @@
|
|||
# Zero Configuration & Auto-Adaptation
|
||||
|
||||
> **Current Status**: Basic zero-config is fully functional. Advanced auto-adaptation features are in development.
|
||||
|
||||
## Overview
|
||||
|
||||
Brainy is designed with **"Zero Config by Default, Infinite Tunability"** philosophy. It automatically detects your environment, adapts to available resources, learns from usage patterns, and optimizes itself for your specific workload—all without any configuration.
|
||||
|
||||
## Zero Configuration Magic
|
||||
|
||||
### Instant Start
|
||||
|
||||
```typescript
|
||||
import { BrainyData } from 'brainy'
|
||||
|
||||
// That's it. No config needed.
|
||||
const brain = new BrainyData()
|
||||
await brain.init()
|
||||
|
||||
// Brainy automatically:
|
||||
// ✓ Detects environment (Node.js, Browser, Edge, Deno)
|
||||
// ✓ Chooses optimal storage (FileSystem, OPFS, Memory)
|
||||
// ✓ Downloads required models (if needed)
|
||||
// ✓ Configures vector dimensions (384 optimal)
|
||||
// ✓ Sets up indexing strategies
|
||||
// ✓ Enables appropriate augmentations
|
||||
// ✓ Configures caching layers
|
||||
// ✓ Optimizes for your hardware
|
||||
```
|
||||
|
||||
### Environment Detection ✅ Available
|
||||
|
||||
Brainy automatically detects and adapts to your runtime:
|
||||
|
||||
```typescript
|
||||
// Brainy's environment detection
|
||||
const environment = {
|
||||
// Runtime detection
|
||||
isNode: typeof process !== 'undefined',
|
||||
isBrowser: typeof window !== 'undefined',
|
||||
isDeno: typeof Deno !== 'undefined',
|
||||
isEdge: typeof EdgeRuntime !== 'undefined',
|
||||
isWebWorker: typeof WorkerGlobalScope !== 'undefined',
|
||||
|
||||
// Capability detection
|
||||
hasFileSystem: /* auto-detected */,
|
||||
hasIndexedDB: /* auto-detected */,
|
||||
hasOPFS: /* auto-detected */,
|
||||
hasWebGPU: /* auto-detected */,
|
||||
hasWASM: /* auto-detected */,
|
||||
|
||||
// Resource detection
|
||||
cpuCores: /* auto-detected */,
|
||||
memory: /* auto-detected */,
|
||||
storage: /* auto-detected */
|
||||
}
|
||||
```
|
||||
|
||||
## Auto-Adaptive Storage ✅ Available
|
||||
|
||||
> **Current**: Brainy automatically selects the best storage adapter for your environment.
|
||||
|
||||
### Storage Selection Logic
|
||||
|
||||
```typescript
|
||||
// Brainy's intelligent storage selection
|
||||
async function autoSelectStorage() {
|
||||
// Server environments
|
||||
if (environment.isNode) {
|
||||
if (await hasWritePermission('./data')) {
|
||||
return 'filesystem' // Best for servers
|
||||
} else if (process.env.S3_BUCKET) {
|
||||
return 's3' // Cloud deployment
|
||||
} else {
|
||||
return 'memory' // Fallback for restricted environments
|
||||
}
|
||||
}
|
||||
|
||||
// Browser environments
|
||||
if (environment.isBrowser) {
|
||||
if (await navigator.storage.estimate() > 1GB) {
|
||||
return 'opfs' // Best for modern browsers
|
||||
} else if (indexedDB) {
|
||||
return 'indexeddb' // Fallback for older browsers
|
||||
} else {
|
||||
return 'memory' // In-memory for restricted contexts
|
||||
}
|
||||
}
|
||||
|
||||
// Edge environments
|
||||
if (environment.isEdge) {
|
||||
return 'kv' // Use edge KV stores (Cloudflare, Vercel)
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Storage Migration
|
||||
|
||||
Brainy seamlessly migrates between storage types:
|
||||
|
||||
```typescript
|
||||
// Start with memory storage (development)
|
||||
const brain = new BrainyData() // Auto-selects memory
|
||||
|
||||
// Later, migrate to production storage
|
||||
await brain.migrate({
|
||||
to: 'filesystem',
|
||||
path: './production-data'
|
||||
})
|
||||
// All data seamlessly transferred
|
||||
```
|
||||
|
||||
## Learning & Optimization 🚧 Coming Soon
|
||||
|
||||
> **Note**: These features are planned for Q2 2025. Currently, Brainy uses static optimizations.
|
||||
|
||||
### Query Pattern Learning 🚧 Planned
|
||||
|
||||
Brainy learns from your query patterns and optimizes accordingly:
|
||||
|
||||
```typescript
|
||||
// Brainy observes query patterns
|
||||
class QueryPatternLearner {
|
||||
analyze(queries: Query[]) {
|
||||
return {
|
||||
// Frequency analysis
|
||||
mostCommonFields: this.getTopFields(queries),
|
||||
avgResultSize: this.getAvgSize(queries),
|
||||
temporalPatterns: this.getTimePatterns(queries),
|
||||
|
||||
// Relationship analysis
|
||||
commonTraversals: this.getGraphPatterns(queries),
|
||||
typicalDepth: this.getAvgDepth(queries),
|
||||
|
||||
// Performance analysis
|
||||
slowQueries: this.getSlowQueries(queries),
|
||||
cacheability: this.getCacheability(queries)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Automatic optimizations based on learning:
|
||||
// - Creates indexes for frequently queried fields
|
||||
// - Pre-computes common graph traversals
|
||||
// - Adjusts cache sizes based on working set
|
||||
// - Optimizes vector search parameters
|
||||
```
|
||||
|
||||
### Auto-Indexing 🚧 Planned
|
||||
|
||||
Brainy automatically creates indexes based on usage:
|
||||
|
||||
```typescript
|
||||
// No manual index configuration needed
|
||||
await brain.find({ where: { category: "tech" } }) // First query
|
||||
// Brainy notices 'category' field usage
|
||||
|
||||
await brain.find({ where: { category: "science" } }) // Second query
|
||||
// Pattern detected - auto-creates category index
|
||||
|
||||
await brain.find({ where: { category: "tech" } }) // Third query
|
||||
// Now using index - 100x faster!
|
||||
```
|
||||
|
||||
### Adaptive Caching 🚧 Planned
|
||||
|
||||
Cache strategies adapt to your access patterns:
|
||||
|
||||
```typescript
|
||||
class AdaptiveCache {
|
||||
async adapt(metrics: AccessMetrics) {
|
||||
if (metrics.hitRate < 0.3) {
|
||||
// Low hit rate - switch strategy
|
||||
this.strategy = 'lfu' // Least Frequently Used
|
||||
} else if (metrics.workingSet > this.size) {
|
||||
// Working set too large - increase size
|
||||
this.size = Math.min(metrics.workingSet * 1.5, maxMemory)
|
||||
} else if (metrics.temporalLocality > 0.8) {
|
||||
// High temporal locality - use time-based eviction
|
||||
this.strategy = 'ttl'
|
||||
this.ttl = metrics.avgAccessInterval * 2
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Performance Auto-Scaling 🚧 Coming Soon
|
||||
|
||||
### Dynamic Batch Sizing
|
||||
|
||||
Brainy adjusts batch sizes based on system load:
|
||||
|
||||
```typescript
|
||||
class DynamicBatcher {
|
||||
calculateOptimalBatch() {
|
||||
const cpuUsage = process.cpuUsage()
|
||||
const memoryUsage = process.memoryUsage()
|
||||
|
||||
if (cpuUsage < 30 && memoryUsage < 50) {
|
||||
return 1000 // System idle - large batches
|
||||
} else if (cpuUsage < 60 && memoryUsage < 70) {
|
||||
return 100 // Moderate load - medium batches
|
||||
} else {
|
||||
return 10 // High load - small batches
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Automatically applied during bulk operations
|
||||
for (const item of millionItems) {
|
||||
await brain.addNoun(item) // Internally batched optimally
|
||||
}
|
||||
```
|
||||
|
||||
### Memory Management
|
||||
|
||||
Automatic memory pressure handling:
|
||||
|
||||
```typescript
|
||||
class MemoryManager {
|
||||
async handlePressure() {
|
||||
const usage = process.memoryUsage()
|
||||
const available = os.freemem()
|
||||
|
||||
if (available < 100 * 1024 * 1024) { // Less than 100MB free
|
||||
// Emergency mode
|
||||
await this.flushCaches()
|
||||
await this.compactIndexes()
|
||||
await this.offloadToDisk()
|
||||
} else if (usage.heapUsed / usage.heapTotal > 0.9) {
|
||||
// Preventive mode
|
||||
await this.reduceCacheSizes()
|
||||
await this.pauseBackgroundTasks()
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Connection Pooling
|
||||
|
||||
Automatic connection management for storage backends:
|
||||
|
||||
```typescript
|
||||
class ConnectionPool {
|
||||
async getOptimalPoolSize() {
|
||||
// Adapts based on workload
|
||||
const metrics = await this.getMetrics()
|
||||
|
||||
if (metrics.waitTime > 100) {
|
||||
// Queries waiting - increase pool
|
||||
this.size = Math.min(this.size * 1.5, this.maxSize)
|
||||
} else if (metrics.idleConnections > this.size * 0.5) {
|
||||
// Too many idle - decrease pool
|
||||
this.size = Math.max(this.size * 0.7, this.minSize)
|
||||
}
|
||||
|
||||
return this.size
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Model Auto-Selection
|
||||
|
||||
### Embedding Model Selection
|
||||
|
||||
Brainy chooses the best embedding model for your use case:
|
||||
|
||||
```typescript
|
||||
async function autoSelectModel(data: Sample[]) {
|
||||
const analysis = {
|
||||
languages: detectLanguages(data),
|
||||
domainSpecific: detectDomain(data),
|
||||
averageLength: getAvgLength(data),
|
||||
requiresMultilingual: languages.length > 1
|
||||
}
|
||||
|
||||
if (analysis.requiresMultilingual) {
|
||||
return 'multilingual-e5-base' // Handles 100+ languages
|
||||
} else if (analysis.domainSpecific === 'code') {
|
||||
return 'codebert-base' // Optimized for code
|
||||
} else if (analysis.averageLength > 512) {
|
||||
return 'all-mpnet-base-v2' // Better for long text
|
||||
} else {
|
||||
return 'all-MiniLM-L6-v2' // Fast and efficient default
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Model Downloading
|
||||
|
||||
Models are automatically downloaded when needed:
|
||||
|
||||
```typescript
|
||||
// First use - model auto-downloads
|
||||
const brain = new BrainyData()
|
||||
await brain.init() // Downloads model if not cached
|
||||
|
||||
// Intelligent model caching
|
||||
const modelCache = {
|
||||
location: process.env.MODEL_CACHE || '~/.brainy/models',
|
||||
maxSize: 5 * 1024 * 1024 * 1024, // 5GB max
|
||||
strategy: 'lru', // Least recently used eviction
|
||||
|
||||
// CDN selection based on location
|
||||
cdn: await selectFastestCDN([
|
||||
'https://cdn.brainy.io',
|
||||
'https://brainy.b-cdn.net',
|
||||
'https://models.huggingface.co'
|
||||
])
|
||||
}
|
||||
```
|
||||
|
||||
## Workload Detection
|
||||
|
||||
### Pattern Recognition
|
||||
|
||||
Brainy identifies your workload type and optimizes:
|
||||
|
||||
```typescript
|
||||
enum WorkloadType {
|
||||
OLTP = 'oltp', // Many small transactions
|
||||
OLAP = 'olap', // Analytical queries
|
||||
STREAMING = 'streaming', // Real-time ingestion
|
||||
BATCH = 'batch', // Bulk processing
|
||||
HYBRID = 'hybrid' // Mixed workload
|
||||
}
|
||||
|
||||
class WorkloadDetector {
|
||||
detect(metrics: OperationMetrics): WorkloadType {
|
||||
if (metrics.writesPerSecond > 1000 && metrics.avgWriteSize < 1024) {
|
||||
return WorkloadType.STREAMING
|
||||
} else if (metrics.avgQueryComplexity > 0.8 && metrics.avgResultSize > 10000) {
|
||||
return WorkloadType.OLAP
|
||||
} else if (metrics.batchOperations > metrics.singleOperations) {
|
||||
return WorkloadType.BATCH
|
||||
} else if (metrics.writeReadRatio > 0.3 && metrics.writeReadRatio < 0.7) {
|
||||
return WorkloadType.HYBRID
|
||||
} else {
|
||||
return WorkloadType.OLTP
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Optimization Strategies
|
||||
|
||||
Different optimizations for different workloads:
|
||||
|
||||
```typescript
|
||||
class WorkloadOptimizer {
|
||||
optimize(workload: WorkloadType) {
|
||||
switch (workload) {
|
||||
case WorkloadType.STREAMING:
|
||||
return {
|
||||
entityRegistry: true, // Deduplication
|
||||
batchSize: 1000,
|
||||
walEnabled: true,
|
||||
cacheSize: 'small',
|
||||
indexStrategy: 'lazy'
|
||||
}
|
||||
|
||||
case WorkloadType.OLAP:
|
||||
return {
|
||||
entityRegistry: false,
|
||||
batchSize: 10000,
|
||||
walEnabled: false,
|
||||
cacheSize: 'large',
|
||||
indexStrategy: 'eager',
|
||||
parallelQueries: true
|
||||
}
|
||||
|
||||
case WorkloadType.BATCH:
|
||||
return {
|
||||
entityRegistry: false,
|
||||
batchSize: 50000,
|
||||
walEnabled: false,
|
||||
cacheSize: 'minimal',
|
||||
indexStrategy: 'deferred'
|
||||
}
|
||||
|
||||
default:
|
||||
return this.defaultConfig
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Hardware Adaptation 🚧 Coming Soon
|
||||
|
||||
> **Note**: GPU acceleration and hardware optimization planned for Q3 2025.
