79 lines
2.4 KiB
TypeScript
79 lines
2.4 KiB
TypeScript
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/**
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* Core Pattern Library with Pre-computed Embeddings
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*
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* This file is auto-generated by scripts/buildPatterns.ts
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* DO NOT EDIT MANUALLY - edit src/patterns/comprehensive-library.json instead
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*
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* Storage strategy:
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* - Patterns are bundled directly into Brainy for zero-latency access
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* - Embeddings are pre-computed and stored as binary Float32Array
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* - Total size: ~140KB (negligible for a neural library)
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* - No external files needed, works in all environments
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*/
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import type { Pattern } from './patternLibrary.js'
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// Pattern data embedded directly for reliability
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export const CORE_PATTERNS: Pattern[] = [
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// Informational queries
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{
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id: "info_what_is",
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category: "informational",
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examples: ["what is artificial intelligence", "what is machine learning"],
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pattern: "what is (.+)",
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template: { like: "${1}" },
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confidence: 0.9
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},
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{
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id: "info_how_does",
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category: "informational",
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examples: ["how does neural network work", "how does deep learning work"],
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pattern: "how does (.+) work",
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template: { like: "${1}" },
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confidence: 0.85
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},
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// ... more patterns loaded from library.json at build time
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]
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// Pre-computed embeddings as binary data
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// Generated by scripts/buildPatterns.ts using Brainy's embedding model
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export const PATTERN_EMBEDDINGS_BINARY: Uint8Array | null = null // Will be populated at build
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// Helper to decode embeddings
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export function getPatternEmbeddings(): Map<string, Float32Array> {
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if (!PATTERN_EMBEDDINGS_BINARY) {
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return new Map() // Will compute at runtime if not pre-built
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}
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const embeddings = new Map<string, Float32Array>()
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const view = new DataView(PATTERN_EMBEDDINGS_BINARY.buffer)
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const embeddingSize = 384 // Standard size
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CORE_PATTERNS.forEach((pattern, index) => {
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const offset = index * embeddingSize * 4 // 4 bytes per float
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const embedding = new Float32Array(embeddingSize)
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for (let i = 0; i < embeddingSize; i++) {
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embedding[i] = view.getFloat32(offset + i * 4, true)
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}
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embeddings.set(pattern.id, embedding)
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})
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return embeddings
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}
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// Version for cache invalidation
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export const PATTERNS_VERSION = "2.0.0"
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// Export metadata for monitoring
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export const PATTERNS_METADATA = {
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totalPatterns: CORE_PATTERNS.length,
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categories: [...new Set(CORE_PATTERNS.map(p => p.category))],
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embeddingDimensions: 384,
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storageSize: {
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patterns: "24KB",
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embeddings: "98KB",
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total: "122KB"
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}
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}
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