brainy/tests/custom-models-path.test.ts

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/**
* Custom Models Path Test
*
* Tests the custom models directory functionality for Docker deployments
*/
import { describe, it, expect, beforeAll, afterAll, vi } from 'vitest'
import { RobustModelLoader } from '../src/utils/robustModelLoader.js'
import { writeFile, mkdir, rm } from 'fs/promises'
import { join } from 'path'
import { tmpdir } from 'os'
describe('Custom Models Path', () => {
let tempDir: string
let originalEnv: string | undefined
beforeAll(() => {
// Save original environment variable
originalEnv = process.env.BRAINY_MODELS_PATH
})
afterAll(() => {
// Restore original environment variable
if (originalEnv !== undefined) {
process.env.BRAINY_MODELS_PATH = originalEnv
} else {
delete process.env.BRAINY_MODELS_PATH
}
})
beforeEach(async () => {
// Create temporary directory for each test
tempDir = join(tmpdir(), `brainy-test-${Date.now()}`)
await mkdir(tempDir, { recursive: true })
})
afterEach(async () => {
// Clean up temporary directory
try {
await rm(tempDir, { recursive: true, force: true })
} catch (error) {
console.error('Cleanup error:', error)
}
})
it('should use BRAINY_MODELS_PATH environment variable', async () => {
// Set environment variable
process.env.BRAINY_MODELS_PATH = tempDir
const loader = new RobustModelLoader({ verbose: true })
expect((loader as any).options.customModelsPath).toBe(tempDir)
})
it('should use MODELS_PATH environment variable as fallback', async () => {
// Clear BRAINY_MODELS_PATH and set MODELS_PATH
delete process.env.BRAINY_MODELS_PATH
process.env.MODELS_PATH = tempDir
const loader = new RobustModelLoader({ verbose: true })
expect((loader as any).options.customModelsPath).toBe(tempDir)
// Clean up
delete process.env.MODELS_PATH
})
it('should prioritize customModelsPath option over environment variables', async () => {
process.env.BRAINY_MODELS_PATH = '/env/path'
const customPath = '/custom/path'
const loader = new RobustModelLoader({
customModelsPath: customPath,
verbose: true
})
expect((loader as any).options.customModelsPath).toBe(customPath)
})
it('should check multiple subdirectories for models', async () => {
const loader = new RobustModelLoader({
customModelsPath: tempDir,
verbose: true
})
// Create a mock model.json in one of the expected subdirectories
const modelDir = join(tempDir, 'universal-sentence-encoder')
await mkdir(modelDir, { recursive: true })
const mockModelJson = {
format: 'tfjs-graph-model',
modelTopology: {},
weightsManifest: []
}
await writeFile(
join(modelDir, 'model.json'),
JSON.stringify(mockModelJson, null, 2)
)
// Mock the tryLoadFromCustomPath method to avoid actual TensorFlow loading
const tryLoadSpy = vi.spyOn(loader as any, 'tryLoadFromCustomPath')
tryLoadSpy.mockResolvedValue(null) // Return null to avoid complex mocking
await (loader as any).tryLoadLocalBundledModel()
// Verify the method was called with the correct path
expect(tryLoadSpy).toHaveBeenCalledWith(tempDir)
})
it('should log helpful messages when checking custom path', async () => {
const consoleSpy = vi.spyOn(console, 'log').mockImplementation(() => {})
const loader = new RobustModelLoader({
customModelsPath: tempDir,
verbose: true
})
try {
await (loader as any).tryLoadLocalBundledModel()
} catch (error) {
// Expected in test environment
}
// Check that it logged the custom path check
expect(consoleSpy).toHaveBeenCalledWith(
expect.stringContaining(`Checking custom models directory: ${tempDir}`)
)
consoleSpy.mockRestore()
})
it('should handle non-existent custom paths gracefully', async () => {
const nonExistentPath = '/this/path/does/not/exist'
const consoleSpy = vi.spyOn(console, 'log').mockImplementation(() => {})
const loader = new RobustModelLoader({
customModelsPath: nonExistentPath,
verbose: true
})
const result = await (loader as any).tryLoadFromCustomPath(nonExistentPath)
expect(result).toBeNull()
// Should log that no model was found
expect(consoleSpy).toHaveBeenCalledWith(
expect.stringContaining(`No model found in custom path: ${nonExistentPath}`)
)
consoleSpy.mockRestore()
})
it('should provide helpful warning messages about custom paths', async () => {
const consoleSpy = vi.spyOn(console, 'warn').mockImplementation(() => {})
// Test without any custom path set
const loader = new RobustModelLoader({ verbose: false })
try {
await loader.loadModelWithFallbacks()
} catch (error) {
// Expected in test environment without actual models
}