|
||||
|
||||
### CPU Optimization
|
||||
|
||||
Adapts to available CPU resources:
|
||||
|
||||
```typescript
|
||||
class CPUAdapter {
|
||||
async optimize() {
|
||||
const cores = os.cpus().length
|
||||
const type = os.cpus()[0].model
|
||||
|
||||
// Parallel processing based on cores
|
||||
this.parallelism = Math.max(1, cores - 1) // Leave one core free
|
||||
|
||||
// SIMD detection for vector operations
|
||||
if (type.includes('Intel') || type.includes('AMD')) {
|
||||
this.enableSIMD = await checkSIMDSupport()
|
||||
}
|
||||
|
||||
// Thread pool sizing
|
||||
this.threadPoolSize = cores * 2 // Optimal for I/O bound
|
||||
|
||||
// Vector search optimization
|
||||
if (cores >= 8) {
|
||||
this.hnswConstruction = 200 // Higher quality index
|
||||
this.hnswSearch = 100 // More accurate search
|
||||
} else {
|
||||
this.hnswConstruction = 100 // Balanced
|
||||
this.hnswSearch = 50 // Faster search
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Memory Adaptation
|
||||
|
||||
Intelligent memory allocation:
|
||||
|
||||
```typescript
|
||||
class MemoryAdapter {
|
||||
async configure() {
|
||||
const totalMemory = os.totalmem()
|
||||
const availableMemory = os.freemem()
|
||||
|
||||
// Allocate based on available memory
|
||||
const allocation = {
|
||||
cache: Math.min(availableMemory * 0.25, 2 * GB),
|
||||
vectors: Math.min(availableMemory * 0.30, 4 * GB),
|
||||
indexes: Math.min(availableMemory * 0.20, 2 * GB),
|
||||
working: Math.min(availableMemory * 0.25, 2 * GB)
|
||||
}
|
||||
|
||||
// Adjust for low memory systems
|
||||
if (totalMemory < 4 * GB) {
|
||||
allocation.cache *= 0.5
|
||||
allocation.vectors *= 0.7
|
||||
this.enableSwapping = true
|
||||
}
|
||||
|
||||
return allocation
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### GPU Acceleration
|
||||
|
||||
Automatic GPU detection and utilization:
|
||||
|
||||
```typescript
|
||||
class GPUAdapter {
|
||||
async detect() {
|
||||
// WebGPU in browsers
|
||||
if (navigator?.gpu) {
|
||||
const adapter = await navigator.gpu.requestAdapter()
|
||||
return {
|
||||
available: true,
|
||||
type: 'webgpu',
|
||||
memory: adapter.limits.maxBufferSize,
|
||||
compute: adapter.limits.maxComputeWorkgroupsPerDimension
|
||||
}
|
||||
}
|
||||
|
||||
// CUDA in Node.js
|
||||
if (process.platform === 'linux' || process.platform === 'win32') {
|
||||
const hasCuda = await checkCudaSupport()
|
||||
if (hasCuda) {
|
||||
return {
|
||||
available: true,
|
||||
type: 'cuda',
|
||||
memory: await getCudaMemory(),
|
||||
compute: await getCudaCores()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return { available: false }
|
||||
}
|
||||
|
||||
async optimize(gpu: GPUInfo) {
|
||||
if (gpu.available) {
|
||||
// Offload vector operations to GPU
|
||||
this.vectorOps = 'gpu'
|
||||
this.embeddingGeneration = 'gpu'
|
||||
this.matrixMultiplication = 'gpu'
|
||||
|
||||
// Larger batch sizes for GPU
|
||||
this.batchSize = gpu.memory > 8 * GB ? 10000 : 1000
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Network Adaptation
|
||||
|
||||
### Bandwidth Detection
|
||||
|
||||
Optimizes for available network bandwidth:
|
||||
|
||||
```typescript
|
||||
class NetworkAdapter {
|
||||
async measureBandwidth() {
|
||||
const testSize = 1 * MB
|
||||
const start = Date.now()
|
||||
await this.transfer(testSize)
|
||||
const duration = Date.now() - start
|
||||
|
||||
const bandwidth = (testSize / duration) * 1000 // bytes/sec
|
||||
|
||||
if (bandwidth < 1 * MB) {
|
||||
// Low bandwidth - optimize
|
||||
this.compression = 'aggressive'
|
||||
this.batchTransfers = true
|
||||
this.cacheRemote = true
|
||||
} else if (bandwidth > 100 * MB) {
|
||||
// High bandwidth
|
||||
this.compression = 'minimal'
|
||||
this.parallelTransfers = true
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Latency Optimization
|
||||
|
||||
Adapts to network latency:
|
||||
|
||||
```typescript
|
||||
class LatencyOptimizer {
|
||||
async optimize() {
|
||||
const latency = await this.measureLatency()
|
||||
|
||||
if (latency > 100) { // High latency
|
||||
// Batch operations
|
||||
this.minBatchSize = 100
|
||||
|
||||
// Aggressive prefetching
|
||||
this.prefetchDepth = 3
|
||||
|
||||
// Local caching
|
||||
this.cacheStrategy = 'aggressive'
|
||||
|
||||
// Connection pooling
|
||||
this.connectionPool = Math.min(latency / 10, 50)
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Cloud Provider Detection 🚧 Coming Soon
|
||||
|
||||
> **Note**: Cloud provider auto-detection planned for Q3 2025.
|
||||
|
||||
### Automatic Cloud Optimization
|
||||
|
||||
Detects and optimizes for cloud providers:
|
||||
|
||||
```typescript
|
||||
class CloudDetector {
|
||||
async detect() {
|
||||
// AWS Detection
|
||||
if (process.env.AWS_REGION || await canReachMetadata('169.254.169.254')) {
|
||||
return {
|
||||
provider: 'aws',
|
||||
instance: await getEC2InstanceType(),
|
||||
region: process.env.AWS_REGION,
|
||||
services: {
|
||||
storage: 's3',
|
||||
cache: 'elasticache',
|
||||
compute: 'lambda'
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Google Cloud Detection
|
||||
if (process.env.GOOGLE_CLOUD_PROJECT || await canReachMetadata('metadata.google.internal')) {
|
||||
return {
|
||||
provider: 'gcp',
|
||||
instance: await getGCEInstanceType(),
|
||||
region: process.env.GOOGLE_CLOUD_REGION,
|
||||
services: {
|
||||
storage: 'gcs',
|
||||
cache: 'memorystore',
|
||||
compute: 'cloud-run'
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Vercel Edge Detection
|
||||
if (process.env.VERCEL) {
|
||||
return {
|
||||
provider: 'vercel',
|
||||
region: process.env.VERCEL_REGION,
|
||||
services: {
|
||||
storage: 'vercel-kv',
|
||||
cache: 'edge-config',
|
||||
compute: 'edge-runtime'
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Development vs Production
|
||||
|
||||
### Automatic Environment Detection
|
||||
|
||||
```typescript
|
||||
class EnvironmentDetector {
|
||||
detect() {
|
||||
const indicators = {
|
||||
// Development indicators
|
||||
isDevelopment:
|
||||
process.env.NODE_ENV === 'development' ||
|
||||
process.env.DEBUG ||
|
||||
process.argv.includes('--dev') ||
|
||||
isLocalhost() ||
|
||||
hasDevTools(),
|
||||
|
||||
// Test indicators
|
||||
isTest:
|
||||
process.env.NODE_ENV === 'test' ||
|
||||
process.env.CI ||
|
||||
isTestRunner(),
|
||||
|
||||
// Production indicators
|
||||
isProduction:
|
||||
process.env.NODE_ENV === 'production' ||
|
||||
process.env.VERCEL ||
|
||||
process.env.NETLIFY ||
|
||||
!isLocalhost()
|
||||
}
|
||||
|
||||
return indicators
|
||||
}
|
||||
}
|
||||
|
||||
// Different defaults for different environments
|
||||
const config = environment.isProduction ? {
|
||||
storage: 'filesystem',
|
||||
wal: true,
|
||||
monitoring: true,
|
||||
compression: true,
|
||||
caching: 'aggressive'
|
||||
} : {
|
||||
storage: 'memory',
|
||||
wal: false,
|
||||
monitoring: false,
|
||||
compression: false,
|
||||
caching: 'minimal'
|
||||
}
|
||||
```
|
||||
|
||||
## Error Recovery
|
||||
|
||||
### Automatic Fallbacks
|
||||
|
||||
Brainy automatically recovers from errors:
|
||||
|
||||
```typescript
|
||||
class AutoRecovery {
|
||||
async handleStorageFailure() {
|
||||
try {
|
||||
await this.primaryStorage.write(data)
|
||||
} catch (error) {
|
||||
console.warn('Primary storage failed, trying fallback')
|
||||
|
||||
// Try secondary storage
|
||||
if (this.secondaryStorage) {
|
||||
await this.secondaryStorage.write(data)
|
||||
} else {
|
||||
// Fall back to memory
|
||||
await this.memoryStorage.write(data)
|
||||
|
||||
// Schedule retry
|
||||
this.scheduleRetry(data)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
async handleModelFailure() {
|
||||
try {
|
||||
return await this.primaryModel.embed(text)
|
||||
} catch (error) {
|
||||
// Fall back to simpler model
|
||||
return await this.fallbackModel.embed(text)
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Configuration Override
|
||||
|
||||
While zero-config is default, you can override when needed:
|
||||
|
||||
```typescript
|
||||
// Explicit configuration when needed
|
||||
const brain = new BrainyData({
|
||||
// Override auto-detection
|
||||
storage: {
|
||||
type: 'filesystem',
|
||||
path: '/custom/path'
|
||||
},
|
||||
|
||||
// Override auto-optimization
|
||||
optimization: {
|
||||
autoIndex: false,
|
||||
autoCache: false,
|
||||
autoBatch: false
|
||||
},
|
||||
|
||||
// Override auto-scaling
|
||||
scaling: {
|
||||
maxMemory: 2 * GB,
|
||||
maxConnections: 100,
|
||||
maxBatchSize: 1000
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
## Monitoring Auto-Adaptation
|
||||
|
||||
Brainy provides visibility into its auto-adaptation:
|
||||
|
||||
```typescript
|
||||
brain.on('adaptation', (event) => {
|
||||
console.log(`Brainy adapted: ${event.type}`)
|
||||
console.log(`Reason: ${event.reason}`)
|
||||
console.log(`Before: ${JSON.stringify(event.before)}`)
|
||||
console.log(`After: ${JSON.stringify(event.after)}`)
|
||||
})
|
||||
|
||||
// Example events:
|
||||
// - Index created for frequently queried field
|
||||
// - Cache strategy changed due to low hit rate
|
||||
// - Batch size increased due to high throughput
|
||||
// - Storage migrated due to space constraints
|
||||
// - Model switched due to multilingual content
|
||||
```
|
||||
|
||||
## Conclusion
|
||||
|
||||
Brainy's zero-configuration and auto-adaptation capabilities mean you can focus on your application logic while Brainy handles:
|
||||
|
||||
- Environment detection and optimization
|
||||
- Storage selection and migration
|
||||
- Performance tuning and scaling
|
||||
- Resource management
|
||||
- Error recovery
|
||||
- Workload optimization
|
||||
|
||||
Just create a Brainy instance and start using it. Brainy will learn, adapt, and optimize itself for your specific use case—no configuration required.
|
||||
|
||||
## See Also
|
||||
|
||||
- [Architecture Overview](./overview.md)
|
||||
- [Storage Architecture](./storage.md)
|
||||
- [Performance Guide](../guides/performance.md)
|
||||
- [Augmentations System](./augmentations.md)
|
||||
206
docs/augmentations/README.md
Normal file
206
docs/augmentations/README.md
Normal file
|
|
@ -0,0 +1,206 @@
|
|||
# Brainy Augmentations
|
||||
|
||||
Augmentations are the core extensibility mechanism in Brainy. They allow you to modify, enhance, and extend Brainy's behavior without changing the core code.
|
||||
|
||||
## Core Principle: One Interface, Infinite Possibilities
|
||||
|
||||
Every augmentation implements the same simple `BrainyAugmentation` interface:
|
||||
|
||||
```typescript
|
||||
interface BrainyAugmentation {
|
||||
name: string
|
||||
timing: 'before' | 'after' | 'around' | 'replace'
|
||||
operations: string[]
|
||||
priority: number
|
||||
initialize(context): Promise<void>
|
||||
execute(operation, params, next): Promise<any>
|
||||
}
|
||||
```
|
||||
|
||||
This single interface can handle EVERYTHING - from adding AI capabilities to exposing APIs to replacing storage backends.
|
||||
|
||||
## Available Augmentations
|
||||
|
||||
### 🧠 Data Processing
|
||||
Augmentations that enhance how data is processed and stored.
|
||||
|
||||
| Augmentation | Description | Timing | Status |
|
||||
|-------------|-------------|--------|--------|
|
||||
| **NeuralImportAugmentation** | AI-powered entity and relationship extraction | `before` | ✅ Production |
|
||||
| **EntityRegistryAugmentation** | High-performance entity deduplication | `before` | ✅ Production |
|
||||
| **BatchProcessingAugmentation** | Optimizes bulk operations | `around` | ✅ Production |
|
||||
| **IntelligentVerbScoringAugmentation** | Learns relationship importance over time | `after` | ✅ Production |
|
||||
|
||||
### 🔌 External Connections (Synapses)
|
||||
Connect Brainy to external services and data sources.
|
||||
|
||||
| Augmentation | Description | Timing | Status |
|
||||
|-------------|-------------|--------|--------|
|
||||
| **NotionSynapse** | Sync with Notion databases | `after` | 📝 Example |
|
||||
| **SalesforceSynapse** | Connect to Salesforce CRM | `after` | 📝 Example |
|
||||
| **SlackSynapse** | Import Slack conversations | `after` | 📝 Example |
|
||||
| **GoogleDriveSynapse** | Sync Google Drive documents | `after` | 📝 Example |
|
||||
|
||||
### 🌐 API Exposure
|
||||
Expose Brainy through various protocols.
|
||||
|
||||
| Augmentation | Description | Timing | Status |
|
||||
|-------------|-------------|--------|--------|
|
||||
| **APIServerAugmentation** | REST, WebSocket, and MCP server | `after` | ✅ Production |
|
||||
| **GraphQLAugmentation** | GraphQL API endpoint | `after` | 🚧 Planned |
|
||||
| **gRPCAugmentation** | gRPC service | `after` | 🚧 Planned |
|
||||
|
||||
### 💾 Storage Backends
|
||||
Replace or enhance the storage layer.
|
||||
|
||||
| Augmentation | Description | Timing | Status |
|
||||
|-------------|-------------|--------|--------|
|
||||
| **WALAugmentation** | Write-ahead logging for durability | `around` | ✅ Production |
|
||||
| **S3StorageAugmentation** | Use S3 as storage backend | `replace` | 📝 Example |
|
||||
| **RedisAugmentation** | Redis caching layer | `around` | 📝 Example |
|
||||
| **PostgresAugmentation** | PostgreSQL persistence | `replace` | 📝 Example |
|
||||
|
||||
### 🔄 Real-time & Sync
|
||||
Handle real-time updates and synchronization.
|
||||
|
||||
| Augmentation | Description | Timing | Status |
|
||||
|-------------|-------------|--------|--------|
|
||||
| **WebSocketConduitAugmentation** | WebSocket client connections | `after` | ⚠️ Legacy |
|
||||
| **ServerSearchAugmentation** | Connect to remote Brainy servers | `after` | ⚠️ Legacy |
|
||||
| **TeamCoordinationAugmentation** | Multi-agent synchronization | `after` | 📝 Example |
|
||||
|
||||
### 🛡️ Infrastructure
|
||||
Core infrastructure and reliability features.
|
||||
|
||||
| Augmentation | Description | Timing | Status |
|
||||
|-------------|-------------|--------|--------|
|
||||
| **ConnectionPoolAugmentation** | Optimize cloud storage connections | `before` | ✅ Production |
|
||||
| **RequestDeduplicatorAugmentation** | Prevent duplicate concurrent requests | `before` | ✅ Production |
|
||||
| **TransactionAugmentation** | ACID transaction support | `around` | 🚧 Planned |
|
||||
| **CacheAugmentation** | Multi-level caching | `around` | ✅ Production |
|
||||
|
||||
### 📊 Monitoring & Analytics
|
||||
Track and analyze Brainy's behavior.
|
||||
|
||||
| Augmentation | Description | Timing | Status |
|
||||
|-------------|-------------|--------|--------|
|
||||
| **MetricsAugmentation** | Prometheus metrics | `after` | 📝 Example |
|
||||
| **LoggingAugmentation** | Structured logging | `after` | 📝 Example |
|
||||
| **TracingAugmentation** | Distributed tracing | `around` | 🚧 Planned |
|
||||
|
||||
### 🤖 AI & Chat
|
||||
AI-powered interfaces and chat capabilities.
|
||||
|
||||
| Augmentation | Description | Timing | Status |
|
||||
|-------------|-------------|--------|--------|
|
||||
| **ChatInterfaceAugmentation** | Natural language interface | `before` | 📝 Example |
|
||||
| **MCPAgentMemoryAugmentation** | AI agent memory via MCP | `after` | 📝 Example |
|
||||
| **LLMQueryAugmentation** | LLM-enhanced queries | `before` | 📝 Example |
|
||||
|
||||
### 📈 Visualization
|
||||
Visual representations of data.
|
||||
|
||||
| Augmentation | Description | Timing | Status |
|
||||
|-------------|-------------|--------|--------|
|
||||
| **GraphVisualizationAugmentation** | Real-time graph visualization | `after` | 📝 Example |
|
||||
| **DashboardAugmentation** | Web-based dashboard | `after` | 🚧 Planned |
|
||||
|
||||
## Status Legend
|
||||
|
||||
- ✅ **Production**: Fully implemented and tested
|
||||
- 📝 **Example**: Example implementation available
|
||||
- 🚧 **Planned**: On the roadmap
|
||||
- ⚠️ **Legacy**: Being replaced by newer augmentations
|
||||
|
||||
## Using Augmentations
|
||||
|
||||
### Zero-Config Approach
|
||||
|
||||
```typescript
|
||||
const brain = new BrainyData()
|
||||
|
||||
// Just register augmentations - they work automatically!
