David Snelling
9a446cd95d
- Enable intelligent verb scoring by default for better relationship quality - Add Write-Ahead Log (WAL) for zero data loss guarantee - Implement request deduplication for 3x concurrent performance - Fix model loading for tests with proper environment setup - Update documentation to highlight new enterprise features BREAKING CHANGE: Intelligent verb scoring now enabled by default
49 lines
No EOL
1.4 KiB
TypeScript
49 lines
No EOL
1.4 KiB
TypeScript
/**
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* Test Setup Configuration
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* Ensures models are available for all tests
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*/
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import { beforeAll } from 'vitest'
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import { existsSync } from 'fs'
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import { join } from 'path'
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beforeAll(() => {
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// Set model path to local models directory
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const modelsPath = join(process.cwd(), 'models')
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// Check if models exist
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if (existsSync(modelsPath)) {
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process.env.BRAINY_MODELS_PATH = modelsPath
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console.log('✅ Using local models for tests:', modelsPath)
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} else {
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console.warn('⚠️ Models directory not found, tests may download models')
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}
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// Disable remote model downloads in tests to avoid network dependencies
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process.env.BRAINY_ALLOW_REMOTE_MODELS = 'false'
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// Set test environment
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process.env.NODE_ENV = 'test'
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// Disable verbose logging in tests
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process.env.BRAINY_LOG_LEVEL = 'error'
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})
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// Export mock embedding function for tests that need it
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export function createMockEmbedding(text: string, dimensions = 384): Float32Array {
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// Create deterministic embeddings based on text hash
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let hash = 0
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for (let i = 0; i < text.length; i++) {
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const char = text.charCodeAt(i)
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hash = ((hash << 5) - hash) + char
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hash = hash & hash // Convert to 32-bit integer
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}
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const embedding = new Float32Array(dimensions)
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for (let i = 0; i < dimensions; i++) {
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// Generate values between -1 and 1 based on hash
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embedding[i] = Math.sin(hash + i) * 0.5
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}
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return embedding
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} |