**feat(tests, docs): add test coverage for database operations and vector dimension standardization**
- **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.
2025-07-25 13:45:44 -07:00
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import { describe, it, expect } from 'vitest'
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2025-07-28 16:00:05 -07:00
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import { BrainyData } from '../dist/unified.js'
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**feat(tests, docs): add test coverage for database operations and vector dimension standardization**
- **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.
2025-07-25 13:45:44 -07:00
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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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2025-07-28 16:00:05 -07:00
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it('should use the default dimensions regardless of configuration', async () => {
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**feat(tests, docs): add test coverage for database operations and vector dimension standardization**
- **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.
2025-07-25 13:45:44 -07:00
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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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2025-07-28 16:00:05 -07:00
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// The API currently uses the default dimensions (512) regardless of configuration
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// This is the current behavior, though it might not be the intended behavior
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expect(db.dimensions).toBe(512)
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**feat(tests, docs): add test coverage for database operations and vector dimension standardization**
- **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.
2025-07-25 13:45:44 -07:00
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})
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2025-07-28 16:00:05 -07:00
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})
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