{ "$schema": "https://uipkge.dev/schema/registry-item.json", "name": "vector-embeddings-inspector", "title": "Vector Embeddings Inspector", "type": "registry:block", "files": [ { "path": "packages/registry-angular/blocks/vector-embeddings-inspector/vector-embeddings-inspector.component.ts", "content": "import { Component, Input, computed, signal, ChangeDetectionStrategy } from '@angular/core'\nimport {\n Binary,\n Check,\n Compass,\n Copy,\n Cpu,\n Database,\n Eye,\n FileCode,\n FileText,\n HardDrive,\n Layers,\n LucideAngularModule,\n RefreshCw,\n Search,\n Sparkles,\n Zap,\n type LucideIconData,\n} from 'lucide-angular'\nimport { cn } from '@/lib/utils'\nimport { UiBadgeComponent, type BadgeVariant } from '@/ui/badge/badge.component'\nimport { UiButtonComponent } from '@/ui/button/button.component'\nimport {\n UiCardComponent,\n UiCardContentComponent,\n UiCardDescriptionComponent,\n UiCardHeaderComponent,\n UiCardTitleComponent,\n} from '@/ui/card/card.component'\nimport { UiInputComponent } from '@/ui/input/input.component'\nimport { UiProgressComponent } from '@/ui/progress/progress.component'\nimport {\n UiSelectComponent,\n UiSelectContentComponent,\n UiSelectItemComponent,\n UiSelectTriggerComponent,\n UiSelectValueComponent,\n} from '@/ui/select/select.component'\nimport { UiSeparatorComponent } from '@/ui/separator/separator.component'\n\nexport type VectorNamespace = 'docs' | 'blog' | 'api' | 'helpdesk'\n\nexport interface VectorSearchResultItem {\n id: string\n vectorId: string\n score: number\n cosineDistance: number\n title: string\n namespace: VectorNamespace\n filePath: string\n chunkIndex: number\n totalChunks: number\n tokenCount: number\n updatedAt: string\n snippet: string\n vectorSample: number[]\n}\n\nexport interface VectorDimensionSample {\n index: number\n dimIndex: number\n value: number\n rawDimName: string\n}\n\ninterface TelemetryCard {\n title: string\n value: string\n subtitle: string\n badge: string\n badgeVariant: BadgeVariant\n metricDetails: string\n icon: LucideIconData\n}\n\n// Index specifications\nconst indexMetadata = {\n name: 'kb-docs-embeddings-v3',\n status: 'Index Ready · Sub-10ms p99',\n metric: 'Cosine Similarity',\n dimensionModel: '1536-dim · OpenAI text-embedding-3-small',\n totalVectors: '482,910 Vectors',\n shards: 4,\n replicas: 2,\n engine: 'HNSW Graph (M=16, efConstruction=200)',\n memoryTotal: '3.8 GB RAM',\n hnswMemory: '2.9 GB',\n vectorMemory: '0.9 GB',\n cacheHitRate: 99.4,\n p99Latency: '8.4ms',\n p50Latency: '2.1ms',\n p95Latency: '5.6ms',\n qpsPeak: '1,420 QPS',\n namespacesCount: 4,\n}\n\n// Telemetry overview\nconst telemetryCards: TelemetryCard[] = [\n {\n title: 'Total Indexed Vectors',\n value: '482,910',\n subtitle: '+14,280 indexed today',\n badge: '4 Shards Active',\n badgeVariant: 'secondary',\n metricDetails: '100% synchronized · 0 unindexed docs',\n icon: Database,\n },\n {\n title: 'Index Memory Footprint',\n value: '3.8 GB RAM',\n subtitle: 'HNSW: 2.9 GB · Raw Vectors: 0.9 GB',\n badge: '99.4% Cache Hit',\n badgeVariant: 'outline',\n metricDetails: 'Compressed FP16 · Inverted index',\n icon: Cpu,\n },\n {\n title: 'Query Latency (p99)',\n value: '8.4ms',\n subtitle: 'p50: 2.1ms · p95: 5.6ms',\n badge: '1,420 QPS Peak',\n badgeVariant: 'secondary',\n metricDetails: 'Sub-10ms SLA guaranteed',\n icon: Zap,\n },\n {\n title: 'Index Namespaces',\n value: '4 Namespaces',\n subtitle: 'docs, helpdesk, blog, api',\n badge: 'Multi-Tenant',\n badgeVariant: 'outline',\n metricDetails: 'docs: 240k · helpdesk: 118k',\n icon: Layers,\n },\n]\n\n// Query presets\nconst queryPresets = [\n 'How do I configure OKLCH color palettes in Tailwind v4?',\n 'Vector quantization & memory footprint reduction',\n 'Dual-framework Vue and React component sync',\n 'Handling cosine distance thresholds for hybrid search',\n]\n\n// Query vector 32 float samples (simulated 1536-dim projection)\nconst queryVectorSample = [\n -0.048, 0.135, 0.092, -0.018, 0.245, -0.11, 0.049, 0.174, -0.082, 0.001, 0.325, -0.059, 0.097, 0.191, -0.149, 0.068,\n -0.024, 0.212, 0.082, -0.094, 0.121, -0.037, 0.156, 0.031, -0.172, 0.099, 0.056, -0.078, 0.195, -0.013, 0.072, 0.141,\n]\n\n// Hardcoded search results with full vector dimensions\nconst allSearchResults: VectorSearchResultItem[] = [\n {\n id: 'res-1',\n vectorId: '#vec_849201',\n score: 0.942,\n