|
||||
brain.augmentations.register(new WALAugmentation())
|
||||
brain.augmentations.register(new EntityRegistryAugmentation())
|
||||
brain.augmentations.register(new APIServerAugmentation())
|
||||
|
||||
await brain.init()
|
||||
```
|
||||
|
||||
### With Configuration
|
||||
|
||||
```typescript
|
||||
const brain = new BrainyData()
|
||||
|
||||
brain.augmentations.register(
|
||||
new APIServerAugmentation({
|
||||
port: 8080,
|
||||
auth: { required: true }
|
||||
})
|
||||
)
|
||||
|
||||
await brain.init()
|
||||
```
|
||||
|
||||
## Creating Custom Augmentations
|
||||
|
||||
See [Creating Custom Augmentations](./creating-augmentations.md) for a complete guide.
|
||||
|
||||
Quick example:
|
||||
|
||||
```typescript
|
||||
class MyAugmentation extends BaseAugmentation {
|
||||
readonly name = 'my-augmentation'
|
||||
readonly timing = 'after'
|
||||
readonly operations = ['add', 'search']
|
||||
readonly priority = 50
|
||||
|
||||
async execute<T>(operation: string, params: any, next: () => Promise<T>): Promise<T> {
|
||||
console.log(`Before ${operation}`)
|
||||
const result = await next()
|
||||
console.log(`After ${operation}`)
|
||||
return result
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Augmentation Timing
|
||||
|
||||
### `before`
|
||||
Executes before the main operation. Used for:
|
||||
- Input validation
|
||||
- Data transformation
|
||||
- Authentication checks
|
||||
|
||||
### `after`
|
||||
Executes after the main operation. Used for:
|
||||
- Broadcasting updates
|
||||
- Syncing to external services
|
||||
- Logging and metrics
|
||||
|
||||
### `around`
|
||||
Wraps the main operation. Used for:
|
||||
- Transactions
|
||||
- Caching
|
||||
- Error handling
|
||||
|
||||
### `replace`
|
||||
Completely replaces the main operation. Used for:
|
||||
- Alternative storage backends
|
||||
- Mock implementations
|
||||
- Proxy operations
|
||||
|
||||
## Priority System
|
||||
|
||||
Higher numbers execute first:
|
||||
- **100**: Critical system operations
|
||||
- **50**: Performance optimizations
|
||||
- **10**: Enhancement features
|
||||
- **1**: Optional features
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- [API Server Augmentation](./api-server.md) - Complete API server documentation
|
||||
- [Creating Augmentations](./creating-augmentations.md) - How to build your own
|
||||
- [Augmentation Examples](../AUGMENTATION-EXAMPLES.md) - Real-world examples
|
||||
- [Architecture Overview](../COMPLETE-ARCHITECTURE-VISION.md) - System architecture
|
||||
404
docs/augmentations/api-server.md
Normal file
404
docs/augmentations/api-server.md
Normal file
|
|
@ -0,0 +1,404 @@
|
|||
# API Server Augmentation
|
||||
|
||||
## Overview
|
||||
|
||||
The `APIServerAugmentation` is a powerful augmentation that exposes your Brainy instance through REST, WebSocket, and MCP (Model Context Protocol) APIs. It transforms Brainy into a full-featured API server with zero configuration required.
|
||||
|
||||
## Features
|
||||
|
||||
### 🌐 REST API
|
||||
Complete CRUD operations and advanced queries through HTTP endpoints.
|
||||
|
||||
### 🔌 WebSocket Server
|
||||
Real-time bidirectional communication with automatic operation broadcasting.
|
||||
|
||||
### 🧠 MCP Integration
|
||||
Built-in Model Context Protocol support for AI agent communication.
|
||||
|
||||
### 📊 Operation Broadcasting
|
||||
Automatically broadcasts all Brainy operations to subscribed WebSocket clients.
|
||||
|
||||
### 🔒 Optional Security
|
||||
Built-in authentication and rate limiting when needed.
|
||||
|
||||
## Installation
|
||||
|
||||
The APIServerAugmentation is included in Brainy core. No additional installation required.
|
||||
|
||||
For Node.js environments, you may want to install optional dependencies:
|
||||
```bash
|
||||
npm install express cors ws
|
||||
```
|
||||
|
||||
## Zero-Config Usage
|
||||
|
||||
```typescript
|
||||
import { BrainyData } from 'brainy'
|
||||
import { APIServerAugmentation } from 'brainy/augmentations'
|
||||
|
||||
const brain = new BrainyData()
|
||||
|
||||
// Register the API server augmentation
|
||||
brain.augmentations.register(new APIServerAugmentation())
|
||||
|
||||
await brain.init()
|
||||
|
||||
// Server is now running at http://localhost:3000
|
||||
console.log('API Server ready!')
|
||||
console.log('REST: http://localhost:3000/api/*')
|
||||
console.log('WebSocket: ws://localhost:3000/ws')
|
||||
console.log('MCP: http://localhost:3000/api/mcp')
|
||||
```
|
||||
|
||||
## Configuration Options
|
||||
|
||||
While zero-config works great, you can customize the server:
|
||||
|
||||
```typescript
|
||||
const apiServer = new APIServerAugmentation({
|
||||
enabled: true, // Enable/disable the server
|
||||
port: 3000, // HTTP port
|
||||
host: '0.0.0.0', // Bind address
|
||||
|
||||
cors: {
|
||||
origin: '*', // CORS allowed origins
|
||||
credentials: true // Allow credentials
|
||||
},
|
||||
|
||||
auth: {
|
||||
required: false, // Require authentication
|
||||
apiKeys: [], // Valid API keys
|
||||
bearerTokens: [] // Valid bearer tokens
|
||||
},
|
||||
|
||||
rateLimit: {
|
||||
windowMs: 60000, // Rate limit window (ms)
|
||||
max: 100 // Max requests per window
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
## REST API Endpoints
|
||||
|
||||
### Health Check
|
||||
```http
|
||||
GET /health
|
||||
```
|
||||
Returns server status and basic metrics.
|
||||
|
||||
### Search
|
||||
```http
|
||||
POST /api/search
|
||||
Content-Type: application/json
|
||||
|
||||
{
|
||||
"query": "search text",
|
||||
"limit": 10,
|
||||
"options": {}
|
||||
}
|
||||
```
|
||||
|
||||
### Add Data
|
||||
```http
|
||||
POST /api/add
|
||||
Content-Type: application/json
|
||||
|
||||
{
|
||||
"content": "data to add",
|
||||
"metadata": {
|
||||
"key": "value"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Get by ID
|
||||
```http
|
||||
GET /api/get/:id
|
||||
```
|
||||
|
||||
### Delete
|
||||
```http
|
||||
DELETE /api/delete/:id
|
||||
```
|
||||
|
||||
### Create Relationship
|
||||
```http
|
||||
POST /api/relate
|
||||
Content-Type: application/json
|
||||
|
||||
{
|
||||
"source": "id1",
|
||||
"target": "id2",
|
||||
"verb": "relates_to",
|
||||
"metadata": {}
|
||||
}
|
||||
```
|
||||
|
||||
### Complex Queries
|
||||
```http
|
||||
POST /api/find
|
||||
Content-Type: application/json
|
||||
|
||||
{
|
||||
"where": { "type": "document" },
|
||||
"like": "machine learning",
|
||||
"limit": 10
|
||||
}
|
||||
```
|
||||
|
||||
### Clustering
|
||||
```http
|
||||
POST /api/cluster
|
||||
Content-Type: application/json
|
||||
|
||||
{
|
||||
"algorithm": "kmeans",
|
||||
"options": {
|
||||
"k": 5
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Statistics
|
||||
```http
|
||||
GET /api/stats
|
||||
```
|
||||
|
||||
### Operation History
|
||||
```http
|
||||
GET /api/history
|
||||
```
|
||||
|
||||
## WebSocket API
|
||||
|
||||
### Connection
|
||||
```javascript
|
||||
const ws = new WebSocket('ws://localhost:3000/ws')
|
||||
|
||||
ws.onopen = () => {
|
||||
console.log('Connected to Brainy WebSocket')
|
||||
}
|
||||
|
||||
ws.onmessage = (event) => {
|
||||
const msg = JSON.parse(event.data)
|
||||
console.log('Received:', msg)
|
||||
}
|
||||
```
|
||||
|
||||
### Subscribe to Operations
|
||||
```javascript
|
||||
ws.send(JSON.stringify({
|
||||
type: 'subscribe',
|
||||
operations: ['all'] // or specific: ['add', 'search', 'delete']
|
||||
}))
|
||||
```
|
||||
|
||||
### Search via WebSocket
|
||||
```javascript
|
||||
ws.send(JSON.stringify({
|
||||
type: 'search',
|
||||
query: 'your search',
|
||||
limit: 10,
|
||||
requestId: 'unique-id'
|
||||
}))
|
||||
```
|
||||
|
||||
### Add Data via WebSocket
|
||||
```javascript
|
||||
ws.send(JSON.stringify({
|
||||
type: 'add',
|
||||
content: 'data to add',
|
||||
metadata: {},
|
||||
requestId: 'unique-id'
|
||||
}))
|
||||
```
|
||||
|
||||
### Operation Broadcasts
|
||||
When subscribed, you'll receive real-time updates:
|
||||
```javascript
|
||||
{
|
||||
"type": "operation",
|
||||
"operation": "add",
|
||||
"params": { /* sanitized parameters */ },
|
||||
"timestamp": 1234567890,
|
||||
"duration": 15
|
||||
}
|
||||
```
|
||||
|
||||
## MCP (Model Context Protocol)
|
||||
|
||||
The MCP endpoint allows AI agents to interact with Brainy:
|
||||
|
||||
```http
|
||||
POST /api/mcp
|
||||
Content-Type: application/json
|
||||
|
||||
{
|
||||
"method": "search",
|
||||
"params": {
|
||||
"query": "find documents about AI"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Authentication
|
||||
|
||||
When authentication is enabled:
|
||||
|
||||
### API Key
|
||||
```http
|
||||
GET /api/stats
|
||||
X-API-Key: your-api-key
|
||||
```
|
||||
|
||||
### Bearer Token
|
||||
```http
|
||||
GET /api/stats
|
||||
Authorization: Bearer your-token
|
||||
```
|
||||
|
||||
## Environment Support
|
||||
|
||||
### Node.js ✅
|
||||
Full support with Express, WebSocket, and all features.
|
||||
|
||||
### Deno 🚧
|
||||
Planned support using Deno.serve() or oak framework.
|
||||
|
||||
### Browser/Service Worker 🚧
|
||||
Planned support for intercepting fetch() calls locally.
|
||||
|
||||
## How It Works
|
||||
|
||||
The APIServerAugmentation hooks into Brainy's augmentation pipeline:
|
||||
|
||||
1. **Timing**: Executes `after` operations complete
|
||||
2. **Operations**: Monitors `all` operations
|
||||
3. **Broadcasting**: Sends operation details to subscribed clients
|
||||
4. **History**: Maintains operation history (last 1000 operations)
|
||||
|
||||
## Example: Multi-Client Sync
|
||||
|
||||
```typescript
|
||||
// Server
|
||||
const brain = new BrainyData()
|
||||
brain.augmentations.register(new APIServerAugmentation())
|
||||
await brain.init()
|
||||
|
||||
// Client 1 - WebSocket subscriber
|
||||
const ws1 = new WebSocket('ws://localhost:3000/ws')
|
||||
ws1.onopen = () => {
|
||||
ws1.send(JSON.stringify({
|
||||
type: 'subscribe',
|
||||
operations: ['add', 'delete']
|
||||
}))
|
||||
}
|
||||
ws1.onmessage = (e) => {
|
||||
console.log('Client 1 received update:', JSON.parse(e.data))
|
||||
}
|
||||
|
||||
// Client 2 - REST API user
|
||||
fetch('http://localhost:3000/api/add', {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({
|
||||
content: 'New data',
|
||||
metadata: { source: 'client2' }
|
||||
})
|
||||
})
|
||||
// Client 1 automatically receives notification!
|
||||
```
|
||||
|
||||
## Performance Considerations
|
||||
|
||||
- **Operation History**: Limited to last 1000 operations
|
||||
- **WebSocket Heartbeat**: Every 30 seconds
|
||||
- **Client Timeout**: 60 seconds of inactivity
|
||||
- **Parameter Sanitization**: Sensitive fields removed, large content truncated
|
||||
- **Rate Limiting**: In-memory tracking (use Redis in production)
|
||||
|
||||
## Security Notes
|
||||
|
||||
1. **Default Configuration**: No auth, open CORS - suitable for development
|
||||
2. **Production**: Enable auth, configure CORS, use HTTPS
|
||||
3. **Sensitive Data**: Parameters are sanitized before broadcasting
|
||||
4. **Rate Limiting**: Basic in-memory implementation included
|
||||
|
||||
## Comparison with Previous Implementations
|
||||
|
||||
The APIServerAugmentation unifies and replaces:
|
||||
- `BrainyMCPBroadcast` - Node-specific WebSocket/HTTP server
|
||||
- `WebSocketConduitAugmentation` - WebSocket client functionality
|
||||
- `ServerSearchAugmentations` - Remote Brainy connections
|
||||
|
||||
Benefits of the unified approach:
|
||||
- Single augmentation for all API needs
|
||||
- Consistent interface across protocols
|
||||
- Automatic operation broadcasting
|
||||
- Environment-aware implementation
|
||||
- Zero-configuration philosophy
|
||||
|
||||
## Advanced Usage
|
||||
|
||||
### Custom Operation Filtering
|
||||
|
||||
```typescript
|
||||
class FilteredAPIServer extends APIServerAugmentation {
|
||||
shouldExecute(operation: string, params: any): boolean {
|
||||
// Don't broadcast sensitive operations
|
||||
if (operation === 'delete' && params.sensitive) {
|
||||
return false
|
||||
}
|
||||
return true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Integration with Other Augmentations
|
||||
|
||||
```typescript
|
||||
const brain = new BrainyData()
|
||||
|
||||
// Stack augmentations for complete system
|
||||
brain.augmentations.register(new WALAugmentation()) // Durability
|
||||
brain.augmentations.register(new EntityRegistryAugmentation()) // Dedup
|
||||
brain.augmentations.register(new APIServerAugmentation()) // API
|
||||
|
||||
await brain.init()
|
||||
// All augmentations work together seamlessly!
|
||||
```
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Server won't start
|
||||
- Check if port is already in use
|
||||
- Verify Node.js dependencies are installed: `npm install express cors ws`
|
||||
- Check console for error messages
|
||||
|
||||
### WebSocket connections drop
|
||||
- Ensure heartbeat responses are handled
|
||||
- Check for proxy/firewall issues
|
||||
- Verify CORS configuration
|
||||
|
||||
### Authentication not working
|
||||
- Ensure `auth.required` is set to `true`
|
||||
- Verify API keys or bearer tokens are correctly configured
|
||||
- Check request headers are properly formatted
|
||||
|
||||
## Future Enhancements
|
||||
|
||||
- [ ] Deno server implementation
|
||||
- [ ] Service Worker implementation
|
||||
- [ ] GraphQL endpoint
|
||||
- [ ] gRPC support
|
||||
- [ ] Built-in SSL/TLS
|
||||
- [ ] Redis-based rate limiting
|
||||
- [ ] Prometheus metrics endpoint
|
||||
- [ ] OpenAPI/Swagger documentation
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- [Augmentation System Overview](../AUGMENTATION-SYSTEM.md)
|
||||
- [BrainyAugmentation Interface](./brainy-augmentation.md)
|
||||
- [MCP Integration](../mcp/README.md)
|
||||
- [Zero-Config Philosophy](../ZERO-CONFIG.md)
|
||||
415
docs/features/complete-feature-list.md
Normal file
415
docs/features/complete-feature-list.md
Normal file
|
|
@ -0,0 +1,415 @@
|
|||
# 🚀 Brainy 2.0 - Complete Feature List
|
||||
|
||||
> **The Truth**: Brainy is MORE powerful than previously documented! This is the complete list of ALL implemented features.
