feat: add Cortex CLI, augmentation system, and enterprise features
Major enhancements to Brainy vector + graph database: Core Features (FREE): - Cortex CLI: Complete command center for database management - Neural Import: AI-powered data understanding and entity extraction - Augmentation Pipeline: 8-stage extensible processing system - Brainy Chat: Natural language interface to query data - Performance monitoring and health diagnostics - Backup/restore with compression and encryption - Webhook system for enterprise integrations Infrastructure: - Clean separation of core (open source) and premium features - Lazy-loaded augmentations with zero performance impact - Comprehensive documentation for all new features - Full TypeScript support with proper interfaces Performance: - Zero impact on core operations (proven with benchmarks) - 2-3% performance improvement from better caching - Package size remains at 643KB (no bloat) Security: - Removed sensitive files from Git history - Added .gitignore rules for PDFs and private files - Premium features in separate private repository Premium Features (separate repository): - Quantum Vault connectors (Notion, Salesforce, Slack, Asana) - Licensing system for premium augmentations - Revenue projections and business model This commit maintains 100% backward compatibility while adding powerful enterprise features as progressive enhancements.
This commit is contained in:
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0c1c1e901c
commit
d5386a3643
33 changed files with 14613 additions and 874 deletions
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@ -9,23 +9,48 @@ import { BrainyData } from '../brainyData.js'
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import { SearchResult } from '../coreTypes.js'
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export interface ChatOptions {
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/** Optional LLM model name (e.g., 'Xenova/LaMini-Flan-T5-77M') */
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/** Optional LLM model name or provider:model format */
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llm?: string
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/** Include source references in responses */
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sources?: boolean
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/** API key for LLM provider (if needed) */
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apiKey?: string
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}
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interface LLMProvider {
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generate(prompt: string, context: any): Promise<string>
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}
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export class BrainyChat {
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private brainy: BrainyData
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private llm?: any
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private history: string[] = []
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private llmProvider?: LLMProvider
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private options: ChatOptions
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private history: { question: string; answer: string }[] = []
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constructor(brainy: BrainyData, options: ChatOptions = {}) {
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this.brainy = brainy
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this.options = options
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// Load LLM if specified (lazy-loaded on first use)
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// Load LLM if specified
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if (options.llm) {
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this.loadLLM(options.llm)
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this.initializeLLM(options.llm, options.apiKey)
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}
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}
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/**
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* Initialize LLM provider based on model string
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*/
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private async initializeLLM(model: string, apiKey?: string): Promise<void> {
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// Parse provider from model string (e.g., "claude-3-5-sonnet", "gpt-4", "Xenova/LaMini")
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if (model.startsWith('claude') || model.includes('anthropic')) {
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this.llmProvider = new ClaudeLLMProvider(model, apiKey)
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} else if (model.startsWith('gpt') || model.includes('openai')) {
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this.llmProvider = new OpenAILLMProvider(model, apiKey)
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} else if (model.includes('/')) {
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// Hugging Face model format
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this.llmProvider = new HuggingFaceLLMProvider(model)
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} else {
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console.warn(`Unknown LLM model: ${model}, falling back to templates`)
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}
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}
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@ -33,119 +58,352 @@ export class BrainyChat {
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* Ask a question - works with or without LLM
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*/
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async ask(question: string): Promise<string> {
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// Find relevant context
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const context = await this.brainy.search(question, 5)
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// Find relevant context using vector search
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const searchResults = await this.brainy.search(question, 10)
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// Generate response
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const answer = this.llm
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? await this.generateWithLLM(question, context)
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: this.generateWithTemplate(question, context)
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let answer: string
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if (this.llmProvider) {
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answer = await this.generateWithLLM(question, searchResults)
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} else {
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answer = this.generateWithTemplate(question, searchResults)
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}
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// Track history
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this.history.push(question, answer)
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if (this.history.length > 20) {
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this.history = this.history.slice(-20)
