fix: implement stub methods in Neural API clustering
Previously, clusterByDomain() and clusterByTime() methods contained stub implementations that always returned empty arrays. This caused empty results when attempting domain-based or temporal clustering. Changes: - Implement _getItemsByField() to query brain storage - Implement _getItemsByTimeWindow() to filter by time windows - Fix _groupByDomain() to check root, metadata, and data fields - Implement _findCrossDomainMembers() for cross-domain analysis - Implement _findCrossDomainClusters() to merge similar clusters - Add comprehensive tests for domain and time clustering - Update documentation structure to include VFS guides The methods now properly query the brain's storage, filter results, and return functional clustering data.
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tests/unit/neural/domain-time-clustering.test.ts
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tests/unit/neural/domain-time-clustering.test.ts
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/**
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* Domain and Time Clustering Tests
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*
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* Tests for clusterByDomain() and clusterByTime() methods
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* that were previously stub implementations.
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*/
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import { describe, it, expect, beforeEach } from 'vitest'
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import { Brainy } from '../../../src/brainy'
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import { NounType } from '../../../src/types/graphTypes'
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import { createAddParams } from '../../helpers/test-factory'
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describe('Domain and Time Clustering', () => {
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let brain: Brainy
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beforeEach(async () => {
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brain = new Brainy({ enableCache: false })
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await brain.init()
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})
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describe('clusterByDomain() - Field-based clustering', () => {
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it('should cluster entities by type field', async () => {
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// Add entities of different types
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await brain.add(createAddParams({
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data: 'John Smith is a person',
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type: NounType.Person
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}))
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await brain.add(createAddParams({
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data: 'Jane Doe is also a person',
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type: NounType.Person
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}))
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await brain.add(createAddParams({
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data: 'Technical document about AI',
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type: NounType.Document
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}))
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await brain.add(createAddParams({
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data: 'Research paper on machine learning',
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type: NounType.Document
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}))
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await brain.add(createAddParams({
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data: 'Microsoft Corporation',
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type: NounType.Organization
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}))
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// Cluster by type field
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const clusters = await brain.neural().clusterByDomain('type', {
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minClusterSize: 1,
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maxClusters: 10
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})
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// Should have clusters for each type
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expect(Array.isArray(clusters)).toBe(true)
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expect(clusters.length).toBeGreaterThan(0)
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// Verify domain values exist
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const domains = new Set(clusters.map(c => c.domain))
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expect(domains.has(NounType.Person) || domains.has('person')).toBe(true)
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expect(domains.has(NounType.Document) || domains.has('document')).toBe(true)
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})
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it('should cluster entities by metadata field', async () => {
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// Add entities with category metadata
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await brain.add(createAddParams({
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data: 'JavaScript programming guide',
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type: NounType.Document,
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metadata: { category: 'programming' }
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}))
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await brain.add(createAddParams({
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data: 'Python tutorial',
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type: NounType.Document,
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metadata: { category: 'programming' }
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}))
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await brain.add(createAddParams({
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data: 'Chocolate cake recipe',
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type: NounType.Document,
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metadata: { category: 'cooking' }
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}))
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await brain.add(createAddParams({
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data: 'Pasta preparation',
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type: NounType.Document,
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metadata: { category: 'cooking' }
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}))
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// Cluster by category field
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const clusters = await brain.neural().clusterByDomain('category', {
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minClusterSize: 1,
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maxClusters: 5
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})
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expect(Array.isArray(clusters)).toBe(true)
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expect(clusters.length).toBeGreaterThan(0)
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// Verify categories are in domains
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const domains = new Set(clusters.map(c => c.domain))
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expect(domains.has('programming')).toBe(true)
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expect(domains.has('cooking')).toBe(true)
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})
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it('should handle entities without the specified field', async () => {
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// Add entities with and without category
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await brain.add(createAddParams({
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data: 'Has category',
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metadata: { category: 'tech' }
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}))
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await brain.add(createAddParams({
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data: 'No category'
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}))
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const clusters = await brain.neural().clusterByDomain('category', {
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minClusterSize: 1
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})
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expect(Array.isArray(clusters)).toBe(true)
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// Should have 'tech' and 'unknown' domains
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const domains = new Set(clusters.map(c => c.domain))
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expect(domains.has('tech')).toBe(true)
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expect(domains.has('unknown')).toBe(true)
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})
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})
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describe('clusterByTime() - Temporal clustering', () => {
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it('should cluster entities by time windows', async () => {
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const now = new Date()