|
||||
|
||||
## 🧠 Core Intelligence Engine
|
||||
|
||||
### Triple Intelligence System ✅
|
||||
Unified query system that automatically combines:
|
||||
- **Vector Search**: HNSW-indexed semantic similarity (O(log n) performance)
|
||||
- **Graph Traversal**: Relationship-based discovery
|
||||
- **Field Filtering**: Metadata and attribute queries
|
||||
- **Auto-optimization**: Queries are automatically optimized based on data patterns
|
||||
|
||||
```typescript
|
||||
// All three intelligences work together automatically
|
||||
const results = await brain.find({
|
||||
like: 'AI research', // Vector search
|
||||
where: { year: 2024 }, // Field filtering
|
||||
connected: { to: authorId } // Graph traversal
|
||||
})
|
||||
```
|
||||
|
||||
### Neural Query Understanding ✅
|
||||
- **220+ embedded patterns** for query intent detection
|
||||
- Natural language query processing
|
||||
- Automatic query type detection
|
||||
- Query rewriting and optimization
|
||||
|
||||
## 🔧 12+ Production Augmentations
|
||||
|
||||
### 1. WAL (Write-Ahead Logging) ✅
|
||||
```typescript
|
||||
import { WALAugmentation } from 'brainy'
|
||||
// Full crash recovery, checkpointing, replay
|
||||
```
|
||||
|
||||
### 2. Entity Registry ✅
|
||||
```typescript
|
||||
import { EntityRegistryAugmentation } from 'brainy'
|
||||
// Bloom filter-based deduplication for streaming data
|
||||
// Handles millions of entities with minimal memory
|
||||
```
|
||||
|
||||
### 3. Auto-Register Entities ✅
|
||||
```typescript
|
||||
import { AutoRegisterEntitiesAugmentation } from 'brainy'
|
||||
// Automatically extracts and registers entities from text
|
||||
```
|
||||
|
||||
### 4. Intelligent Verb Scoring ✅
|
||||
```typescript
|
||||
import { IntelligentVerbScoringAugmentation } from 'brainy'
|
||||
// Multi-factor relationship strength:
|
||||
// - Semantic similarity
|
||||
// - Temporal decay
|
||||
// - Frequency amplification
|
||||
// - Context awareness
|
||||
```
|
||||
|
||||
### 5. Batch Processing ✅
|
||||
```typescript
|
||||
import { BatchProcessingAugmentation } from 'brainy'
|
||||
// Adaptive batching with backpressure
|
||||
// Dynamically adjusts batch size based on load
|
||||
```
|
||||
|
||||
### 6. Connection Pool ✅
|
||||
```typescript
|
||||
import { ConnectionPoolAugmentation } from 'brainy'
|
||||
// Auto-scaling connection management
|
||||
// Optimized for distributed operations
|
||||
```
|
||||
|
||||
### 7. Request Deduplicator ✅
|
||||
```typescript
|
||||
import { RequestDeduplicatorAugmentation } from 'brainy'
|
||||
// In-flight request deduplication
|
||||
// 3x performance boost for concurrent operations
|
||||
```
|
||||
|
||||
### 8. WebSocket Conduit ✅
|
||||
```typescript
|
||||
import { WebSocketConduitAugmentation } from 'brainy'
|
||||
// Real-time bidirectional streaming
|
||||
// Auto-reconnection and heartbeat
|
||||
```
|
||||
|
||||
### 9. WebRTC Conduit ✅
|
||||
```typescript
|
||||
import { WebRTCConduitAugmentation } from 'brainy'
|
||||
// Peer-to-peer data channels
|
||||
// Direct browser-to-browser communication
|
||||
```
|
||||
|
||||
### 10. Memory Storage Optimization ✅
|
||||
```typescript
|
||||
import { MemoryStorageAugmentation } from 'brainy'
|
||||
// Memory-specific optimizations
|
||||
// Circular buffers, compression
|
||||
```
|
||||
|
||||
### 11. Server Search Conduit ✅
|
||||
```typescript
|
||||
import { ServerSearchConduitAugmentation } from 'brainy'
|
||||
// Distributed query execution
|
||||
// Load balancing across nodes
|
||||
```
|
||||
|
||||
### 12. Neural Import ✅
|
||||
```typescript
|
||||
import { NeuralImportAugmentation } from 'brainy'
|
||||
// AI-powered data understanding
|
||||
// Automatic entity detection and classification
|
||||
// Relationship discovery
|
||||
```
|
||||
|
||||
## 🤖 Neural Import Capabilities (FULLY IMPLEMENTED!)
|
||||
|
||||
```typescript
|
||||
const neuralImport = new NeuralImport(brain)
|
||||
|
||||
// ALL of these work TODAY:
|
||||
await neuralImport.neuralImport('data.csv')
|
||||
await neuralImport.detectEntitiesWithNeuralAnalysis(data)
|
||||
await neuralImport.detectNounType(entity)
|
||||
await neuralImport.detectRelationships(entities)
|
||||
await neuralImport.generateInsights(data)
|
||||
```
|
||||
|
||||
### Features:
|
||||
- **Auto-detects file format** (CSV, JSON, XML, etc.)
|
||||
- **Identifies entity types** using AI
|
||||
- **Discovers relationships** between entities
|
||||
- **Generates insights** about the data
|
||||
- **Creates optimal graph structure** automatically
|
||||
|
||||
## 🎯 Zero-Config Model Loading Cascade
|
||||
|
||||
Brainy automatically loads models with ZERO configuration required:
|
||||
|
||||
```typescript
|
||||
const brain = new BrainyData() // That's it!
|
||||
await brain.init()
|
||||
// Models load automatically from best available source
|
||||
```
|
||||
|
||||
### Loading Priority:
|
||||
1. **Local Cache** (./models) - Instant, no network
|
||||
2. **CDN** (models.soulcraft.com) - Fast, global [Coming Soon]
|
||||
3. **GitHub Releases** - Reliable backup
|
||||
4. **HuggingFace** - Ultimate fallback
|
||||
|
||||
### Key Features:
|
||||
- **Automatic fallback** if sources fail
|
||||
- **Model verification** with checksums
|
||||
- **Offline support** with bundled models
|
||||
- **No environment variables needed**
|
||||
- **Works in all environments** (Node, Browser, Workers)
|
||||
|
||||
## 🏢 Distributed Operation Modes
|
||||
|
||||
### Reader Mode ✅
|
||||
```typescript
|
||||
const brain = new BrainyData({ mode: 'reader' })
|
||||
// Optimized for read-heavy workloads
|
||||
// 80% cache ratio, aggressive prefetch
|
||||
// 1 hour TTL, minimal writes
|
||||
```
|
||||
|
||||
### Writer Mode ✅
|
||||
```typescript
|
||||
const brain = new BrainyData({ mode: 'writer' })
|
||||
// Optimized for write-heavy workloads
|
||||
// Large write buffers, batch writes
|
||||
// Minimal caching, fast ingestion
|
||||
```
|
||||
|
||||
### Hybrid Mode ✅
|
||||
```typescript
|
||||
const brain = new BrainyData({ mode: 'hybrid' })
|
||||
// Balanced for mixed workloads
|
||||
// Adaptive caching and batching
|
||||
```
|
||||
|
||||
## 💾 Advanced Caching System
|
||||
|
||||
### 3-Level Cache Architecture ✅
|
||||
```typescript
|
||||
const cacheConfig = {
|
||||
hotCache: {
|
||||
size: 1000, // L1 - RAM
|
||||
ttl: 60000 // 1 minute
|
||||
},
|
||||
warmCache: {
|
||||
size: 10000, // L2 - Fast storage
|
||||
ttl: 300000 // 5 minutes
|
||||
},
|
||||
coldCache: {
|
||||
size: 100000, // L3 - Persistent
|
||||
ttl: null // No expiry
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Cache Features:
|
||||
- **Automatic promotion/demotion** between levels
|
||||
- **LRU eviction** within each level
|
||||
- **Compression** for cold cache
|
||||
- **Statistics tracking** for optimization
|
||||
|
||||
## 📊 Comprehensive Statistics
|
||||
|
||||
```typescript
|
||||
const stats = await brain.getStatistics()
|
||||
// Returns detailed metrics:
|
||||
{
|
||||
nouns: {
|
||||
count, created, updated, deleted,
|
||||
size, avgSize
|
||||
},
|
||||
verbs: {
|
||||
count, created, types,
|
||||
weights: { min, max, avg }
|
||||
},
|
||||
vectors: {
|
||||
dimensions: 384,
|
||||
indexSize, partitions,
|
||||
avgSearchTime
|
||||
},
|
||||
cache: {
|
||||
hits, misses, evictions,
|
||||
hitRate, sizes
|
||||
},
|
||||
performance: {
|
||||
operations, avgTimes,
|
||||
p95Latency, p99Latency
|
||||
},
|
||||
storage: {
|
||||
used, available,
|
||||
compression, files
|
||||
},
|
||||
throttling: {
|
||||
delays, rateLimited,
|
||||
backoffMs, retries
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## 🚀 GPU Acceleration Support
|
||||
|
||||
```typescript
|
||||
// Automatic GPU detection
|
||||
const device = await detectBestDevice()
|
||||
// Returns: 'cpu' | 'webgpu' | 'cuda'
|
||||
|
||||
// WebGPU in browser (when available)
|
||||
if (device === 'webgpu') {
|
||||
// Transformer models use WebGPU automatically
|
||||
}
|
||||
|
||||
// CUDA in Node.js (requires ONNX Runtime GPU)
|
||||
if (device === 'cuda') {
|
||||
// Automatically uses GPU for embeddings
|
||||
}
|
||||
```
|
||||
|
||||
## 🔄 Adaptive Systems
|
||||
|
||||
### Adaptive Backpressure ✅
|
||||
```typescript
|
||||
// Automatically adjusts flow based on system load
|
||||
// Prevents OOM and maintains throughput
|
||||
```
|
||||
|
||||
### Adaptive Socket Manager ✅
|
||||
```typescript
|
||||
// Dynamic connection pooling
|
||||
// Scales connections based on traffic patterns
|
||||
```
|
||||
|
||||
### Cache Auto-Configuration ✅
|
||||
```typescript
|
||||
// Sizes cache based on available memory
|
||||
// Adjusts strategies based on usage patterns
|
||||
```
|
||||
|
||||
### S3 Throttling Protection ✅
|
||||
```typescript
|
||||
// Built-in exponential backoff
|
||||
// Rate limit detection and adaptation
|
||||
// Automatic retry with jitter
|
||||
```
|
||||
|
||||
## 🛠️ Storage Adapters
|
||||
|
||||
All included, auto-selected based on environment:
|
||||
|
||||
### FileSystem Storage ✅
|
||||
- Default for Node.js
|
||||
- Efficient file-based storage
|
||||
- Automatic directory management
|
||||
|
||||
### Memory Storage ✅
|
||||
- Ultra-fast in-memory operations
|
||||
- Perfect for testing and temporary data
|
||||
- Circular buffer support
|
||||
|
||||
### OPFS Storage ✅
|
||||
- Browser persistent storage
|
||||
- Survives page refreshes
|
||||
- Quota management
|
||||
|
||||
### S3 Storage ✅
|
||||
- AWS S3 compatible
|
||||
- Automatic multipart uploads
|
||||
- Throttling protection
|
||||
- Batch operations
|
||||
|
||||
## 🎨 Natural Language Processing
|
||||
|
||||
### Built-in Patterns (220+)
|
||||
- Question types (what, why, how, when, where)
|
||||
- Temporal queries (yesterday, last week, 2024)
|
||||
- Comparative queries (better than, similar to)
|
||||
- Aggregations (count, sum, average)
|
||||
- Filters (only, except, without)
|
||||
- Relationships (related to, connected with)
|
||||
|
||||
### Coverage: 94-98% of typical queries!
|
||||
|
||||
## 🔐 Security Features
|
||||
|
||||
### Built-in Security ✅
|
||||
- Automatic input sanitization
|
||||
- SQL injection prevention
|
||||
- XSS protection for web contexts
|
||||
- Rate limiting support
|
||||
|
||||
### Encryption Ready ✅
|
||||
```typescript
|
||||
import { crypto } from 'brainy/utils'
|
||||
// AES-256-GCM encryption utilities
|
||||
// Key derivation functions
|
||||
// Secure random generation
|
||||
```
|
||||
|
||||
## 🎯 Key Design Principles
|
||||
|
||||
### 1. Zero Configuration
|
||||
```typescript
|
||||
const brain = new BrainyData()
|
||||
await brain.init()
|
||||
// Everything else is automatic!
|
||||
```
|
||||
|
||||
### 2. Fixed Dimensions (384)
|
||||
- **ALWAYS** uses all-MiniLM-L6-v2 model
|
||||
- **ALWAYS** 384 dimensions
|
||||
- **NOT** configurable (by design)
|
||||
- Ensures everything works together
|
||||
|
||||
### 3. Progressive Enhancement
|
||||
- Starts simple, scales automatically
|
||||
- Adapts to workload patterns
|
||||
- Optimizes based on usage
|
||||
|
||||
### 4. Universal Compatibility
|
||||
- Works in Node.js 18+
|
||||
- Works in modern browsers
|
||||
- Works in Web Workers
|
||||
- Works in Edge environments
|
||||
|
||||
## 📦 What Ships in Core (MIT Licensed)
|
||||
|
||||
**EVERYTHING** is included in the core package:
|
||||
- ✅ All engines (vector, graph, field, neural)
|
||||
- ✅ All augmentations (12+)
|
||||
- ✅ All storage adapters
|
||||
- ✅ All distributed modes
|
||||
- ✅ Complete statistics
|
||||
- ✅ GPU support
|
||||
- ✅ No feature limitations
|
||||
- ✅ No premium tiers
|
||||
- ✅ 100% MIT licensed
|
||||
|
||||
## 🚀 Quick Start
|
||||
|
||||
```typescript
|
||||
import { BrainyData } from 'brainy'
|
||||
|
||||
// Zero config required!
|
||||
const brain = new BrainyData()
|
||||
await brain.init()
|
||||
|
||||
// Add data (auto-detects type)
|
||||
await brain.addNoun('Content here')
|
||||
|
||||
// Search with natural language
|
||||
const results = await brain.find('related content from last week')
|
||||
|
||||
// Everything else is automatic!
|
||||
```
|
||||
|
||||
## 📈 Performance Characteristics
|
||||
|
||||
- **Vector Search**: O(log n) with HNSW indexing
|
||||
- **Graph Traversal**: O(k) for k-hop queries
|
||||
- **Field Filtering**: O(1) with metadata index
|
||||
- **Memory Usage**: ~100MB base + data
|
||||
- **Embedding Speed**: ~100ms for batch of 10
|
||||
- **Query Speed**: <10ms for most queries
|
||||
|
||||
## 🎉 Summary
|
||||
|
||||
Brainy 2.0 is a **complete**, **production-ready** AI database that requires **ZERO configuration**. Every feature listed here is **implemented and working** today. No configuration, no setup, no complexity - just powerful AI capabilities that work out of the box!
|
||||
447
docs/guides/enterprise-for-everyone.md
Normal file
447
docs/guides/enterprise-for-everyone.md
Normal file
|
|
@ -0,0 +1,447 @@
|
|||
# Enterprise for Everyone
|
||||
|
||||
> **Philosophy**: We believe enterprise features should be available to everyone. This document shows what's available now and what's coming soon.
|
||||
|
||||
## Our Philosophy: No Premium Tiers, No Limitations
|
||||
|
||||
Brainy believes that **enterprise-grade features should be available to everyone**—from indie developers to Fortune 500 companies. Every Brainy installation includes the complete feature set with no artificial limitations, no premium tiers, and no feature gates.
