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**test(tests): enhance test clarity, isolation, and robustness**

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- **Test Improvements**:
  - Introduced data-clearing steps (`.clear()`) across critical test cases for ensuring better test isolation and preventing state leakage.
  - Extended support for overriding global utilities (`testUtils`) and added fallback behaviors for test vector creation.

- **Configuration Updates**:
  - Added support for `distanceFunction` as an alternative to `metric` in vector operations for consistency.
  - Adjusted and unified asynchronous `timeout` handling across test suites for predictability.

- **Purpose**:
  - These updates improve reliability, maintainability, and clarity in test cases while ensuring compatibility across diverse test environments.
This commit is contained in:
David Snelling 2025-07-16 11:39:53 -07:00
parent 387179f370
commit bcbaf4a678
5 changed files with 170 additions and 126 deletions
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97
tests/core.test.ts
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@ -97,6 +97,7 @@ describe('Brainy Core Functionality', () => {
})
await data.init()
await data.clear() // Clear any existing data
// Add vectors
await data.add([1, 0, 0], { id: 'v1', label: 'x-axis' })
@ -118,6 +119,7 @@ describe('Brainy Core Functionality', () => {
})
await data.init()
await data.clear() // Clear any existing data
// Add multiple vectors
const vectors = [
@ -148,6 +150,10 @@ describe('Brainy Core Functionality', () => {
await euclideanData.init()
await cosineData.init()
// Clear any existing data to ensure test isolation
await euclideanData.clear()
await cosineData.clear()
const vector = [1, 1]
const metadata = { id: 'test' }
@ -168,51 +174,61 @@ describe('Brainy Core Functionality', () => {
})
describe('Text Processing', () => {
it('should handle text items with embedding function', async () => {
const embeddingFunction = brainy.createEmbeddingFunction()
it(
'should handle text items with embedding function',
async () => {
const embeddingFunction = brainy.createEmbeddingFunction()
const data = new brainy.BrainyData({
embeddingFunction,
metric: 'cosine'
})
const data = new brainy.BrainyData({
embeddingFunction,
dimensions: 512, // Universal Sentence Encoder produces 512-dimensional vectors
metric: 'cosine'
})
await data.init()
await data.init()
// Add text items
await data.addItem('Hello world', { id: 'greeting', type: 'text' })
await data.addItem('Goodbye world', { id: 'farewell', type: 'text' })
// Add text items
await data.addItem('Hello world', { id: 'greeting', type: 'text' })
await data.addItem('Goodbye world', { id: 'farewell', type: 'text' })
// Search with text
const results = await data.search('Hi there', 1)
// Search with text
const results = await data.search('Hi there', 1)
expect(results).toBeDefined()
expect(results.length).toBeGreaterThan(0)
expect(results[0].metadata).toHaveProperty('id')
}, testUtils.timeout)
expect(results).toBeDefined()
expect(results.length).toBeGreaterThan(0)
expect(results[0].metadata).toHaveProperty('id')
},
globalThis.testUtils?.timeout || 30000
)
it('should handle mixed vector and text operations', async () => {
const embeddingFunction = brainy.createEmbeddingFunction()
it(
'should handle mixed vector and text operations',
async () => {
const embeddingFunction = brainy.createEmbeddingFunction()
const data = new brainy.BrainyData({
embeddingFunction,
metric: 'cosine'
})
const data = new brainy.BrainyData({
embeddingFunction,
dimensions: 512, // Universal Sentence Encoder produces 512-dimensional vectors
metric: 'cosine'
})
await data.init()
await data.init()
// Add text item
await data.addItem('Machine learning', { id: 'text1', type: 'text' })
// Add text item
await data.addItem('Machine learning', { id: 'text1', type: 'text' })
// Add vector item (using embedding of similar text)
const embedding = await embeddingFunction('Artificial intelligence')
await data.add(embedding, { id: 'vector1', type: 'vector' })
// Add vector item (using embedding of similar text)
const embedding = await embeddingFunction('Artificial intelligence')
await data.add(embedding, { id: 'vector1', type: 'vector' })
// Search should find both
const results = await data.search('AI and ML', 2)
// Search should find both
const results = await data.search('AI and ML', 2)
expect(results).toBeDefined()
expect(results.length).toBeGreaterThan(0)
}, testUtils.timeout)
expect(results).toBeDefined()
expect(results.length).toBeGreaterThan(0)
},
globalThis.testUtils?.timeout || 30000
)
})
describe('Error Handling', () => {
@ -246,6 +262,7 @@ describe('Brainy Core Functionality', () => {
})
await data.init()
await data.clear() // Clear any existing data
// Search in empty database
const results = await data.search([1, 2], 1)
@ -268,7 +285,9 @@ describe('Brainy Core Functionality', () => {
// Add 100 test vectors
for (let i = 0; i < 100; i++) {
const vector = testUtils.createTestVector(10)
const vector =
globalThis.testUtils?.createTestVector(10) ||
Array.from({ length: 10 }, (_, i) => (i + 1) / 10)
await data.add(vector, { id: `item_${i}`, index: i })
}
@ -276,7 +295,11 @@ describe('Brainy Core Functionality', () => {
// Search should be fast
const searchStart = Date.now()
const results = await data.search(testUtils.createTestVector(10), 10)
const results = await data.search(
globalThis.testUtils?.createTestVector(10) ||
Array.from({ length: 10 }, (_, i) => (i + 1) / 10),
10
)
const searchTime = Date.now() - searchStart
expect(results.length).toBeLessThanOrEqual(10)
@ -298,7 +321,9 @@ describe('Brainy Core Functionality', () => {
// Add noise vectors
for (let i = 0; i < 50; i++) {
const noiseVector = testUtils.createTestVector(5)
const noiseVector =
globalThis.testUtils?.createTestVector(5) ||
Array.from({ length: 5 }, (_, i) => (i + 1) / 5)
await data.add(noiseVector, { id: `noise_${i}`, type: 'noise' })
}