dpsifr
2322b53a0c
- **Core**: Introduced a new `getStatistics` utility function in `statistics.ts` for fetching database statistics at the root level of the library. Enhanced `BrainyData` methods to ensure metadata includes `id` field and refined statistics calculations, excluding verbs from the noun count. - **Tests**: Added comprehensive test coverage in `statistics.test.ts` for the new utility function, validating proper error handling, statistics accuracy, and consistent results between instance methods and standalone function. - **Storage Config**: Enabled dynamic support for AWS S3, Cloudflare R2, and Google Cloud Storage in web service configuration, utilizing environment variables for adapter setup. Addressed a race condition in `FileSystemStorage` initialization by deferring path module imports. **Purpose**: Enhance database analytics by introducing a reusable `getStatistics` function, improve flexibility in storage configuration, and ensure robust testing for reliability and accuracy.
393 lines
12 KiB
TypeScript
393 lines
12 KiB
TypeScript
/**
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* Core Functionality Tests
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* Tests core Brainy features as a consumer would use them
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*/
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import { describe, it, expect, beforeAll } from 'vitest'
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describe('Brainy Core Functionality', () => {
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let brainy: any
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beforeAll(async () => {
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// Load brainy library as a consumer would
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brainy = await import('../dist/unified.js')
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})
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describe('Library Exports', () => {
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it('should export BrainyData class', () => {
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expect(brainy.BrainyData).toBeDefined()
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expect(typeof brainy.BrainyData).toBe('function')
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})
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it('should export environment detection functions', () => {
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expect(typeof brainy.isBrowser).toBe('function')
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expect(typeof brainy.isNode).toBe('function')
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expect(typeof brainy.isWebWorker).toBe('function')
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expect(typeof brainy.areWebWorkersAvailable).toBe('function')
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expect(typeof brainy.isThreadingAvailable).toBe('function')
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})
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it('should export embedding function creator', () => {
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expect(typeof brainy.createEmbeddingFunction).toBe('function')
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})
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it('should export environment object', () => {
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expect(brainy.environment).toBeDefined()
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expect(typeof brainy.environment).toBe('object')
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expect(brainy.environment).toHaveProperty('isBrowser')
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expect(brainy.environment).toHaveProperty('isNode')
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expect(brainy.environment).toHaveProperty('isServerless')
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})
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})
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describe('BrainyData Configuration', () => {
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it('should create instance with minimal configuration', () => {
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const data = new brainy.BrainyData({
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dimensions: 3
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})
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expect(data).toBeDefined()
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expect(data.dimensions).toBe(3)
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})
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it('should create instance with full configuration', () => {
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const data = new brainy.BrainyData({
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dimensions: 128,
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metric: 'cosine',
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maxConnections: 32,
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efConstruction: 200,
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storage: 'memory'
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})
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expect(data).toBeDefined()
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expect(data.dimensions).toBe(128)
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})
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it('should validate configuration parameters', () => {
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expect(() => {
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new brainy.BrainyData({
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dimensions: 0 // Invalid dimensions
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})
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}).toThrow()
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expect(() => {
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new brainy.BrainyData({
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dimensions: -1 // Invalid dimensions
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})
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}).toThrow()
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})
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it('should use default values for optional parameters', () => {
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const data = new brainy.BrainyData({
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dimensions: 10
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})
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expect(data.dimensions).toBe(10)
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// Should have reasonable defaults for other parameters
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expect(data.maxConnections).toBeGreaterThan(0)
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expect(data.efConstruction).toBeGreaterThan(0)
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})
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})
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describe('Vector Operations', () => {
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it('should handle vector addition and search', async () => {
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const data = new brainy.BrainyData({
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dimensions: 3,
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metric: 'euclidean'
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})
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await data.init()
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await data.clear() // Clear any existing data
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// Add vectors
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await data.add([1, 0, 0], { id: 'v1', label: 'x-axis' })
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await data.add([0, 1, 0], { id: 'v2', label: 'y-axis' })
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await data.add([0, 0, 1], { id: 'v3', label: 'z-axis' })
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// Search for similar vector
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const results = await data.search([1, 0, 0], 1)
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expect(results).toBeDefined()
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expect(results.length).toBe(1)
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expect(results[0].metadata.id).toBe('v1')
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})
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it('should handle batch vector operations', async () => {
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const data = new brainy.BrainyData({
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dimensions: 2,
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metric: 'euclidean'
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})