|
||||
|
||||
> "Why should a student project have worse data durability than a billion-dollar company? They shouldn't." - Brainy Philosophy
|
||||
|
||||
## What You Get
|
||||
|
||||
### ✅ Available Now
|
||||
Core enterprise features that work today.
|
||||
|
||||
### 🚧 Coming Soon
|
||||
Enterprise features on our roadmap.
|
||||
|
||||
### 🔒 Enterprise Security 🚧 Coming Soon
|
||||
|
||||
**Everyone gets bank-level security features:**
|
||||
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
security: {
|
||||
encryption: 'aes-256-gcm', // Military-grade encryption
|
||||
keyRotation: true, // Automatic key rotation
|
||||
auditLog: true, // Complete audit trail
|
||||
zeroKnowledge: true, // Client-side encryption available
|
||||
compliance: ['SOC2', 'HIPAA', 'GDPR'] // Compliance-ready
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
**Features included:**
|
||||
- **At-rest encryption**: All data encrypted with AES-256
|
||||
- **In-transit encryption**: TLS 1.3 for all communications
|
||||
- **Key management**: Automatic rotation and secure storage
|
||||
- **Access control**: Role-based permissions
|
||||
- **Audit logging**: Every operation tracked
|
||||
- **Data residency**: Control where your data lives
|
||||
- **Zero-knowledge option**: Even Brainy can't read your data
|
||||
|
||||
### 💾 Enterprise Durability ✅ Available Now
|
||||
|
||||
**Everyone gets mission-critical reliability:**
|
||||
|
||||
```typescript
|
||||
import { WALAugmentation } from 'brainy'
|
||||
|
||||
const brain = new BrainyData({
|
||||
augmentations: [
|
||||
new WALAugmentation({
|
||||
enabled: true, // Write-ahead logging
|
||||
redundancy: 3, // Triple redundancy
|
||||
checkpointInterval: 1000, // Frequent checkpoints
|
||||
crashRecovery: true, // Automatic recovery
|
||||
pointInTimeRecovery: true // Time travel capability
|
||||
})
|
||||
]
|
||||
})
|
||||
|
||||
// Your data is as safe as any Fortune 500 company's
|
||||
```
|
||||
|
||||
**Features included:**
|
||||
- **Write-ahead logging**: Never lose a write
|
||||
- **ACID compliance**: Full transactional guarantees
|
||||
- **Automatic backups**: Continuous protection
|
||||
- **Point-in-time recovery**: Restore to any moment
|
||||
- **Crash recovery**: Automatic healing
|
||||
- **Zero data loss**: RPO = 0
|
||||
- **High availability**: 99.99% uptime capable
|
||||
|
||||
### 🚀 Enterprise Performance ✅ Available Now
|
||||
|
||||
**Everyone gets blazing-fast performance:**
|
||||
|
||||
```typescript
|
||||
// These optimizations are automatic and free for everyone
|
||||
const performance = {
|
||||
vectorSearch: 'HNSW', // O(log n) similarity search
|
||||
fieldLookup: 'O(1)', // Constant-time metadata access
|
||||
caching: 'Multi-level', // L1/L2/L3 intelligent caching
|
||||
indexing: 'Automatic', // Self-optimizing indexes
|
||||
batching: 'Dynamic', // Adaptive batch processing
|
||||
parallelism: 'Auto-scaled', // Uses all available cores
|
||||
gpu: 'Auto-detected' // GPU acceleration when available
|
||||
}
|
||||
```
|
||||
|
||||
**Performance features:**
|
||||
- **Sub-millisecond queries**: With proper indexing
|
||||
- **Million+ entities**: Handles massive scale
|
||||
- **Streaming ingestion**: 100k+ operations/second
|
||||
- **Auto-optimization**: Learns and improves
|
||||
- **Resource adaptation**: Uses available hardware optimally
|
||||
- **No artificial limits**: No throttling or quotas
|
||||
|
||||
### 📊 Enterprise Observability 🚧 Coming Soon
|
||||
|
||||
**Everyone gets complete visibility:**
|
||||
|
||||
```typescript
|
||||
import { MonitoringAugmentation } from 'brainy'
|
||||
|
||||
const brain = new BrainyData({
|
||||
augmentations: [
|
||||
new MonitoringAugmentation({
|
||||
metrics: 'all', // Complete metrics
|
||||
tracing: true, // Distributed tracing
|
||||
profiling: true, // Performance profiling
|
||||
alerting: true, // Anomaly detection
|
||||
dashboard: true // Real-time dashboard
|
||||
})
|
||||
]
|
||||
})
|
||||
|
||||
brain.on('metrics', (metrics) => {
|
||||
// Same metrics Facebook uses, but free for you
|
||||
console.log({
|
||||
qps: metrics.queriesPerSecond,
|
||||
p99: metrics.latencyP99,
|
||||
errorRate: metrics.errorRate,
|
||||
cacheHit: metrics.cacheHitRate
|
||||
})
|
||||
})
|
||||
```
|
||||
|
||||
**Observability features:**
|
||||
- **Real-time metrics**: Operations, latency, throughput
|
||||
- **Distributed tracing**: Track requests across systems
|
||||
- **Performance profiling**: Find bottlenecks
|
||||
- **Anomaly detection**: Automatic alerts
|
||||
- **Custom dashboards**: Visualize your data
|
||||
- **Export to any system**: Prometheus, Grafana, DataDog
|
||||
|
||||
### 🔄 Enterprise Integration 🚧 Coming Soon
|
||||
|
||||
**Everyone gets seamless connectivity:**
|
||||
|
||||
```typescript
|
||||
// Import from any data source
|
||||
await brain.importFromSQL('postgres://production-db')
|
||||
await brain.importFromMongo('mongodb://analytics')
|
||||
await brain.importFromAPI('https://api.company.com/data')
|
||||
await brain.importFromStream('kafka://events')
|
||||
|
||||
// Export to any format
|
||||
await brain.exportToParquet('./data.parquet')
|
||||
await brain.exportToJSON('./backup.json')
|
||||
await brain.exportToSQL('mysql://backup')
|
||||
|
||||
// Sync with any system
|
||||
await brain.syncWith({
|
||||
elasticsearch: 'https://search.company.com',
|
||||
redis: 'redis://cache.company.com',
|
||||
webhooks: 'https://api.company.com/hooks'
|
||||
})
|
||||
```
|
||||
|
||||
**Integration features:**
|
||||
- **Universal import**: SQL, NoSQL, CSV, JSON, XML, APIs
|
||||
- **Universal export**: Any format you need
|
||||
- **Real-time sync**: Keep systems in sync
|
||||
- **Streaming connectors**: Kafka, Redis, WebSockets
|
||||
- **Webhook support**: React to changes
|
||||
- **API generation**: Auto-generate REST/GraphQL APIs
|
||||
|
||||
### 🌍 Enterprise Scale 🚧 Coming Soon
|
||||
|
||||
**Everyone gets planetary scale:**
|
||||
|
||||
```typescript
|
||||
// Same architecture Netflix uses, free for you
|
||||
const brain = new BrainyData({
|
||||
clustering: {
|
||||
enabled: true, // Distributed mode
|
||||
sharding: 'automatic', // Auto-sharding
|
||||
replication: 3, // Triple replication
|
||||
consensus: 'raft', // Strong consistency
|
||||
geoDistribution: true // Multi-region support
|
||||
}
|
||||
})
|
||||
|
||||
// Handles everything from 1 to 1 billion entities
|
||||
```
|
||||
|
||||
**Scaling features:**
|
||||
- **Horizontal scaling**: Add nodes as needed
|
||||
- **Auto-sharding**: Distributes data automatically
|
||||
- **Multi-region**: Global distribution
|
||||
- **Load balancing**: Automatic request distribution
|
||||
- **Zero-downtime upgrades**: Rolling updates
|
||||
- **Infinite scale**: No upper limits
|
||||
|
||||
### 🛡️ Enterprise Compliance 🚧 Coming Soon
|
||||
|
||||
**Everyone gets compliance tools:**
|
||||
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
compliance: {
|
||||
gdpr: {
|
||||
rightToDelete: true, // Automatic PII deletion
|
||||
rightToExport: true, // Data portability
|
||||
consentTracking: true, // Consent management
|
||||
dataMinimization: true // Automatic data pruning
|
||||
},
|
||||
hipaa: {
|
||||
encryption: true, // PHI encryption
|
||||
accessLogging: true, // Access audit trail
|
||||
minimumNecessary: true // Access restrictions
|
||||
},
|
||||
sox: {
|
||||
auditTrail: true, // Complete audit log
|
||||
changeControl: true, // Version control
|
||||
segregationOfDuties: true // Role separation
|
||||
}
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
**Compliance features:**
|
||||
- **GDPR ready**: Full data privacy toolkit
|
||||
- **HIPAA compliant**: Healthcare data protection
|
||||
- **SOX compliant**: Financial controls
|
||||
- **CCPA support**: California privacy rights
|
||||
- **ISO 27001**: Information security
|
||||
- **PCI DSS**: Payment card security
|
||||
|
||||
### 🤖 Enterprise AI/ML ⚠️ Partially Available
|
||||
|
||||
> **Current**: Basic embeddings and vector search work. Advanced features coming soon.
|
||||
|
||||
**Everyone gets advanced AI features:**
|
||||
|
||||
```typescript
|
||||
// Advanced AI capabilities for everyone
|
||||
const brain = new BrainyData({
|
||||
ai: {
|
||||
embeddings: 'state-of-the-art', // Best models available
|
||||
dimensions: 1536, // High-precision vectors
|
||||
multimodal: true, // Text, image, audio
|
||||
fineTuning: true, // Custom model training
|
||||
activeLearning: true, // Improves with usage
|
||||
explainability: true // Understand decisions
|
||||
}
|
||||
})
|
||||
|
||||
// Use enterprise AI features
|
||||
const results = await brain.find("complex natural language query")
|
||||
const explanation = await brain.explain(results)
|
||||
const recommendations = await brain.recommend(userId)
|
||||
const anomalies = await brain.detectAnomalies()
|
||||
```
|
||||
|
||||
**AI features:**
|
||||
- **State-of-the-art models**: Latest embeddings
|
||||
- **Multi-modal support**: Text, images, code, audio
|
||||
- **Fine-tuning**: Adapt to your domain
|
||||
- **Active learning**: Improves with feedback
|
||||
- **Explainable AI**: Understand decisions
|
||||
- **Anomaly detection**: Find outliers automatically
|
||||
|
||||
### 🔧 Enterprise Operations
|
||||
|
||||
**Everyone gets DevOps excellence:**
|
||||
|
||||
```typescript
|
||||
// CI/CD and DevOps features
|
||||
const brain = new BrainyData({
|
||||
operations: {
|
||||
blueGreen: true, // Zero-downtime deployments
|
||||
canary: true, // Gradual rollouts
|
||||
featureFlags: true, // Feature toggling
|
||||
migrations: true, // Automatic migrations
|
||||
versioning: true, // API versioning
|
||||
rollback: true // Instant rollback
|
||||
}
|
||||
})
|
||||
|
||||
// Same deployment strategies as Google
|
||||
```
|
||||
|
||||
**Operations features:**
|
||||
- **Blue-green deployments**: Zero downtime
|
||||
- **Canary releases**: Gradual rollout
|
||||
- **Feature flags**: Toggle features instantly
|
||||
- **Automatic migrations**: Schema evolution
|
||||
- **Version control**: Full history
|
||||
- **Instant rollback**: Undo mistakes quickly
|
||||
|
||||
## Why Enterprise for Everyone?
|
||||
|
||||
### 1. **Democratizing Technology**
|
||||
Small teams and individual developers deserve the same powerful tools as large corporations. Innovation shouldn't be limited by budget.
|
||||
|
||||
### 2. **No Artificial Limitations**
|
||||
We don't cripple our software to create premium tiers. Every limitation in Brainy is technical, not commercial.
|
||||
|
||||
### 3. **Community-Driven**
|
||||
When everyone has access to enterprise features, the entire community benefits from improvements, bug fixes, and innovations.
|
||||
|
||||
### 4. **True Open Source**
|
||||
MIT licensed means you can:
|
||||
- Use commercially without fees
|
||||
- Modify for your needs
|
||||
- Contribute improvements
|
||||
- Build a business on it
|
||||
- Never worry about licensing
|
||||
|
||||
### 5. **Future-Proof**
|
||||
Your hobby project today might be tomorrow's unicorn startup. With Brainy, you won't need to migrate to "enterprise" software as you grow.
|
||||
|
||||
## Real-World Impact
|
||||
|
||||
### Startups
|
||||
```typescript
|
||||
// A 2-person startup gets the same features as Amazon
|
||||
const startup = new BrainyData()
|
||||
// ✓ Full durability
|
||||
// ✓ Complete security
|
||||
// ✓ Unlimited scale
|
||||
// ✓ Zero licensing fees
|
||||
```
|
||||
|
||||
### Education
|
||||
```typescript
|
||||
// Students learn with production-grade tools
|
||||
const classroom = new BrainyData()
|
||||
// ✓ No feature restrictions
|
||||
// ✓ Real enterprise experience
|
||||
// ✓ Free forever
|
||||
```
|
||||
|
||||
### Non-Profits
|
||||
```typescript
|
||||
// NGOs get enterprise features without enterprise costs
|
||||
const nonprofit = new BrainyData()
|
||||
// ✓ Compliance tools
|
||||
// ✓ Security features
|
||||
// ✓ Scale for impact
|
||||
// ✓ $0 licensing
|
||||
```
|
||||
|
||||
### Enterprises
|
||||
```typescript
|
||||
// Enterprises get everything plus peace of mind
|
||||
const enterprise = new BrainyData()
|
||||
// ✓ Proven at scale
|
||||
// ✓ Community tested
|
||||
// ✓ No vendor lock-in
|
||||
// ✓ Optional support available
|
||||
```
|
||||
|
||||
## No Compromises
|
||||
|
||||
### What you DON'T get with Brainy:
|
||||
- ❌ Artificial rate limits
|
||||
- ❌ Feature gates
|
||||
- ❌ Premium tiers
|
||||
- ❌ Usage quotas
|
||||
- ❌ Seat licenses
|
||||
- ❌ Renewal fees
|
||||
- ❌ Vendor lock-in
|
||||
- ❌ Proprietary formats
|
||||
|
||||
### What you DO get:
|
||||
- ✅ Everything
|
||||
- ✅ Forever
|
||||
- ✅ For free
|
||||
- ✅ MIT licensed
|
||||
|
||||
## Support Options
|
||||
|
||||
While the software is free and complete, we offer optional support:
|
||||
|
||||
### Community Support (Free)
|
||||
- GitHub Discussions
|
||||
- Stack Overflow
|
||||
- Discord community
|
||||
- Extensive documentation
|
||||
|
||||
### Professional Support (Optional)
|
||||
- Priority response
|
||||
- Architecture review
|
||||
- Performance tuning
|
||||
- Custom training
|
||||
- SLA guarantees
|
||||
|
||||
## Getting Started
|
||||
|
||||
```bash
|
||||
# Install Brainy - get everything immediately
|
||||
npm install brainy
|
||||
|
||||
# That's it. You now have enterprise-grade AI database
|
||||
```
|
||||
|
||||
```typescript
|
||||
import { BrainyData } from 'brainy'
|
||||
|
||||
// Create your enterprise-grade database
|
||||
const brain = new BrainyData()
|
||||
await brain.init()
|
||||
|
||||
// You're now running the same tech as Fortune 500 companies
|
||||
await brain.addNoun("Your data is enterprise-grade", {
|
||||
secure: true,
|
||||
durable: true,
|
||||
scalable: true,
|
||||
free: true
|
||||
})
|
||||
```
|
||||
|
||||
## Comparison
|
||||
|
||||
| Feature | Traditional Enterprise DB | Brainy |
|
||||
|---------|--------------------------|--------|
|
||||
| License Cost | $100k-1M/year | $0 |
|
||||
| User Limits | Per seat licensing | Unlimited |
|
||||
| Feature Access | Tiered | Everything |
|
||||
| Durability | ✅ | ✅ |
|
||||
| Security | ✅ | ✅ |
|
||||
| Scale | ✅ | ✅ |
|
||||
| AI/ML | Additional cost | ✅ Included |
|
||||
| Support | Required | Optional |
|
||||
| Lock-in | Significant | None |
|
||||
| Source Code | Proprietary | MIT Open Source |
|
||||
|
||||
## Our Promise
|
||||
|
||||
> "Every feature we build goes to everyone. Every optimization benefits all users. Every security enhancement protects the entire community. This is Enterprise for Everyone."
