brainy/tests/integration/image-import.test.ts
David Snelling 1874b77896 feat: add ImageHandler with EXIF extraction and comprehensive MIME detection (v5.2.0)
Implements Phase 1.5 (Comprehensive MIME Type Detection) and adds built-in image processing support to IntelligentImportAugmentation.

**New Features:**
- ImageHandler: Extracts image metadata (dimensions, format, color space) using sharp
- EXIF extraction: Camera data, GPS, timestamps using exifr library
- Support for JPEG, PNG, WebP, GIF, TIFF, BMP, SVG, HEIC, AVIF formats
- MimeTypeDetector: Unified MIME type detection with magic byte support
- FormatDetector: Enhanced with image format detection via MIME + magic bytes

**Architecture Fixes:**
- Fixed brain.import() augmentation pipeline integration (src/brainy.ts:3140-3154)
- Added parameter spreading for ImportSource objects to enable augmentation access
- Fixed metadata propagation through ImportCoordinator to final results
- Added augmentation data check in ImportCoordinator.extract()

**Integration:**
- ImageHandler registered as built-in handler alongside CSV, Excel, PDF
- Images import as 'media' entities with 'image' subtype
- Full metadata preserved in knowledge graph entities
- Configuration options: enableImage, extractEXIF, imageDefaults

**Test Coverage:**
- 15 integration tests (image-import.test.ts) - 100% passing
- 27 unit tests (image-handler.test.ts) - 100% passing
- Format detection tests for all supported image types
- Error handling and resilience tests