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await data.init()
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await data.clear() // Clear any existing data
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// Add multiple vectors
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const vectors = [
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{ vector: [1, 1], metadata: { id: 'batch1' } },
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{ vector: [2, 2], metadata: { id: 'batch2' } },
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{ vector: [3, 3], metadata: { id: 'batch3' } }
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]
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for (const { vector, metadata } of vectors) {
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await data.add(vector, metadata)
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}
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// Search should return results
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const results = await data.search([1.5, 1.5], 3)
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expect(results.length).toBe(3)
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})
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it('should handle different distance metrics', async () => {
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const euclideanData = new brainy.BrainyData({
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dimensions: 2,
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metric: 'euclidean'
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})
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const cosineData = new brainy.BrainyData({
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dimensions: 2,
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metric: 'cosine'
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})
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await euclideanData.init()
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await cosineData.init()
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// Clear any existing data to ensure test isolation
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await euclideanData.clear()
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await cosineData.clear()
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const vector = [1, 1]
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const metadata = { id: 'test' }
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await euclideanData.add(vector, metadata)
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await cosineData.add(vector, metadata)
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const euclideanResults = await euclideanData.search(vector, 1)
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const cosineResults = await cosineData.search(vector, 1)
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expect(euclideanResults.length).toBe(1)
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expect(cosineResults.length).toBe(1)
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// Both should find the exact match, but distances might differ
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expect(euclideanResults[0].metadata.id).toBe('test')
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expect(cosineResults[0].metadata.id).toBe('test')
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})
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})
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describe('Text Processing', () => {
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it(
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'should handle text items with embedding function',
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async () => {
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const embeddingFunction = brainy.createEmbeddingFunction()
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const data = new brainy.BrainyData({
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embeddingFunction,
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dimensions: 512, // Universal Sentence Encoder produces 512-dimensional vectors
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metric: 'cosine',
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storage: {
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forceMemoryStorage: true
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}
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})
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await data.init()
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// Add text items
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await data.addItem('Hello world', { id: 'greeting', type: 'text' })
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await data.addItem('Goodbye world', { id: 'farewell', type: 'text' })
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// Search with text
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const results = await data.search('Hi there', 1)
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expect(results).toBeDefined()
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expect(results.length).toBeGreaterThan(0)
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expect(results[0].metadata).toHaveProperty('id')
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},
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globalThis.testUtils?.timeout || 30000
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)
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it(
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'should handle mixed vector and text operations',
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async () => {
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const embeddingFunction = brainy.createEmbeddingFunction()
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const data = new brainy.BrainyData({
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embeddingFunction,
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dimensions: 512, // Universal Sentence Encoder produces 512-dimensional vectors
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metric: 'cosine'
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})
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await data.init()
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// Add text item
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await data.addItem('Machine learning', { id: 'text1', type: 'text' })
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// Add vector item (using embedding of similar text)
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const embedding = await embeddingFunction('Artificial intelligence')
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await data.add(embedding, { id: 'vector1', type: 'vector' })
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// Search should find both
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const results = await data.search('AI and ML', 2)
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expect(results).toBeDefined()
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expect(results.length).toBeGreaterThan(0)
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},
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globalThis.testUtils?.timeout || 30000
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)
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})
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describe('Error Handling', () => {
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it('should handle invalid vector dimensions', async () => {
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const data = new brainy.BrainyData({
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dimensions: 3,
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metric: 'euclidean'
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})
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await data.init()
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// Try to add vector with wrong dimensions
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await expect(data.add([1, 2], { id: 'wrong' })).rejects.toThrow()
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await expect(data.add([1, 2, 3, 4], { id: 'wrong' })).rejects.toThrow()
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})
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it('should handle search before initialization', async () => {
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const data = new brainy.BrainyData({
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dimensions: 2,
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metric: 'euclidean'
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})
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// Try to search without initialization
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await expect(data.search([1, 2], 1)).rejects.toThrow()
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})