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const oneDayAgo = new Date(now.getTime() - 24 * 60 * 60 * 1000)
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const oneWeekAgo = new Date(now.getTime() - 7 * 24 * 60 * 60 * 1000)
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const oneMonthAgo = new Date(now.getTime() - 30 * 24 * 60 * 60 * 1000)
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// Add entities with different timestamps
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await brain.add(createAddParams({
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data: 'Recent item 1',
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metadata: { publishedAt: now.toISOString() }
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}))
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await brain.add(createAddParams({
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data: 'Recent item 2',
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metadata: { publishedAt: oneDayAgo.toISOString() }
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}))
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await brain.add(createAddParams({
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data: 'Old item 1',
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metadata: { publishedAt: oneWeekAgo.toISOString() }
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}))
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await brain.add(createAddParams({
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data: 'Very old item',
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metadata: { publishedAt: oneMonthAgo.toISOString() }
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}))
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// Define time windows
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const timeWindows = [
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{
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start: new Date(now.getTime() - 2 * 24 * 60 * 60 * 1000), // Last 2 days
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end: now,
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label: 'Recent'
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},
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{
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start: new Date(now.getTime() - 14 * 24 * 60 * 60 * 1000), // 2-14 days ago
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end: new Date(now.getTime() - 2 * 24 * 60 * 60 * 1000),
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label: 'This Week'
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},
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{
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start: new Date(now.getTime() - 60 * 24 * 60 * 60 * 1000), // 14-60 days ago
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end: new Date(now.getTime() - 14 * 24 * 60 * 60 * 1000),
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label: 'Older'
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}
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]
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// Cluster by time
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const clusters = await brain.neural().clusterByTime('publishedAt', timeWindows, {
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timeField: 'publishedAt',
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windows: timeWindows
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})
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expect(Array.isArray(clusters)).toBe(true)
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expect(clusters.length).toBeGreaterThan(0)
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// Verify time windows are represented
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const windowLabels = new Set(clusters.map(c => c.timeWindow?.label))
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expect(windowLabels.size).toBeGreaterThan(0)
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})
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it('should cluster entities by createdAt timestamps', async () => {
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// These will use the auto-generated createdAt timestamps
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const id1 = await brain.add(createAddParams({
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data: 'First item'
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}))
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// Wait a bit to ensure different timestamps
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await new Promise(resolve => setTimeout(resolve, 10))
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const id2 = await brain.add(createAddParams({
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data: 'Second item'
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}))
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const now = new Date()
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const timeWindows = [
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{
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start: new Date(now.getTime() - 60 * 60 * 1000), // Last hour
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end: new Date(now.getTime() + 60 * 60 * 1000), // Next hour (to include all)
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label: 'Now'
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}
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]
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const clusters = await brain.neural().clusterByTime('createdAt', timeWindows, {
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timeField: 'createdAt',
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windows: timeWindows
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})
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expect(Array.isArray(clusters)).toBe(true)
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// Both items should be in the 'Now' time window
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const nowCluster = clusters.find(c => c.timeWindow?.label === 'Now')
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expect(nowCluster).toBeDefined()
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if (nowCluster) {
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expect(nowCluster.members.length).toBeGreaterThanOrEqual(2)
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}
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})
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it('should handle empty time windows gracefully', async () => {
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const futureStart = new Date(Date.now() + 365 * 24 * 60 * 60 * 1000) // 1 year from now
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const futureEnd = new Date(Date.now() + 2 * 365 * 24 * 60 * 60 * 1000) // 2 years from now
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const timeWindows = [
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{
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start: futureStart,
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end: futureEnd,
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label: 'Future'
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}
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]
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const clusters = await brain.neural().clusterByTime('createdAt', timeWindows, {
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timeField: 'createdAt',
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windows: timeWindows
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})
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// Should return empty array or array with empty clusters
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expect(Array.isArray(clusters)).toBe(true)
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})
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})
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describe('Cross-domain functionality', () => {
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it('should find cross-domain clusters when enabled', async () => {
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// Add entities from different domains with similar content
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await brain.add(createAddParams({
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data: 'Machine learning and artificial intelligence',
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type: NounType.Document,
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metadata: { category: 'tech' }
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}))
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await brain.add(createAddParams({
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data: 'AI and neural networks',
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type: NounType.Concept,
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metadata: { category: 'science' }
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}))
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const clusters = await brain.neural().clusterByDomain('category', {
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minClusterSize: 1,
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preserveDomainBoundaries: false, // Enable cross-domain clustering
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crossDomainThreshold: 0.5
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})
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expect(Array.isArray(clusters)).toBe(true)
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expect(clusters.length).toBeGreaterThan(0)
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})
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})
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})
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