**Breaking Changes:** None - backward compatible

Generated with Claude Code

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-03 14:06:17 -08:00

361 lines
11 KiB
TypeScript

/**
* Image Import Integration Test (v5.2.0)
*
* Tests that ImageHandler works as a built-in handler with IntelligentImportAugmentation
*/
import { describe, it, expect, beforeEach, afterEach } from 'vitest'
import { Brainy } from '../../src/brainy.js'
import sharp from 'sharp'
describe('Image Import Integration (v5.2.0)', () => {
let brain: Brainy
// Create test image
const createTestImage = async (width: number, height: number): Promise<Buffer> => {
const channels = 3
const pixelData = Buffer.alloc(width * height * channels)
// Fill with gradient
for (let y = 0; y < height; y++) {
for (let x = 0; x < width; x++) {
const i = (y * width + x) * channels
pixelData[i] = Math.floor((x / width) * 255)
pixelData[i + 1] = Math.floor((y / height) * 255)
pixelData[i + 2] = 128
}
}
return sharp(pixelData, {
raw: { width, height, channels }
})
.jpeg({ quality: 90 })
.toBuffer()
}
beforeEach(async () => {
// IntelligentImportAugmentation is enabled by default with all handlers
// Configure it to only enable image handler for focused testing
brain = new Brainy({
silent: true,
augmentations: {
intelligentImport: {
enableCSV: false,
enableExcel: false,
enablePDF: false,
enableImage: true // Only enable image handler for this test
}
}
})
await brain.init()
})
afterEach(async () => {
await brain.close()
})
describe('Built-in Image Handler', () => {
it('should automatically handle image import via IntelligentImportAugmentation', async () => {
const imageBuffer = await createTestImage(800, 600)
// Import image - should be automatically processed by ImageHandler
const result = await brain.import({
type: 'buffer',
data: imageBuffer,
filename: 'test-photo.jpg'
})
expect(result).toBeDefined()
expect(result.entities).toHaveLength(1)
const imageEntity = result.entities[0]
expect(imageEntity.type).toBe('media')
expect(imageEntity.metadata?.subtype).toBe('image')
expect(imageEntity.metadata).toBeDefined()
})
it('should extract image metadata via built-in handler', async () => {
const imageBuffer = await createTestImage(1920, 1080)
const result = await brain.import({
type: 'buffer',
data: imageBuffer,
filename: 'hd-photo.jpg'
})
const imageEntity = result.entities[0]
expect(imageEntity).toBeDefined()
expect(imageEntity.metadata).toBeDefined()
expect(typeof imageEntity.metadata).toBe('object')
expect(imageEntity.metadata.width).toBe(1920)
expect(imageEntity.metadata.height).toBe(1080)
expect(imageEntity.metadata.format).toBe('jpeg')
})
it('should extract EXIF data when available', async () => {
const imageBuffer = await createTestImage(400, 300)
const result = await brain.import({
type: 'buffer',
data: imageBuffer,
filename: 'camera-photo.jpg',
options: {
extractEXIF: true
}
})
const imageEntity = result.entities[0]
// Test image won't have EXIF, but should not crash
expect(imageEntity).toBeDefined()
expect(imageEntity.type).toBe('media')
expect(imageEntity.metadata?.subtype).toBe('image')
})
it('should allow disabling EXIF extraction via config', async () => {
const imageBuffer = await createTestImage(400, 300)
const result = await brain.import({
type: 'buffer',
data: imageBuffer,
filename: 'no-exif.jpg',
options: {
extractEXIF: false
}
})
expect(result.entities).toHaveLength(1)
expect(result.entities[0].type).toBe('media')
expect(result.entities[0].metadata?.subtype).toBe('image')
})
it('should handle PNG images', async () => {
const pngBuffer = await sharp(Buffer.alloc(100 * 100 * 4), {
raw: { width: 100, height: 100, channels: 4 }
})
.png()
.toBuffer()
const result = await brain.import({
type: 'buffer',
data: pngBuffer,
filename: 'logo.png'
})
const imageEntity = result.entities[0]
expect(imageEntity.metadata.format).toBe('png')
expect(imageEntity.metadata.hasAlpha).toBe(true)
})
it('should handle WebP images', async () => {
const webpBuffer = await sharp(Buffer.alloc(200 * 200 * 3), {
raw: { width: 200, height: 200, channels: 3 }
})
.webp({ quality: 90 })
.toBuffer()
const result = await brain.import({
type: 'buffer',
data: webpBuffer,
filename: 'modern.webp'
})
const imageEntity = result.entities[0]
expect(imageEntity.metadata.format).toBe('webp')
})
})
describe('Configuration', () => {
it('should allow disabling image handler', async () => {
// Create new brain with image handler disabled
const brainNoImage = new Brainy({
silent: true,
augmentations: {
intelligentImport: {
enableImage: false
}
}
})
await brainNoImage.init()
const imageBuffer = await createTestImage(400, 300)
// Import should still work, but won't be processed by ImageHandler
const result = await brainNoImage.import({
type: 'buffer',
data: imageBuffer,
filename: 'unprocessed.jpg'
})
// Should create entity but not extract image metadata
expect(result).toBeDefined()
await brainNoImage.close()
})
it('should apply imageDefaults config', async () => {
const brainWithDefaults = new Brainy({
silent: true,
augmentations: {
intelligentImport: {
enableImage: true,
imageDefaults: {
extractEXIF: false // Default to no EXIF extraction
}
}
}
})
await brainWithDefaults.init()
const imageBuffer = await createTestImage(400, 300)
const result = await brainWithDefaults.import({
type: 'buffer',
data: imageBuffer,
filename: 'default-config.jpg'
})
expect(result.entities[0].type).toBe('media')
expect(result.entities[0].metadata?.subtype).toBe('image')
await brainWithDefaults.close()
})
})
describe('Image Format Detection', () => {
it('should detect JPEG by filename', async () => {
const imageBuffer = await createTestImage(400, 300)
const result = await brain.import({
type: 'buffer',
data: imageBuffer,
filename: 'photo.jpg'
})
expect(result.entities[0].metadata.format).toBe('jpeg')
})
it('should detect JPEG by .jpeg extension', async () => {
const imageBuffer = await createTestImage(400, 300)
const result = await brain.import({
type: 'buffer',
data: imageBuffer,
filename: 'photo.jpeg'
})
expect(result.entities[0].metadata.format).toBe('jpeg')
})
it('should handle various image sizes', async () => {
const sizes = [
[100, 100],
[800, 600],
[1920, 1080],
[4000, 3000]
]
for (const [width, height] of sizes) {
const imageBuffer = await createTestImage(width, height)
const result = await brain.import({
type: 'buffer',
data: imageBuffer,
filename: `image-${width}x${height}.jpg`
})
const imageEntity = result.entities[0]
expect(imageEntity.metadata.width).toBe(width)
expect(imageEntity.metadata.height).toBe(height)
}
})
})
describe('Error Handling', () => {
it('should handle invalid image data gracefully', async () => {
const invalidBuffer = Buffer.from('not an image')
// Brainy is resilient - invalid images still import with fallback minimal metadata
const result = await brain.import({
type: 'buffer',
data: invalidBuffer,
filename: 'invalid.jpg'
})
expect(result).toBeDefined()
expect(result.entities).toHaveLength(1)
expect(result.entities[0].type).toBe('media')
expect(result.entities[0].name).toBe('invalid.jpg')
})
it('should handle empty image buffer', async () => {
const emptyBuffer = Buffer.alloc(0)
// Brainy is resilient - empty buffers still import with fallback minimal metadata
const result = await brain.import({
type: 'buffer',
data: emptyBuffer,
filename: 'empty.jpg'
})
expect(result).toBeDefined()
expect(result.entities).toHaveLength(1)
expect(result.entities[0].type).toBe('media')
expect(result.entities[0].name).toBe('empty.jpg')
})
})
describe('Integration with Knowledge Graph', () => {
it('should store image entities in knowledge graph', async () => {
const imageBuffer = await createTestImage(800, 600)
const result = await brain.import({
type: 'buffer',
data: imageBuffer,
filename: 'stored-image.jpg'
})
// Verify entity was created with metadata in import result
expect(result.entities).toHaveLength(1)
expect(result.entities[0].metadata.width).toBe(800)
expect(result.entities[0].metadata.height).toBe(600)
// Query for image entities - verifies it's in the knowledge graph
const images = await brain.find({ type: 'media' })
expect(images.length).toBeGreaterThan(0)
// Verify media entities have the expected type
const mediaEntities = images.filter(img => img.type === 'media')
expect(mediaEntities.length).toBeGreaterThan(0)
})
it('should support querying by image metadata', async () => {
// Import multiple images and capture metadata from results
const result1 = await brain.import({
type: 'buffer',
data: await createTestImage(1920, 1080),
filename: 'hd.jpg'
})
const result2 = await brain.import({
type: 'buffer',
data: await createTestImage(800, 600),
filename: 'sd.jpg'
})
// Verify metadata in import results
expect(result1.entities[0].metadata.width).toBe(1920)
expect(result1.entities[0].metadata.height).toBe(1080)
expect(result2.entities[0].metadata.width).toBe(800)
expect(result2.entities[0].metadata.height).toBe(600)
// Query all media entities - verifies they're in the knowledge graph
const allImages = await brain.find({ type: 'media' })
expect(allImages.length).toBeGreaterThan(0)
// Verify multiple media entities were stored
const mediaEntities = allImages.filter(img => img.type === 'media')
expect(mediaEntities.length).toBeGreaterThanOrEqual(2)
})
})
})