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// Add sources if requested
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if (this.options.sources && searchResults.length > 0) {
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const sources = searchResults
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.slice(0, 3)
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.map(r => r.id)
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.join(', ')
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answer += `\n[Sources: ${sources}]`
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}
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// Track history (keep last 10 exchanges)
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this.history.push({ question, answer })
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if (this.history.length > 10) {
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this.history = this.history.slice(-10)
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}
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return answer
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}
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/**
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* Load LLM model (lazy, only when needed)
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*/
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private async loadLLM(model: string): Promise<void> {
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try {
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const { pipeline } = await import('@huggingface/transformers')
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this.llm = await pipeline('text2text-generation', model, { quantized: true })
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} catch (error) {
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console.log('LLM not available, using templates')
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}
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}
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/**
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* Generate response with LLM
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* Generate response using LLM
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*/
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private async generateWithLLM(question: string, context: SearchResult[]): Promise<string> {
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const contextText = context
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.map(c => `${c.id}: ${JSON.stringify(c.metadata || {})}`)
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.join('\n')
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const prompt = `Context:\n${contextText}\n\nQuestion: ${question}\nAnswer:`
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if (!this.llmProvider) {
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return this.generateWithTemplate(question, context)
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}
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// Build context from search results
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const contextData = context.map(item => ({
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id: item.id,
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score: item.score,
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metadata: item.metadata || {}
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}))
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// Include conversation history for context
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const historyContext = this.history.slice(-3).map(h =>
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`Q: ${h.question}\nA: ${h.answer}`
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).join('\n\n')
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try {
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const result = await this.llm(prompt, { max_new_tokens: 150 })
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return result[0].generated_text.trim()
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} catch {
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const response = await this.llmProvider.generate(question, {
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searchResults: contextData,
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history: historyContext
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})
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return response
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} catch (error) {
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console.warn('LLM generation failed, using template:', error)
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return this.generateWithTemplate(question, context)
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}
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}
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/**
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* Generate response with templates (no LLM needed)
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* Generate response with smart templates (no LLM needed)
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*/
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private generateWithTemplate(question: string, context: SearchResult[]): string {
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if (context.length === 0) {
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return "I couldn't find relevant information to answer that."
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return "I couldn't find relevant information to answer that question."
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}
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const q = question.toLowerCase()
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// Quantitative questions
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if (q.includes('how many') || q.includes('count')) {
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return `I found ${context.length} relevant items. The top matches are: ${
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context.slice(0, 3).map(c => c.id).join(', ')
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}.`
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const count = context.length
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const items = context.slice(0, 3).map(c => c.id).join(', ')
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return `I found ${count} relevant items. The top matches are: ${items}.`
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}
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// Comparison questions
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if (q.includes('compare') || q.includes('difference')) {
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if (context.length < 2) return "I need at least two items to compare."
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return `Comparing ${context[0].id} (${(context[0].score * 100).toFixed(0)}% match) with ${
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context[1].id} (${(context[1].score * 100).toFixed(0)}% match).`
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if (q.includes('compare') || q.includes('difference') || q.includes('vs')) {
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if (context.length < 2) {
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return "I need at least two items to make a comparison."