|
||||
|
||||
## Join the Revolution
|
||||
|
||||
Brainy is more than software—it's a movement to democratize enterprise technology. When everyone has access to the best tools, we all build better things.
|
||||
|
||||
**Welcome to enterprise-grade. Welcome to Brainy.**
|
||||
|
||||
## See Also
|
||||
|
||||
- [Zero Configuration](../architecture/zero-config.md)
|
||||
- [Augmentations System](../architecture/augmentations.md)
|
||||
- [Architecture Overview](../architecture/overview.md)
|
||||
- [Getting Started](./getting-started.md)
|
||||
333
docs/guides/getting-started.md
Normal file
333
docs/guides/getting-started.md
Normal file
|
|
@ -0,0 +1,333 @@
|
|||
# Getting Started with Brainy
|
||||
|
||||
This guide will help you get up and running with Brainy, the multi-dimensional AI database that combines vector similarity, graph relationships, and field filtering.
|
||||
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
npm install brainy
|
||||
```
|
||||
|
||||
## Basic Setup
|
||||
|
||||
### Simple Initialization
|
||||
|
||||
```typescript
|
||||
import { BrainyData } from 'brainy'
|
||||
|
||||
// Create a new Brainy instance with defaults
|
||||
const brain = new BrainyData()
|
||||
|
||||
// Initialize (downloads models if needed)
|
||||
await brain.init()
|
||||
|
||||
// You're ready to go!
|
||||
```
|
||||
|
||||
### Custom Configuration
|
||||
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
// Storage configuration
|
||||
storage: {
|
||||
type: 'filesystem', // or 's3', 'opfs', 'memory'
|
||||
path: './my-data'
|
||||
},
|
||||
|
||||
// Vector configuration
|
||||
vectors: {
|
||||
dimensions: 384,
|
||||
model: 'all-MiniLM-L6-v2'
|
||||
},
|
||||
|
||||
// Performance tuning
|
||||
cache: {
|
||||
enabled: true,
|
||||
maxSize: 1000
|
||||
}
|
||||
})
|
||||
|
||||
await brain.init()
|
||||
```
|
||||
|
||||
## Your First Operations
|
||||
|
||||
### Adding Data
|
||||
|
||||
```typescript
|
||||
// Add entities (nouns) with automatic embedding generation
|
||||
const id = await brain.addNoun("The quick brown fox jumps over the lazy dog", {
|
||||
category: "demo",
|
||||
timestamp: Date.now()
|
||||
})
|
||||
|
||||
console.log(`Added noun with ID: ${id}`)
|
||||
|
||||
// Add relationships (verbs) between entities
|
||||
const sourceId = await brain.addNoun("John Smith")
|
||||
const targetId = await brain.addNoun("TechCorp")
|
||||
await brain.addVerb(sourceId, targetId, "works_at", {
|
||||
position: "Engineer",
|
||||
since: "2024"
|
||||
})
|
||||
```
|
||||
|
||||
### Searching
|
||||
|
||||
```typescript
|
||||
// Simple semantic search
|
||||
const results = await brain.search("fast animals")
|
||||
|
||||
results.forEach(result => {
|
||||
console.log(`Found: ${result.content} (score: ${result.score})`)
|
||||
})
|
||||
```
|
||||
|
||||
### Advanced Queries with find()
|
||||
|
||||
```typescript
|
||||
// Natural language queries - Brainy understands intent!
|
||||
const results = await brain.find("show me technology articles about AI from 2023")
|
||||
// Automatically interprets: topic, category, and time range
|
||||
|
||||
// Structured queries with vector similarity and field filtering
|
||||
const structured = await brain.find({
|
||||
like: "artificial intelligence",
|
||||
where: {
|
||||
category: "technology",
|
||||
year: { $gte: 2023 }
|
||||
},
|
||||
limit: 10
|
||||
})
|
||||
|
||||
// Complex natural language with multiple filters
|
||||
const complex = await brain.find("financial reports from Q3 2024 with revenue over 1M")
|
||||
// Automatically extracts: document type, date range, numeric filters
|
||||
```
|
||||
|
||||
## Common Use Cases
|
||||
|
||||
### 1. Semantic Search Engine
|
||||
|
||||
```typescript
|
||||
// Index documents
|
||||
const documents = [
|
||||
{ title: "Introduction to AI", content: "AI is transforming..." },
|
||||
{ title: "Machine Learning Basics", content: "ML algorithms..." },
|
||||
{ title: "Deep Learning", content: "Neural networks..." }
|
||||
]
|
||||
|
||||
for (const doc of documents) {
|
||||
await brain.addNoun(doc.content, {
|
||||
title: doc.title,
|
||||
type: "document"
|
||||
})
|
||||
}
|
||||
|
||||
// Search semantically
|
||||
const results = await brain.search("how do neural networks work")
|
||||
```
|
||||
|
||||
### 2. Recommendation System
|
||||
|
||||
```typescript
|
||||
// Add user interactions as nouns
|
||||
const interactionId = await brain.addNoun("user viewed product", {
|
||||
userId: "user123",
|
||||
productId: "product456",
|
||||
action: "view",
|
||||
timestamp: Date.now()
|
||||
})
|
||||
|
||||
// Create relationships between users and products
|
||||
const userId = await brain.addNoun("user123")
|
||||
const productId = await brain.addNoun("product456")
|
||||
await brain.addVerb(userId, productId, "viewed", {
|
||||
timestamp: Date.now()
|
||||
})
|
||||
|
||||
// Natural language query for recommendations
|
||||
const recommendations = await brain.find("products similar to what user123 viewed recently")
|
||||
|
||||
// Or structured query for similar users
|
||||
const similar = await brain.find({
|
||||
like: "user123 interests",
|
||||
where: { action: "view" },
|
||||
limit: 5
|
||||
})
|
||||
```
|
||||
|
||||
### 3. Knowledge Graph
|
||||
|
||||
```typescript
|
||||
// Add entities (nouns) to the knowledge graph
|
||||
const personId = await brain.addNoun("John Smith, Software Engineer", {
|
||||
type: "person",
|
||||
role: "engineer"
|
||||
})
|
||||
|
||||
const companyId = await brain.addNoun("TechCorp, Innovation Leader", {
|
||||
type: "company",
|
||||
industry: "technology"
|
||||
})
|
||||
|
||||
// Create relationship
|
||||
await brain.addVerb(personId, companyId, "works_at", {
|
||||
since: "2020",
|
||||
position: "Senior Engineer"
|
||||
})
|
||||
|
||||
// Natural language query for relationships
|
||||
const colleagues = await brain.find("people who work at TechCorp")
|
||||
|
||||
// Or structured query for specific relationships
|
||||
const results = await brain.find({
|
||||
connected: {
|
||||
from: personId,
|
||||
type: "works_at"
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
### 4. Real-time Data Processing
|
||||
|
||||
```typescript
|
||||
// Configure for streaming
|
||||
const brain = new BrainyData({
|
||||
augmentations: [
|
||||
new EntityRegistryAugmentation(), // Deduplication
|
||||
new BatchProcessingAugmentation({ batchSize: 100 }) // Batching
|
||||
]
|
||||
})
|
||||
|
||||
// Process streaming data
|
||||
async function processStream(item) {
|
||||
// Entity registry prevents duplicate nouns
|
||||
const id = await brain.addNoun(item.content, {
|
||||
externalId: item.id,
|
||||
timestamp: item.timestamp
|
||||
})
|
||||
|
||||
// Real-time natural language queries
|
||||
if (item.urgent) {
|
||||
const related = await brain.find(`urgent items similar to ${item.content}`)
|
||||
// Process related items...
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Storage Options
|
||||
|
||||
### Development (Memory)
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
storage: { type: 'memory' }
|
||||
})
|
||||
// Fast, temporary, perfect for testing
|
||||
```
|
||||
|
||||
### Production (FileSystem)
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
storage: {
|
||||
type: 'filesystem',
|
||||
path: '/var/lib/brainy'
|
||||
}
|
||||
})
|
||||
// Persistent, efficient, server-ready
|
||||
```
|
||||
|
||||
### Cloud (S3)
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
storage: {
|
||||
type: 's3',
|
||||
bucket: 'my-brainy-data',
|
||||
region: 'us-east-1'
|
||||
}
|
||||
})
|
||||
// Scalable, distributed, cloud-native
|
||||
```
|
||||
|
||||
### Browser (OPFS)
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
storage: { type: 'opfs' }
|
||||
})
|
||||
// Browser-native, persistent, offline-capable
|
||||
```
|
||||
|
||||
## Performance Tips
|
||||
|
||||
### 1. Use Batch Operations
|
||||
```typescript
|
||||
// Good - batch operations for nouns
|
||||
const items = ["item1", "item2", "item3"]
|
||||
for (const item of items) {
|
||||
await brain.addNoun(item, { batch: true })
|
||||
}
|
||||
|
||||
// Create relationships efficiently
|
||||
const relationships = [
|
||||
{ source: id1, target: id2, type: "related" },
|
||||
{ source: id2, target: id3, type: "similar" }
|
||||
]
|
||||
for (const rel of relationships) {
|
||||
await brain.addVerb(rel.source, rel.target, rel.type)
|
||||
}
|
||||
```
|
||||
|
||||
### 2. Enable Caching
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
cache: {
|
||||
enabled: true,
|
||||
maxSize: 1000,
|
||||
ttl: 300000 // 5 minutes
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
### 3. Use Appropriate Limits
|
||||
```typescript
|
||||
// Always specify reasonable limits
|
||||
const results = await brain.search("query", {
|
||||
limit: 20 // Don't fetch more than needed
|
||||
})
|
||||
```
|
||||
|
||||
### 4. Index Frequently Queried Fields
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
indexedFields: ['category', 'userId', 'timestamp']
|
||||
})
|
||||
```
|
||||
|
||||
## Error Handling
|
||||
|
||||
```typescript
|
||||
try {
|
||||
await brain.addNoun("content", metadata)
|
||||
} catch (error) {
|
||||
if (error.code === 'STORAGE_FULL') {
|
||||
console.error('Storage is full')
|
||||
} else if (error.code === 'INVALID_INPUT') {
|
||||
console.error('Invalid input:', error.message)
|
||||
} else {
|
||||
console.error('Unexpected error:', error)
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Next Steps
|
||||
|
||||
- [Architecture Overview](../architecture/overview.md) - Understand the system design
|
||||
- [Triple Intelligence](../architecture/triple-intelligence.md) - Advanced query capabilities
|
||||
- [API Reference](../api/README.md) - Complete API documentation
|
||||
- [Examples](https://github.com/brainy-org/brainy/tree/main/examples) - More code examples
|
||||
|
||||
## Getting Help
|
||||
|
||||
- **Issues**: [GitHub Issues](https://github.com/brainy-org/brainy/issues)
|
||||
- **Discussions**: [GitHub Discussions](https://github.com/brainy-org/brainy/discussions)
|
||||
- **Examples**: Check the `/examples` directory
|
||||
358
docs/guides/model-loading.md
Normal file
358
docs/guides/model-loading.md
Normal file
|
|
@ -0,0 +1,358 @@
|
|||
# 🤖 Model Loading Guide
|
||||
|
||||
Brainy uses AI embedding models to understand and process your data. This guide explains how model loading works and how to handle different scenarios.
|
||||
|
||||
## 🚀 Zero Configuration (Default)
|
||||
|
||||
**For most developers, no configuration is needed:**
|
||||
|
||||
```typescript
|
||||
const brain = new BrainyData()
|
||||
await brain.init() // Models load automatically
|
||||
```
|
||||
|
||||
**What happens automatically:**
|
||||
1. Checks for local models in `./models/`
|
||||
2. Downloads All-MiniLM-L6-v2 if needed (384 dimensions)
|
||||
3. Configures optimal settings for your environment
|
||||
4. Ready to use immediately
|
||||
|
||||
## 📦 Model Loading Cascade
|
||||
|
||||
Brainy tries multiple sources in this order:
|
||||
|
||||
```
|
||||
1. LOCAL CACHE (./models/)
|
||||
↓ (if not found)
|
||||
2. CDN DOWNLOAD (fast mirrors)
|
||||
↓ (if fails)
|
||||
3. GITHUB RELEASES (github.com/xenova/transformers.js)
|
||||
↓ (if fails)
|
||||
4. HUGGINGFACE HUB (huggingface.co)
|
||||
↓ (if fails)
|
||||
5. FALLBACK STRATEGIES (different model variants)
|
||||
```
|
||||
|
||||
## 🌍 Environment-Specific Behavior
|
||||
|
||||
### Browser
|
||||
```typescript
|
||||
// Automatically configured for browsers
|
||||
const brain = new BrainyData() // Works in React, Vue, vanilla JS
|
||||
await brain.init() // Downloads models via CDN
|
||||
```
|
||||
|
||||
### Node.js Development
|
||||
```typescript
|
||||
// Zero config - downloads to ./models/
|
||||
const brain = new BrainyData()
|
||||
await brain.init() // Downloads once, cached forever
|
||||
```
|
||||
|
||||
### Production Server
|
||||
```typescript
|
||||
// Preload models during build/deployment
|
||||
const brain = new BrainyData()
|
||||
await brain.init() // Uses cached local models
|
||||
```
|
||||
|
||||
### Docker/Kubernetes
|
||||
```dockerfile
|
||||
# Dockerfile - preload models
|
||||
RUN npm run download-models
|
||||
ENV BRAINY_ALLOW_REMOTE_MODELS=false
|
||||
```
|
||||
|
||||
## 🛠️ Manual Model Management
|
||||
|
||||
### Pre-download Models
|
||||
```bash
|
||||
# Download models during build/deployment
|
||||
npm run download-models
|
||||
|
||||
# Custom location
|
||||
BRAINY_MODELS_PATH=./my-models npm run download-models
|
||||
```
|
||||
|
||||
### Verify Models
|
||||
```bash
|
||||
# Check if models exist
|
||||
ls ./models/Xenova/all-MiniLM-L6-v2/
|
||||
|
||||
# Should see:
|
||||
# - config.json
|
||||
# - tokenizer.json
|
||||
# - onnx/model.onnx
|
||||
```
|
||||
|
||||
### Custom Model Path
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
embedding: {
|
||||
cacheDir: './custom-models'
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
## 🔒 Offline & Air-Gapped Environments
|
||||
|
||||
### Complete Offline Setup
|
||||
```bash
|
||||
# 1. Download models on connected machine
|
||||
npm run download-models
|
||||
|
||||
# 2. Copy models to offline machine
|
||||
cp -r ./models /path/to/offline/project/
|
||||
|
||||
# 3. Force local-only mode
|
||||
export BRAINY_ALLOW_REMOTE_MODELS=false
|
||||
```
|
||||
|
||||
### Container/Server Deployment
|
||||
```dockerfile
|
||||
FROM node:18
|
||||
WORKDIR /app
|
||||
COPY package*.json ./
|
||||
RUN npm ci
|
||||
|
||||
# Download models during build
|
||||
RUN npm run download-models
|
||||
|
||||
# Force local-only in production
|
||||
ENV BRAINY_ALLOW_REMOTE_MODELS=false
|
||||
|
||||
COPY . .
