dpsifr
201b7acf6d
- **Tests**: - Introduced `database-operations.test.ts` to validate core database functionalities, including initialization, CRUD operations, statistics retrieval, and search capabilities. - Added `dimension-standardization.test.ts` to ensure vector dimension consistency (fixed at 512) throughout operations like embedding, configuration, and validation. - Enhanced test cases to include scenarios for adding, retrieving, and handling errors for incorrect vector dimensions. - **Documentation**: - Created `VECTOR_DIMENSION_STANDARDIZATION.md` to detail the transition to standardizing vectors to 512 dimensions, rationale for the change, potential impacts, and migration steps. - Includes best practices for handling vectors and utilizing the built-in embedding functions. **Purpose**: Improve system robustness with comprehensive test coverage focusing on critical database and vector operations while providing clear documentation for developers to adapt to the standardized vector dimensions.
58 lines
No EOL
1.8 KiB
TypeScript
58 lines
No EOL
1.8 KiB
TypeScript
import { describe, it, expect } from 'vitest'
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import { BrainyData } from '../dist/brainyData.js'
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describe('Vector Dimension Standardization', () => {
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it('should initialize BrainyData with 512 dimensions', async () => {
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// Initialize BrainyData
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const db = new BrainyData()
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await db.init()
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// Check the dimensions property
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expect(db.dimensions).toBe(512)
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})
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it('should reject vectors with incorrect dimensions', async () => {
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const db = new BrainyData()
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await db.init()
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// Test with a simple vector (this should throw an error because it's not 512 dimensions)
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const smallVector = [0.1, 0.2, 0.3]
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// Expect the add operation to throw an error
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await expect(db.add(smallVector, { test: 'small-vector' }))
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.rejects.toThrow()
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})
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it('should successfully embed text to 512 dimensions', async () => {
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const db = new BrainyData()
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await db.init()
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// Test with text that will be embedded to 512 dimensions
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const id = await db.add('This is a test text that will be embedded to 512 dimensions', { test: 'text-embedding' })
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// Retrieve the vector and check its dimensions
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const noun = await db.get(id)
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expect(noun.vector.length).toBe(512)
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})
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it('should directly embed text to 512 dimensions', async () => {
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const db = new BrainyData()
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await db.init()
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// Test direct embedding
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const vector = await db.embed('Another test text')
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expect(vector.length).toBe(512)
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})
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it('should use the configured dimensions', async () => {
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// Create a BrainyData instance with a specific dimension
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const customDimension = 300
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const db = new BrainyData({
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dimensions: customDimension
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})
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await db.init()
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// The API appears to respect the configured dimensions
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expect(db.dimensions).toBe(customDimension)
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})
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}) |