feat\!: migrate from TensorFlow.js to Transformers.js with ONNX Runtime BREAKING CHANGE: Complete migration from TensorFlow.js to Transformers.js for embedding generation This is a major architectural change that replaces TensorFlow.js (USE model) with Transformers.js (all-MiniLM-L6-v2) for significantly improved performance and reduced complexity. Key Changes: - Replace TensorFlow.js Universal Sentence Encoder with Transformers.js all-MiniLM-L6-v2 - Reduce model size from 525MB to 87MB (83% reduction) - Reduce embedding dimensions from 512 to 384 (faster distance calculations) - Remove TensorFlow.js Float32Array patching (caused ONNX conflicts) - Implement smart bundled model detection for offline operation - Add explicit model download script for Docker deployments - Remove complex environment variables in favor of simple configuration - Update all distance functions to use optimized pure JavaScript - Remove TensorFlow-specific utilities and type definitions Performance Improvements: - Model loading: 5x faster (87MB vs 525MB) - Memory usage: 75% reduction (~200-400MB vs ~1.5GB) - Distance calculations: Faster pure JS vs GPU overhead for small vectors - Cold start performance: Significantly improved Files Changed: - Updated package.json: New dependencies, simplified scripts - Rewrote src/utils/embedding.ts: Complete Transformers.js implementation - Updated src/utils/distance.ts: Optimized JavaScript distance functions - Simplified src/setup.ts: Removed TensorFlow-specific patching - Simplified src/utils/textEncoding.ts: Only Node.js TextEncoder/Decoder patches - Deleted src/utils/robustModelLoader.ts: TensorFlow-specific loader - Deleted src/types/tensorflowTypes.ts: TensorFlow type definitions - Added scripts/download-models.cjs: Docker-compatible model downloader - Added comprehensive documentation: README.md, OFFLINE_MODELS.md, analysis docs Testing: - All 19 tests passing - Removed test mocking in favor of real implementation testing - Updated test environment for Transformers.js compatibility - Performance tests validate improved efficiency This migration resolves production issues with Docker egress limitations and provides a more robust, performant foundation for vector operations.
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// Check that the warning mentions the custom path option or brainy-models
const warnCalls = consoleSpy.mock.calls.flat()
const hasCustomPathMention = warnCalls.some(call =>
feat\!: migrate from TensorFlow.js to Transformers.js with ONNX Runtime BREAKING CHANGE: Complete migration from TensorFlow.js to Transformers.js for embedding generation This is a major architectural change that replaces TensorFlow.js (USE model) with Transformers.js (all-MiniLM-L6-v2) for significantly improved performance and reduced complexity. Key Changes: - Replace TensorFlow.js Universal Sentence Encoder with Transformers.js all-MiniLM-L6-v2 - Reduce model size from 525MB to 87MB (83% reduction) - Reduce embedding dimensions from 512 to 384 (faster distance calculations) - Remove TensorFlow.js Float32Array patching (caused ONNX conflicts) - Implement smart bundled model detection for offline operation - Add explicit model download script for Docker deployments - Remove complex environment variables in favor of simple configuration - Update all distance functions to use optimized pure JavaScript - Remove TensorFlow-specific utilities and type definitions Performance Improvements: - Model loading: 5x faster (87MB vs 525MB) - Memory usage: 75% reduction (~200-400MB vs ~1.5GB) - Distance calculations: Faster pure JS vs GPU overhead for small vectors - Cold start performance: Significantly improved Files Changed: - Updated package.json: New dependencies, simplified scripts - Rewrote src/utils/embedding.ts: Complete Transformers.js implementation - Updated src/utils/distance.ts: Optimized JavaScript distance functions - Simplified src/setup.ts: Removed TensorFlow-specific patching - Simplified src/utils/textEncoding.ts: Only Node.js TextEncoder/Decoder patches - Deleted src/utils/robustModelLoader.ts: TensorFlow-specific loader - Deleted src/types/tensorflowTypes.ts: TensorFlow type definitions - Added scripts/download-models.cjs: Docker-compatible model downloader - Added comprehensive documentation: README.md, OFFLINE_MODELS.md, analysis docs Testing: - All 19 tests passing - Removed test mocking in favor of real implementation testing - Updated test environment for Transformers.js compatibility - Performance tests validate improved efficiency This migration resolves production issues with Docker egress limitations and provides a more robust, performant foundation for vector operations.
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typeof call === 'string' && (
call.includes('BRAINY_MODELS_PATH') ||
call.includes('customModelsPath') ||
call.includes('@soulcraft/brainy-models')
)
)
expect(hasCustomPathMention).toBe(true)
consoleSpy.mockRestore()
})
})