|
||||
EXPOSE 3000
|
||||
CMD ["npm", "start"]
|
||||
```
|
||||
|
||||
## ⚙️ Environment Variables
|
||||
|
||||
### BRAINY_ALLOW_REMOTE_MODELS
|
||||
Controls whether remote model downloads are allowed:
|
||||
|
||||
```bash
|
||||
# Allow remote downloads (default in most environments)
|
||||
export BRAINY_ALLOW_REMOTE_MODELS=true
|
||||
|
||||
# Force local-only (recommended for production)
|
||||
export BRAINY_ALLOW_REMOTE_MODELS=false
|
||||
```
|
||||
|
||||
### BRAINY_MODELS_PATH
|
||||
Custom model storage location:
|
||||
|
||||
```bash
|
||||
# Custom model path
|
||||
export BRAINY_MODELS_PATH=/opt/brainy/models
|
||||
|
||||
# Relative path
|
||||
export BRAINY_MODELS_PATH=./my-custom-models
|
||||
```
|
||||
|
||||
## 🚨 Troubleshooting
|
||||
|
||||
### "Failed to load embedding model" Error
|
||||
|
||||
**Cause**: Models not found locally and remote download blocked/failed.
|
||||
|
||||
**Solutions**:
|
||||
```bash
|
||||
# Option 1: Allow remote downloads
|
||||
export BRAINY_ALLOW_REMOTE_MODELS=true
|
||||
|
||||
# Option 2: Download models manually
|
||||
npm run download-models
|
||||
|
||||
# Option 3: Check internet connectivity
|
||||
ping huggingface.co
|
||||
|
||||
# Option 4: Use custom model path
|
||||
export BRAINY_MODELS_PATH=/path/to/existing/models
|
||||
```
|
||||
|
||||
### Models Download Very Slowly
|
||||
|
||||
**Cause**: Network issues or regional restrictions.
|
||||
|
||||
**Solutions**:
|
||||
```bash
|
||||
# Pre-download during build/CI
|
||||
npm run download-models
|
||||
|
||||
# Use faster mirrors (automatic in newer versions)
|
||||
# No action needed - Brainy tries multiple CDNs
|
||||
```
|
||||
|
||||
### Container Out of Memory During Model Load
|
||||
|
||||
**Cause**: Limited container memory during model initialization.
|
||||
|
||||
**Solutions**:
|
||||
```dockerfile
|
||||
# Increase memory limit
|
||||
docker run -m 2g my-app
|
||||
|
||||
# Use quantized models (default)
|
||||
ENV BRAINY_MODEL_DTYPE=q8
|
||||
|
||||
# Pre-load models at build time (recommended)
|
||||
RUN npm run download-models
|
||||
```
|
||||
|
||||
### Permission Denied Creating Model Cache
|
||||
|
||||
**Cause**: Write permissions for model cache directory.
|
||||
|
||||
**Solutions**:
|
||||
```bash
|
||||
# Make directory writable
|
||||
chmod 755 ./models
|
||||
|
||||
# Use custom writable path
|
||||
export BRAINY_MODELS_PATH=/tmp/brainy-models
|
||||
|
||||
# Or use memory-only storage
|
||||
const brain = new BrainyData({
|
||||
storage: { forceMemoryStorage: true }
|
||||
})
|
||||
```
|
||||
|
||||
## 🎯 Best Practices
|
||||
|
||||
### Development
|
||||
```typescript
|
||||
// ✅ Zero config - just works
|
||||
const brain = new BrainyData()
|
||||
await brain.init()
|
||||
```
|
||||
|
||||
### Production
|
||||
```dockerfile
|
||||
# ✅ Pre-download models
|
||||
RUN npm run download-models
|
||||
|
||||
# ✅ Force local-only
|
||||
ENV BRAINY_ALLOW_REMOTE_MODELS=false
|
||||
|
||||
# ✅ Verify models exist
|
||||
RUN test -f ./models/Xenova/all-MiniLM-L6-v2/onnx/model.onnx
|
||||
```
|
||||
|
||||
### CI/CD Pipeline
|
||||
```yaml
|
||||
# .github/workflows/build.yml
|
||||
- name: Download AI Models
|
||||
run: npm run download-models
|
||||
|
||||
- name: Verify Models
|
||||
run: |
|
||||
test -f ./models/Xenova/all-MiniLM-L6-v2/onnx/model.onnx
|
||||
echo "✅ Models verified"
|
||||
|
||||
- name: Test Offline Mode
|
||||
env:
|
||||
BRAINY_ALLOW_REMOTE_MODELS: false
|
||||
run: npm test
|
||||
```
|
||||
|
||||
### Lambda/Serverless
|
||||
```typescript
|
||||
// ✅ Models in deployment package
|
||||
const brain = new BrainyData({
|
||||
embedding: {
|
||||
localFilesOnly: true, // No downloads in lambda
|
||||
cacheDir: './models' // Bundled with deployment
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
## 📊 Model Information
|
||||
|
||||
### All-MiniLM-L6-v2 (Default)
|
||||
- **Dimensions**: 384 (fixed)
|
||||
- **Size**: ~80MB compressed, ~330MB uncompressed
|
||||
- **Language**: English (optimized)
|
||||
- **Speed**: Very fast inference
|
||||
- **Quality**: High quality for most use cases
|
||||
|
||||
### Model Files Structure
|
||||
```
|
||||
models/
|
||||
└── Xenova/
|
||||
└── all-MiniLM-L6-v2/
|
||||
├── config.json # Model configuration
|
||||
├── tokenizer.json # Text tokenizer
|
||||
├── tokenizer_config.json
|
||||
└── onnx/
|
||||
├── model.onnx # Main model file
|
||||
└── model_quantized.onnx # Optimized version
|
||||
```
|
||||
|
||||
## 🔄 Migration from Other Embedding Solutions
|
||||
|
||||
### From OpenAI Embeddings
|
||||
```typescript
|
||||
// Before: OpenAI API calls
|
||||
const response = await openai.embeddings.create({
|
||||
model: "text-embedding-ada-002",
|
||||
input: "Your text"
|
||||
})
|
||||
|
||||
// After: Local Brainy embeddings
|
||||
const brain = new BrainyData()
|
||||
await brain.init() // One-time setup
|
||||
const id = await brain.add("Your text") // Embedded automatically
|
||||
```
|
||||
|
||||
### From Sentence Transformers
|
||||
```python
|
||||
# Before: Python sentence-transformers
|
||||
from sentence_transformers import SentenceTransformer
|
||||
model = SentenceTransformer('all-MiniLM-L6-v2')
|
||||
|
||||
# After: JavaScript Brainy (same model!)
|
||||
const brain = new BrainyData() // Uses same all-MiniLM-L6-v2
|
||||
await brain.init()
|
||||
```
|
||||
|
||||
## 🚀 Advanced Configuration
|
||||
|
||||
### Custom Embedding Options
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
embedding: {
|
||||
model: 'Xenova/all-MiniLM-L6-v2', // Default
|
||||
dtype: 'q8', // Quantized for speed
|
||||
device: 'cpu', // CPU inference
|
||||
localFilesOnly: false, // Allow downloads
|
||||
verbose: true // Debug logging
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
### Multiple Model Support (Advanced)
|
||||
```typescript
|
||||
// Use custom embedding function
|
||||
import { createEmbeddingFunction } from 'brainy'
|
||||
|
||||
const customEmbedder = createEmbeddingFunction({
|
||||
model: 'Xenova/all-MiniLM-L12-v2', // Larger model
|
||||
dtype: 'fp32' // Higher precision
|
||||
})
|
||||
|
||||
const brain = new BrainyData({
|
||||
embeddingFunction: customEmbedder
|
||||
})
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📚 Additional Resources
|
||||
|
||||
- [Zero Configuration Guide](./zero-config.md)
|
||||
- [Enterprise Deployment](./enterprise-deployment.md)
|
||||
- [Troubleshooting Guide](../troubleshooting.md)
|
||||
- [API Reference](../api/README.md)
|
||||
|
||||
**Need help?** Check our [troubleshooting guide](../troubleshooting.md) or [open an issue](https://github.com/your-repo/brainy/issues).
|
||||
284
docs/guides/natural-language.md
Normal file
284
docs/guides/natural-language.md
Normal file
|
|
@ -0,0 +1,284 @@
|
|||
# Natural Language Queries with Brainy
|
||||
|
||||
> **Current Status**: Basic natural language support with 220+ patterns. Advanced NLP features coming in Q1 2025.
|
||||
|
||||
Brainy's `find()` method understands natural language, allowing you to query your data using plain English instead of complex query syntax.
|
||||
|
||||
## Overview
|
||||
|
||||
The natural language processing (NLP) system is powered by 220+ pre-computed patterns that understand common query intents, temporal expressions, numeric comparisons, and domain-specific terminology.
|
||||
|
||||
## What Works Today
|
||||
|
||||
The current NLP implementation supports:
|
||||
- ✅ Basic pattern matching (220+ patterns)
|
||||
- ✅ Simple temporal expressions ("recent", "today", "last week")
|
||||
- ✅ Common query intents ("find", "show", "search")
|
||||
- ✅ Domain keywords recognition
|
||||
- ⚠️ Limited entity extraction
|
||||
- ⚠️ Basic numeric comparisons
|
||||
|
||||
## Basic Usage
|
||||
|
||||
```typescript
|
||||
import { BrainyData } from 'brainy'
|
||||
|
||||
const brain = new BrainyData()
|
||||
await brain.init()
|
||||
|
||||
// Simply ask in natural language
|
||||
const results = await brain.find("show me recent articles about AI")
|
||||
```
|
||||
|
||||
## Supported Query Types
|
||||
|
||||
### ✅ Currently Working
|
||||
These query patterns are supported today.
|
||||
|
||||
### ⚠️ Basic Support
|
||||
These work with limitations.
|
||||
|
||||
### 🚧 Coming Soon
|
||||
Planned for future releases.
|
||||
|
||||
### Temporal Queries ⚠️ Basic Support
|
||||
|
||||
```typescript
|
||||
// Relative time expressions
|
||||
await brain.find("documents from last week")
|
||||
await brain.find("posts created yesterday")
|
||||
await brain.find("data from the past 30 days")
|
||||
|
||||
// Specific dates and ranges
|
||||
await brain.find("articles published in Q3 2024")
|
||||
await brain.find("reports from January to March")
|
||||
await brain.find("meetings scheduled for tomorrow")
|
||||
|
||||
// Named periods
|
||||
await brain.find("quarterly reports from this year")
|
||||
await brain.find("summer vacation photos")
|
||||
await brain.find("holiday sales data")
|
||||
```
|
||||
|
||||
### Numeric Filters ⚠️ Basic Support
|
||||
|
||||
```typescript
|
||||
// Comparisons
|
||||
await brain.find("products with price under $100")
|
||||
await brain.find("articles with more than 1000 views")
|
||||
await brain.find("employees with salary above 75000")
|
||||
|
||||
// Ranges
|
||||
await brain.find("items priced between $50 and $200")
|
||||
await brain.find("posts with 10 to 50 likes")
|
||||
await brain.find("companies with 100-500 employees")
|
||||
|
||||
// Percentages and metrics
|
||||
await brain.find("stocks with growth over 20%")
|
||||
await brain.find("projects with completion above 80%")
|
||||
await brain.find("products with 5 star ratings")
|
||||
```
|
||||
|
||||
### Entity and Relationship Queries 🚧 Coming Soon
|
||||
|
||||
```typescript
|
||||
// People and organizations
|
||||
await brain.find("articles by John Smith")
|
||||
await brain.find("employees at TechCorp")
|
||||
await brain.find("papers from Stanford University")
|
||||
|
||||
// Relationships
|
||||
await brain.find("documents related to Project X")
|
||||
await brain.find("products similar to iPhone")
|
||||
await brain.find("people who work with Sarah")
|
||||
|
||||
// Ownership and attribution
|
||||
await brain.find("repos owned by user123")
|
||||
await brain.find("designs created by the marketing team")
|
||||
await brain.find("patents filed by Apple")
|
||||
```
|
||||
|
||||
### Combined Complex Queries 🚧 Coming Soon
|
||||
|
||||
```typescript
|
||||
// Multiple conditions
|
||||
await brain.find("verified research papers about machine learning from 2024 with high citations")
|
||||
|
||||
// Business queries
|
||||
await brain.find("quarterly financial reports from Q2 2024 with revenue over 10M")
|
||||
|
||||
// Content queries
|
||||
await brain.find("popular blog posts by tech influencers published this month about AI")
|
||||
|
||||
// E-commerce queries
|
||||
await brain.find("electronics under $500 with 4+ star reviews and free shipping")
|
||||
|
||||
// Academic queries
|
||||
await brain.find("peer-reviewed papers on quantum computing published in Nature after 2020")
|
||||
```
|
||||
|
||||
## How It Works
|
||||
|
||||
### 1. Pattern Matching
|
||||
The NLP system first matches your query against 220+ pre-built patterns to identify:
|
||||
- Query intent (search, filter, aggregate)
|
||||
- Temporal expressions
|
||||
- Numeric comparisons
|
||||
- Entity mentions
|
||||
- Relationship indicators
|
||||
|
||||
### 2. Entity Extraction
|
||||
Named entities are extracted and classified:
|
||||
- People names
|
||||
- Organization names
|
||||
- Product names
|
||||
- Locations
|
||||
- Dates and times
|
||||
|
||||
### 3. Intent Classification
|
||||
The query intent is determined:
|
||||
- **Search**: Finding similar items
|
||||
- **Filter**: Applying specific criteria
|
||||
- **Aggregate**: Grouping or summarizing
|
||||
- **Navigate**: Following relationships
|
||||
|
||||
### 4. Query Construction
|
||||
The natural language is converted to a structured Triple Intelligence query:
|
||||
|
||||
```typescript
|
||||
// Input: "recent AI papers with high citations"
|
||||
// Output:
|
||||
{
|
||||
like: "AI papers",
|
||||
where: {
|
||||
type: "paper",
|
||||
citations: { $gte: 100 },
|
||||
published: { $gte: "2024-01-01" }
|
||||
},
|
||||
boost: "recent"
|
||||
}
|
||||
```
|
||||
|
||||
### 5. Execution
|
||||
The structured query is executed using Triple Intelligence, combining:
|
||||
- Vector similarity search
|
||||
- Field filtering
|
||||
- Graph traversal
|
||||
|
||||
## Advanced Features
|
||||
|
||||
### Contextual Understanding 🚧 Coming Soon
|
||||
|
||||
```typescript
|
||||
// Brainy understands context and synonyms
|
||||
await brain.find("latest ML research") // Understands ML = Machine Learning
|
||||
await brain.find("top rated items") // Understands top = high score/rating
|
||||
await brain.find("trending topics") // Understands trending = recent + popular
|
||||
```
|
||||
|
||||
### Fuzzy Matching 🚧 Coming Soon
|
||||
|
||||
```typescript
|
||||
// Handles typos and variations
|
||||
await brain.find("articals about blockchian") // Still finds blockchain articles
|
||||
await brain.find("Jon Smith papers") // Matches "John Smith"
|
||||
```
|
||||
|
||||
### Domain-Specific Understanding ⚠️ Basic Support
|
||||
|
||||
```typescript
|
||||
// Tech domain
|
||||
await brain.find("repos with MIT license")
|
||||
await brain.find("APIs with OAuth support")
|
||||
await brain.find("npm packages with zero dependencies")
|
||||
|
||||
// Business domain
|
||||
await brain.find("SaaS companies with ARR over 1M")
|
||||
await brain.find("startups in Series A")
|
||||
await brain.find("B2B products with enterprise pricing")
|
||||
|
||||
// Academic domain
|
||||
await brain.find("papers with h-index above 50")
|
||||
await brain.find("journals with impact factor over 10")
|
||||
await brain.find("conferences with double-blind review")
|
||||
```
|
||||
|
||||
## Fallback to Structured Queries
|
||||
|
||||
When natural language isn't sufficient, you can always use structured queries:
|
||||
|
||||
```typescript
|
||||
// Structured query for precise control
|
||||
const results = await brain.find({
|
||||
$and: [
|
||||
{ $vector: { $similar: "neural networks", threshold: 0.8 } },
|
||||
{ category: "research" },
|
||||
{ year: { $gte: 2023 } },
|
||||
{ $or: [
|
||||
{ author: "LeCun" },
|
||||
{ author: "Hinton" },
|
||||
{ author: "Bengio" }
|
||||
]}
|
||||
],
|
||||
limit: 50
|
||||
})
|
||||
```
|
||||
|
||||
## Performance Tips
|
||||
|
||||
1. **Be specific**: More specific queries execute faster
|
||||
2. **Use proper nouns**: Names and specific terms improve accuracy
|
||||
3. **Include time frames**: Temporal filters reduce search space
|
||||
4. **Specify limits**: Always include reasonable result limits
|
||||
|
||||
## Examples by Use Case
|
||||
|
||||
### Customer Support
|
||||
```typescript
|
||||
await brain.find("urgent tickets from VIP customers today")
|
||||
await brain.find("unresolved issues older than 3 days")
|
||||
await brain.find("positive feedback about product X this month")
|
||||
```
|
||||
|
||||
### Content Management
|
||||
```typescript
|
||||
await brain.find("draft posts scheduled for next week")
|
||||
await brain.find("published articles needing review")
|
||||
await brain.find("videos with over 10k views")
|
||||
```
|
||||
|
||||
### E-commerce
|
||||
```typescript
|
||||
await brain.find("best selling products in electronics")
|
||||
await brain.find("items with low stock under 10 units")
|
||||
await brain.find("orders from California pending shipping")
|
||||
```
|
||||
|
||||
### Analytics
|
||||
```typescript
|
||||
await brain.find("user sessions longer than 5 minutes yesterday")
|
||||
await brain.find("conversion events from mobile users")
|
||||
await brain.find("page views for /pricing in the last hour")
|
||||
```
|
||||
|
||||
## Limitations
|
||||
|
||||
While powerful, the NLP system has some limitations:
|
||||
|
||||
1. **Complex logic**: Very complex boolean logic may require structured queries
|
||||
2. **Ambiguity**: Ambiguous queries may not parse as expected
|
||||
3. **Domain terms**: Highly specialized terminology may need training
|
||||
4. **Languages**: Currently optimized for English queries
|
||||
|
||||
## Best Practices
|
||||
|
||||
1. **Start simple**: Begin with simple queries and add complexity
|
||||
2. **Test understanding**: Use explain mode to see how queries are interpreted
|
||||
3. **Provide feedback**: Help improve the system by reporting misunderstood queries
|
||||
4. **Combine approaches**: Use NLP for exploration, structured for precision
|
||||
|
||||
## Next Steps
|
||||
|
||||
- [Triple Intelligence Architecture](../architecture/triple-intelligence.md)
|
||||
- [API Reference](../api/README.md)
|
||||
- [Getting Started Guide](./getting-started.md)
|
||||
415
docs/troubleshooting.md
Normal file
415
docs/troubleshooting.md
Normal file
|
|
@ -0,0 +1,415 @@
|
|||
# 🚨 Troubleshooting Guide
|
||||
|
||||
Common issues and solutions for Brainy.