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it('should handle empty search results gracefully', async () => {
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const data = new brainy.BrainyData({
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dimensions: 2,
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metric: 'euclidean'
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})
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await data.init()
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await data.clear() // Clear any existing data
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// Search in empty database
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const results = await data.search([1, 2], 1)
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expect(results).toBeDefined()
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expect(Array.isArray(results)).toBe(true)
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expect(results.length).toBe(0)
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})
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})
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describe('Performance and Scalability', () => {
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it('should handle moderate number of vectors efficiently', async () => {
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const data = new brainy.BrainyData({
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dimensions: 10,
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metric: 'euclidean'
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})
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await data.init()
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const startTime = Date.now()
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// Add 100 test vectors
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for (let i = 0; i < 100; i++) {
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const vector =
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globalThis.testUtils?.createTestVector(10) ||
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Array.from({ length: 10 }, (_, i) => (i + 1) / 10)
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await data.add(vector, { id: `item_${i}`, index: i })
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}
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const addTime = Date.now() - startTime
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// Search should be fast
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const searchStart = Date.now()
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const results = await data.search(
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globalThis.testUtils?.createTestVector(10) ||
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Array.from({ length: 10 }, (_, i) => (i + 1) / 10),
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10
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)
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const searchTime = Date.now() - searchStart
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expect(results.length).toBeLessThanOrEqual(10)
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expect(addTime).toBeLessThan(10000) // Should complete within 10 seconds
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expect(searchTime).toBeLessThan(1000) // Search should be under 1 second
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})
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it('should maintain search quality with more data', async () => {
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// Create database with proper configuration for testing
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const db = new brainy.BrainyData({
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embeddingFunction: brainy.createEmbeddingFunction(),
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metric: 'cosine'
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})
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await db.init()
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await db.clear() // Clear any existing data
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// Add known data
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await db.add('known data', { id: 'known' })
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// Add noise data
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for (let i = 0; i < 100; i++) {
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await db.add(`noise_${i}`, { id: `noise_${i}` })
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}
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// Perform search using the correct method
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const results = await db.search('known data', 10)
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// Debugging output
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console.log(
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'Search results:',
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results.map((r) => r.metadata?.id)
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)
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// Assertions
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expect(results.length).toBeGreaterThan(0)
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expect(results[0].metadata?.id).toBe('known')
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})
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})
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describe('Database Statistics', () => {
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it('should provide accurate statistics about the database', async () => {
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const data = new brainy.BrainyData({
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dimensions: 3,
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metric: 'euclidean'
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})
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await data.init()
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await data.clear() // Clear any existing data
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// Add some vectors (nouns)
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await data.add([1, 0, 0], { id: 'v1', label: 'x-axis' })
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await data.add([0, 1, 0], { id: 'v2', label: 'y-axis' })
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await data.add([0, 0, 1], { id: 'v3', label: 'z-axis' })
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// Add some connections (verbs)
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await data.connect('v1', 'v2', 'related_to')
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await data.connect('v2', 'v3', 'related_to')
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// Get statistics
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const stats = await data.getStatistics()
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// Debug: Log all nouns in the database
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const allNouns = await data.getAllNouns()
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console.log('All nouns in database:', allNouns.map(n => n.id))
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// Debug: Log all verbs in the database
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const allVerbs = await data.getAllVerbs()
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console.log('All verbs in database:', allVerbs.map(v => v.id))
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// Debug: Log the verb IDs set used in getStatistics
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const verbIds = new Set(allVerbs.map(verb => verb.id))
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console.log('Verb IDs set:', Array.from(verbIds))
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// Verify statistics
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expect(stats).toBeDefined()
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expect(stats).toHaveProperty('nounCount')
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expect(stats).toHaveProperty('verbCount')
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expect(stats).toHaveProperty('metadataCount')
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expect(stats).toHaveProperty('hnswIndexSize')
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// Verify counts
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expect(stats.nounCount).toBe(3)
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expect(stats.verbCount).toBe(2)
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expect(stats.hnswIndexSize).toBe(3)
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})
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})
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})
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