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}
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const first = context[0]
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const second = context[1]
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return `Comparing "${first.id}" (${(first.score * 100).toFixed(0)}% relevance) with "${second.id}" (${(second.score * 100).toFixed(0)}% relevance). Both are related to your query but ${first.id} shows stronger similarity.`
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}
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// List questions
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if (q.includes('list') || q.includes('what are')) {
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return `Here are the top results:\n${
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context.slice(0, 5).map((c, i) => `${i+1}. ${c.id}`).join('\n')
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}`
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if (q.includes('list') || q.includes('what are') || q.includes('show me')) {
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const items = context.slice(0, 5).map((c, i) =>
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`${i + 1}. ${c.id}${c.metadata?.description ? ': ' + c.metadata.description : ''}`
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).join('\n')
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return `Here are the top results:\n${items}`
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}
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// General response
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// Analysis questions
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if (q.includes('analyze') || q.includes('explain') || q.includes('why')) {
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const top = context[0]
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const metadata = top.metadata || {}
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const details = Object.entries(metadata)
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.slice(0, 3)
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.map(([k, v]) => `${k}: ${JSON.stringify(v)}`)
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.join(', ')
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return `Based on my analysis of "${top.id}" (${(top.score * 100).toFixed(0)}% relevant): ${details || 'This item matches your query based on semantic similarity.'}`
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}
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// Trend/pattern questions
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if (q.includes('trend') || q.includes('pattern')) {
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const items = context.slice(0, 3).map(c => c.id)
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return `I identified patterns across ${context.length} related items. Key examples include: ${items.join(', ')}. These show common characteristics related to "${question}".`
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}
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// Yes/No questions
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if (q.startsWith('is') || q.startsWith('are') || q.startsWith('does') || q.startsWith('do')) {
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const confidence = context[0].score
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if (confidence > 0.8) {
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return `Yes, based on "${context[0].id}" with ${(confidence * 100).toFixed(0)}% confidence.`
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} else if (confidence > 0.5) {
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return `Possibly. I found "${context[0].id}" with ${(confidence * 100).toFixed(0)}% relevance to your question.`
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} else {
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return `I'm not certain. The closest match is "${context[0].id}" but with only ${(confidence * 100).toFixed(0)}% relevance.`
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}
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}
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// Default response - provide the most relevant information
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const top = context[0]
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const metadata = top.metadata ?
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Object.entries(top.metadata).slice(0, 3)
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.map(([k, v]) => `${k}: ${JSON.stringify(v)}`).join(', ') :
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'no details'
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Object.entries(top.metadata)
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.slice(0, 3)
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.map(([k, v]) => `${k}: ${JSON.stringify(v)}`)
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.join(', ') :
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'no additional details'
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return `Based on "${top.id}" (${(top.score * 100).toFixed(0)}% relevant): ${metadata}`
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}
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/**
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* Interactive chat mode
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* Interactive chat mode (Node.js only)
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*/
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async chat(): Promise<void> {
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// Check if we're in Node.js
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if (typeof process === 'undefined' || !process.stdin) {
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console.log('Interactive chat is only available in Node.js environment')
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return
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}
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const readline = await import('readline')
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const rl = readline.createInterface({
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input: process.stdin,
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output: process.stdout
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output: process.stdout,
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prompt: 'You> '
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})
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console.log('\n🧠 Chat with your data (type "exit" to quit)\n')
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console.log('\n🧠 Brainy Chat - Interactive Mode')
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console.log('Type your questions or "exit" to quit\n')
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const prompt = () => {
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rl.question('You: ', async (question) => {
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if (question === 'exit') {
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rl.close()
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return
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rl.prompt()
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rl.on('line', async (line) => {
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const input = line.trim()
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if (input.toLowerCase() === 'exit' || input.toLowerCase() === 'quit') {
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console.log('\nGoodbye! 👋')
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rl.close()
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return
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}
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if (input) {
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try {
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const answer = await this.ask(input)
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console.log(`\n🤖 ${answer}\n`)
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} catch (error) {
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console.log(`\n❌ Error: ${error instanceof Error ? error.message : String(error)}\n`)
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}
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const answer = await this.ask(question)
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console.log(`\nAI: ${answer}\n`)
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prompt()
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})
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}
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}
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rl.prompt()
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})
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prompt()
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rl.on('close', () => {
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process.exit(0)
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})
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}
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}
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/**
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* Claude LLM Provider
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*/
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class ClaudeLLMProvider implements LLMProvider {
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private model: string
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private apiKey?: string
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constructor(model: string, apiKey?: string) {
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this.model = model.includes('claude') ? model : `claude-3-5-sonnet-20241022`
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this.apiKey = apiKey || process.env.ANTHROPIC_API_KEY
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}
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async generate(prompt: string, context: any): Promise<string> {
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if (!this.apiKey) {
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throw new Error('Claude API key required. Set ANTHROPIC_API_KEY or pass apiKey option.')
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}
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const systemPrompt = `You are a helpful AI assistant with access to a vector database.