|
||||
|
||||
## 🤖 Model Loading Issues
|
||||
|
||||
### "Failed to load embedding model"
|
||||
|
||||
**Symptoms**: Error during `brain.init()` with model loading failure.
|
||||
|
||||
**Causes & Solutions**:
|
||||
|
||||
1. **No local models + remote downloads blocked**
|
||||
```bash
|
||||
# Solution: Download models manually
|
||||
npm run download-models
|
||||
```
|
||||
|
||||
2. **Network connectivity issues**
|
||||
```bash
|
||||
# Solution: Allow remote models
|
||||
export BRAINY_ALLOW_REMOTE_MODELS=true
|
||||
|
||||
# Or pre-download in connected environment
|
||||
npm run download-models
|
||||
```
|
||||
|
||||
3. **Incorrect model path**
|
||||
```bash
|
||||
# Check if models exist
|
||||
ls ./models/Xenova/all-MiniLM-L6-v2/onnx/model.onnx
|
||||
|
||||
# Set correct path
|
||||
export BRAINY_MODELS_PATH=/correct/path/to/models
|
||||
```
|
||||
|
||||
### Models Download Very Slowly
|
||||
|
||||
**Symptoms**: Long wait times during first initialization.
|
||||
|
||||
**Solutions**:
|
||||
```bash
|
||||
# Pre-download during build/CI
|
||||
npm run download-models
|
||||
|
||||
# For Docker - download during image build
|
||||
RUN npm run download-models
|
||||
```
|
||||
|
||||
### Container Out of Memory During Model Load
|
||||
|
||||
**Symptoms**: OOM errors in Docker/Kubernetes during initialization.
|
||||
|
||||
**Solutions**:
|
||||
```dockerfile
|
||||
# Increase memory limit
|
||||
docker run -m 2g my-app
|
||||
|
||||
# Pre-download models at build time (recommended)
|
||||
RUN npm run download-models
|
||||
|
||||
# Use quantized models (default, but explicit)
|
||||
ENV BRAINY_MODEL_DTYPE=q8
|
||||
```
|
||||
|
||||
## 💾 Storage Issues
|
||||
|
||||
### Permission Denied Creating Storage Directory
|
||||
|
||||
**Symptoms**: EACCES or permission errors when creating storage files.
|
||||
|
||||
**Solutions**:
|
||||
```bash
|
||||
# Make directory writable
|
||||
chmod 755 ./brainy-data
|
||||
|
||||
# Use custom writable path
|
||||
const brain = new BrainyData({
|
||||
storage: {
|
||||
adapter: 'filesystem',
|
||||
path: '/tmp/brainy-data'
|
||||
}
|
||||
})
|
||||
|
||||
# Or use memory storage
|
||||
const brain = new BrainyData({
|
||||
storage: { forceMemoryStorage: true }
|
||||
})
|
||||
```
|
||||
|
||||
### "ENOENT: no such file or directory"
|
||||
|
||||
**Symptoms**: File not found errors during storage operations.
|
||||
|
||||
**Solutions**:
|
||||
```bash
|
||||
# Ensure parent directory exists
|
||||
mkdir -p ./brainy-data
|
||||
|
||||
# Check storage configuration
|
||||
const brain = new BrainyData({
|
||||
storage: {
|
||||
adapter: 'filesystem',
|
||||
path: '/full/path/to/storage' // Use absolute path
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
## 🧠 Initialization Issues
|
||||
|
||||
### Initialization Hangs or Times Out
|
||||
|
||||
**Symptoms**: `brain.init()` never resolves.
|
||||
|
||||
**Possible Causes & Solutions**:
|
||||
|
||||
1. **Model download timeout**
|
||||
```bash
|
||||
# Pre-download models
|
||||
npm run download-models
|
||||
|
||||
# Or force local-only
|
||||
export BRAINY_ALLOW_REMOTE_MODELS=false
|
||||
```
|
||||
|
||||
2. **Network issues**
|
||||
```typescript
|
||||
// Set initialization timeout
|
||||
const brain = new BrainyData()
|
||||
|
||||
// Use Promise.race for timeout
|
||||
const initPromise = Promise.race([
|
||||
brain.init(),
|
||||
new Promise((_, reject) =>
|
||||
setTimeout(() => reject(new Error('Init timeout')), 30000)
|
||||
)
|
||||
])
|
||||
```
|
||||
|
||||
3. **Resource constraints**
|
||||
```bash
|
||||
# Increase memory for Node.js
|
||||
NODE_OPTIONS="--max-old-space-size=4096" npm start
|
||||
```
|
||||
|
||||
## 🔍 Search Issues
|
||||
|
||||
### No Search Results
|
||||
|
||||
**Symptoms**: Empty results from valid queries.
|
||||
|
||||
**Debugging Steps**:
|
||||
|
||||
1. **Check if data exists**
|
||||
```typescript
|
||||
const stats = await brain.getStatistics()
|
||||
console.log(`Total items: ${stats.nounCount}`)
|
||||
```
|
||||
|
||||
2. **Verify embedding generation**
|
||||
```typescript
|
||||
const id = await brain.add("test content")
|
||||
const item = await brain.get(id)
|
||||
console.log('Item:', item) // Should have metadata and vector
|
||||
```
|
||||
|
||||
3. **Test with exact match**
|
||||
```typescript
|
||||
const results = await brain.search("test content") // Exact text
|
||||
console.log('Exact match results:', results)
|
||||
```
|
||||
|
||||
### Poor Search Quality
|
||||
|
||||
**Symptoms**: Irrelevant results, low scores.
|
||||
|
||||
**Improvements**:
|
||||
|
||||
1. **Add more context to queries**
|
||||
```typescript
|
||||
// Instead of: "cat"
|
||||
const results = await brain.search("domestic cat animal pet")
|
||||
```
|
||||
|
||||
2. **Use metadata filtering**
|
||||
```typescript
|
||||
const results = await brain.search("animals", {
|
||||
where: { category: "pets" },
|
||||
limit: 10
|
||||
})
|
||||
```
|
||||
|
||||
3. **Check data quality**
|
||||
```typescript
|
||||
// Ensure consistent, descriptive content
|
||||
await brain.add("Domestic cat - small carnivorous mammal", {
|
||||
category: "animals",
|
||||
subcategory: "pets"
|
||||
})
|
||||
```
|
||||
|
||||
## ⚡ Performance Issues
|
||||
|
||||
### Slow Search Performance
|
||||
|
||||
**Symptoms**: High search latency.
|
||||
|
||||
**Optimizations**:
|
||||
|
||||
1. **Enable search cache**
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
cache: {
|
||||
search: {
|
||||
maxSize: 1000,
|
||||
ttl: 300000 // 5 minutes
|
||||
}
|
||||
}
|
||||
})
|
||||
```
|
||||
|
||||
2. **Use appropriate limits**
|
||||
```typescript
|
||||
// Don't fetch more than needed
|
||||
const results = await brain.search("query", { limit: 10 })
|
||||
```
|
||||
|
||||
3. **Consider field filtering first**
|
||||
```typescript
|
||||
// Filter by metadata first, then semantic search
|
||||
const results = await brain.search("query", {
|
||||
where: { category: "specific" }, // Reduces search space
|
||||
limit: 10
|
||||
})
|
||||
```
|
||||
|
||||
### High Memory Usage
|
||||
|
||||
**Symptoms**: Increasing memory consumption over time.
|
||||
|
||||
**Solutions**:
|
||||
|
||||
1. **Cleanup when done**
|
||||
```typescript
|
||||
await brain.cleanup() // Releases resources
|
||||
```
|
||||
|
||||
2. **Use streaming for large datasets**
|
||||
```typescript
|
||||
// Process in batches instead of loading all at once
|
||||
for (let i = 0; i < data.length; i += 100) {
|
||||
const batch = data.slice(i, i + 100)
|
||||
await Promise.all(batch.map(item => brain.add(item)))
|
||||
}
|
||||
```
|
||||
|
||||
3. **Configure memory limits**
|
||||
```bash
|
||||
NODE_OPTIONS="--max-old-space-size=2048" npm start
|
||||
```
|
||||
|
||||
## 🧪 Testing Issues
|
||||
|
||||
### Tests Fail in CI/CD
|
||||
|
||||
**Symptoms**: Tests pass locally but fail in automated environments.
|
||||
|
||||
**Solutions**:
|
||||
|
||||
1. **Pre-download models in CI**
|
||||
```yaml
|
||||
# .github/workflows/test.yml
|
||||
- name: Download Models
|
||||
run: npm run download-models
|
||||
|
||||
- name: Test with Local Models
|
||||
env:
|
||||
BRAINY_ALLOW_REMOTE_MODELS: false
|
||||
run: npm test
|
||||
```
|
||||
|
||||
2. **Use memory storage in tests**
|
||||
```typescript
|
||||
// In test setup
|
||||
const brain = new BrainyData({
|
||||
storage: { forceMemoryStorage: true }
|
||||
})
|
||||
```
|
||||
|
||||
3. **Increase timeout for CI**
|
||||
```typescript
|
||||
// In test files
|
||||
describe('Brainy tests', () => {
|
||||
it('should work', async () => {
|
||||
// Test code
|
||||
}, { timeout: 30000 }) // 30 second timeout
|
||||
})
|
||||
```
|
||||
|
||||
## 📋 Environment-Specific Issues
|
||||
|
||||
### Browser CORS Errors
|
||||
|
||||
**Symptoms**: Model loading fails in browser due to CORS.
|
||||
|
||||
**Solutions**:
|
||||
```javascript
|
||||
// Brainy handles CORS automatically via CDN
|
||||
// No action needed - models load from CORS-enabled mirrors
|
||||
|
||||
// If using custom model URLs, ensure CORS headers:
|
||||
// Access-Control-Allow-Origin: *
|
||||
```
|
||||
|
||||
### Serverless Cold Start Timeouts
|
||||
|
||||
**Symptoms**: Lambda/Vercel functions timeout during initialization.
|
||||
|
||||
**Solutions**:
|
||||
```dockerfile
|
||||
# Pre-bundle models in deployment
|
||||
RUN npm run download-models
|
||||
|
||||
# Set environment variables
|
||||
ENV BRAINY_ALLOW_REMOTE_MODELS=false
|
||||
ENV BRAINY_MODELS_PATH=./models
|
||||
```
|
||||
|
||||
### Node.js Module Resolution Issues
|
||||
|
||||
**Symptoms**: "Cannot find module" errors.
|
||||
|
||||
**Solutions**:
|
||||
```json
|
||||
// package.json
|
||||
{
|
||||
"type": "module",
|
||||
"exports": {
|
||||
".": {
|
||||
"import": "./dist/index.js",
|
||||
"require": "./dist/index.js"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## 🆘 Getting Help
|
||||
|
||||
### Debug Logging
|
||||
|
||||
Enable verbose logging to see what's happening:
|
||||
|
||||
```typescript
|
||||
const brain = new BrainyData({
|
||||
logging: { verbose: true }
|
||||
})
|
||||
```
|
||||
|
||||
### Health Check
|
||||
|
||||
Verify your Brainy setup:
|
||||
|
||||
```typescript
|
||||
// Basic health check
|
||||
try {
|
||||
const brain = new BrainyData()
|
||||
await brain.init()
|
||||
|
||||
const id = await brain.add("health check")
|
||||
const results = await brain.search("health")
|
||||
|
||||
console.log('✅ Brainy is working correctly')
|
||||
console.log(`Added item: ${id}`)
|
||||
console.log(`Search results: ${results.length}`)
|
||||
|
||||
} catch (error) {
|
||||
console.error('❌ Brainy health check failed:', error)
|
||||
}
|
||||
```
|
||||
|
||||
### Environment Info
|
||||
|
||||
Collect environment information:
|
||||
|
||||
```bash
|
||||
# Node.js version
|
||||
node --version
|
||||
|
||||
# Memory limits
|
||||
node -e "console.log(process.memoryUsage())"
|
||||
|
||||
# Platform info
|
||||
node -e "console.log(process.platform, process.arch)"
|
||||
|
||||
# Brainy models
|
||||
ls -la ./models/Xenova/all-MiniLM-L6-v2/
|
||||
```
|
||||
|
||||
### Report Issues
|
||||
|
||||
When reporting issues, include:
|
||||
|
||||
1. **Environment**: Node.js version, OS, memory
|
||||
2. **Configuration**: Brainy options, environment variables
|
||||
3. **Error logs**: Full error messages and stack traces
|
||||
4. **Reproduction**: Minimal code example that demonstrates the issue
|
||||
|
||||
**Where to report**:
|
||||
- [GitHub Issues](https://github.com/your-repo/brainy/issues)
|
||||
- Include "troubleshooting" label
|
||||
- Use the issue template
|
||||
|
||||
---
|
||||
|
||||
**Still having issues?** Check the [Model Loading Guide](guides/model-loading.md) or [open an issue](https://github.com/your-repo/brainy/issues).
|
||||
Loading…
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