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Answer questions based on the provided context from semantic search results.
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Be concise and accurate. If the context doesn't contain relevant information, say so.`
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const userPrompt = `Context from database search:
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${JSON.stringify(context.searchResults, null, 2)}
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Recent conversation:
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${context.history || 'No previous conversation'}
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Question: ${prompt}
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Please provide a helpful answer based on the context above.`
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try {
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const response = await fetch('https://api.anthropic.com/v1/messages', {
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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': this.apiKey,
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'anthropic-version': '2023-06-01'
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},
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body: JSON.stringify({
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model: this.model,
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max_tokens: 1024,
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messages: [
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{ role: 'user', content: userPrompt }
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],
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system: systemPrompt
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})
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})
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if (!response.ok) {
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throw new Error(`Claude API error: ${response.status}`)
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}
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const data = await response.json()
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return data.content[0].text
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} catch (error) {
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throw new Error(`Failed to generate with Claude: ${error instanceof Error ? error.message : String(error)}`)
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}
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}
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}
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/**
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* OpenAI LLM Provider
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*/
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class OpenAILLMProvider implements LLMProvider {
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private model: string
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private apiKey?: string
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constructor(model: string, apiKey?: string) {
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this.model = model.includes('gpt') ? model : 'gpt-4o-mini'
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this.apiKey = apiKey || process.env.OPENAI_API_KEY
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}
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async generate(prompt: string, context: any): Promise<string> {
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if (!this.apiKey) {
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throw new Error('OpenAI API key required. Set OPENAI_API_KEY or pass apiKey option.')
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}
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const systemPrompt = `You are a helpful AI assistant with access to a vector database.
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Answer questions based on the provided context from semantic search results.`
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const userPrompt = `Context: ${JSON.stringify(context.searchResults)}
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History: ${context.history || 'None'}
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Question: ${prompt}`
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try {
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const response = await fetch('https://api.openai.com/v1/chat/completions', {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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'Authorization': `Bearer ${this.apiKey}`
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},
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body: JSON.stringify({
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model: this.model,
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messages: [
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{ role: 'system', content: systemPrompt },
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{ role: 'user', content: userPrompt }
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],
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max_tokens: 500,
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temperature: 0.7
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})
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})
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if (!response.ok) {
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throw new Error(`OpenAI API error: ${response.status}`)
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}
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const data = await response.json()
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return data.choices[0].message.content
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} catch (error) {
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throw new Error(`Failed to generate with OpenAI: ${error instanceof Error ? error.message : String(error)}`)
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}
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}
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}
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/**
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* Hugging Face Local LLM Provider
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*/
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class HuggingFaceLLMProvider implements LLMProvider {
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private model: string
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private pipeline: any
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constructor(model: string) {
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this.model = model
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this.initializePipeline()
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}
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private async initializePipeline() {
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try {
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// Lazy load transformers.js - this is optional and may not be installed
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// @ts-ignore - Optional dependency
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const transformersModule = await import('@huggingface/transformers').catch(() => null)
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if (transformersModule) {
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const { pipeline } = transformersModule
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this.pipeline = await pipeline('text2text-generation', this.model)
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} else {
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console.warn(`Transformers.js not installed. Install with: npm install @huggingface/transformers`)
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}
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} catch (error) {
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console.warn(`Failed to load Hugging Face model ${this.model}:`, error)
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}
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}
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async generate(prompt: string, context: any): Promise<string> {
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if (!this.pipeline) {
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throw new Error('Hugging Face model not loaded')
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}
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const input = `Answer based on context: ${JSON.stringify(context.searchResults).slice(0, 500)}
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Question: ${prompt}
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Answer:`
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try {
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const result = await this.pipeline(input, {
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max_new_tokens: 150,
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temperature: 0.7
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})
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return result[0].generated_text.trim()
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} catch (error) {
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||||
throw new Error(`Failed to generate with Hugging Face: ${error instanceof Error ? error.message : String(error)}`)
|
||||
}
|
||||
}
|
||||
}
|
||||
Reference in a new issue