cosineDistance: 0.058,\n title: 'Tailwind v4 OKLCH Architecture',\n namespace: 'docs',\n filePath: 'docs/styling/tailwind-v4-oklch.md',\n chunkIndex: 3,\n totalChunks: 8,\n tokenCount: 384,\n updatedAt: '2 hours ago',\n snippet:\n 'Tailwind CSS v4 introduces native CSS-first token configuration with `@theme inline` and OKLCH color spaces. In contrast to RGB/HSL, OKLCH ensures perceptually uniform lightness across hues, preventing contrast degradation in dark mode variants while preserving single-source token truth.',\n vectorSample: [\n -0.042, 0.128, 0.089, -0.015, 0.231, -0.104, 0.045, 0.167, -0.078, 0.002, 0.312, -0.054, 0.091, 0.183, -0.142,\n 0.063, -0.021, 0.204, 0.077, -0.089, 0.115, -0.034, 0.149, 0.028, -0.165, 0.094, 0.052, -0.073, 0.188, -0.011,\n 0.067, 0.134,\n ],\n },\n {\n id: 'res-2',\n vectorId: '#vec_739104',\n score: 0.887,\n cosineDistance: 0.113,\n title: 'Color Tokens Derivation Guide',\n namespace: 'docs',\n filePath: 'docs/tokens/color-derivation.md',\n chunkIndex: 1,\n totalChunks: 5,\n tokenCount: 412,\n updatedAt: '1 day ago',\n snippet:\n 'Deriving consistent dark-mode contrasts requires anchoring chroma and shifting lightness along the OKLCH L-axis. Use `--color-primary: oklch(0.65 0.22 260)` for vibrant interactive states and calibrate border contrast with `--color-border: oklch(0.28 0.01 260)` for WCAG AA compliance.',\n vectorSample: [\n -0.038, 0.119, 0.074, -0.029, 0.198, -0.088, 0.032, 0.145, -0.065, 0.018, 0.284, -0.041, 0.082, 0.161, -0.125,\n 0.051, -0.015, 0.189, 0.062, -0.075, 0.098, -0.026, 0.131, 0.019, -0.148, 0.081, 0.044, -0.061, 0.162, -0.008,\n 0.055, 0.118,\n ],\n },\n {\n id: 'res-3',\n vectorId: '#vec_610482',\n score: 0.824,\n cosineDistance: 0.176,\n title: 'Design Systems Monorepo Guide',\n namespace: 'blog',\n filePath: 'blog/2026/design-systems-monorepo.md',\n chunkIndex: 5,\n totalChunks: 12,\n tokenCount: 526,\n updatedAt: '3 days ago',\n snippet:\n 'When architecting a dual-framework registry (Vue + React), shared design tokens must compile cleanly without runtime overhead. We leverage modern CSS custom properties and PostCSS pipelines to mirror CVA component variants across both frameworks with zero extra runtime.',\n vectorSample: [\n -0.015, 0.082, 0.061, -0.045, 0.154, -0.062, 0.021, 0.112, -0.049, 0.034, 0.215, -0.028, 0.064, 0.128, -0.098,\n 0.039, -0.008, 0.142, 0.048, -0.058, 0.074, -0.018, 0.099, 0.012, -0.112, 0.062, 0.031, -0.045, 0.124, -0.004,\n 0.041, 0.089,\n ],\n },\n {\n id: 'res-4',\n vectorId: '#vec_552109',\n score: 0.768,\n cosineDistance: 0.232,\n title: 'API Embedding Ingestion Pipeline',\n namespace: 'api',\n filePath: 'api/v1/embeddings/ingest-worker.ts',\n chunkIndex: 2,\n totalChunks: 6,\n tokenCount: 340,\n updatedAt: '5 days ago',\n snippet:\n 'Batch ingestion pipelines chunk markdown documents at 512-token boundaries with 64-token sliding window overlap before sending to the embedding model endpoint. Vector metadata records store namespace tags, checksum hashes, and parent chunk UUIDs for deterministic cache validation.',\n vectorSample: [\n 0.024, 0.051, 0.038, -0.062, 0.112, -0.041, 0.008, 0.078, -0.032, 0.051, 0.162, -0.015, 0.042, 0.094, -0.071,\n 0.022, 0.004, 0.105, 0.031, -0.041, 0.052, -0.009, 0.071, 0.005, -0.084, 0.045, 0.019, -0.031, 0.088, 0.002,\n 0.028, 0.062,\n ],\n },\n {\n id: 'res-5',\n vectorId: '#vec_419820',\n score: 0.715,\n cosineDistance: 0.285,\n title: 'Troubleshooting Semantic Search Discrepancies',\n namespace: 'helpdesk',\n filePath: 'helpdesk/kb/search-accuracy-faq.md',\n chunkIndex: 4,\n totalChunks: 7,\n tokenCount: 295,\n updatedAt: '1 week ago',\n snippet:\n 'When semantic ranking diverges from keyword relevance, verify tokenizer parity between query embedding model and index model. Cosine thresholds below 0.75 should trigger hybrid search fallback using sparse BM25 reranking to recover exact keyword lexical matches.',\n vectorSample: [\n 0.042, 0.031, 0.019, -0.078, 0.084, -0.025, -0.005, 0.052, -0.019, 0.068, 0.118, -0.008, 0.028, 0.067, -0.052,\n 0.011, 0.015, 0.074, 0.018, -0.029, 0.036, -0.002, 0.048, -0.004, -0.061, 0.029, 0.008, -0.019, 0.061, 0.008,\n 0.015, 0.041,\n ],\n },\n]\n\nfunction getNamespaceBadgeClass(namespace: string): string {\n switch (namespace) {\n case 'docs':\n return 'bg-info/10 text-info border-info/20'\n case 'blog':\n return 'bg-chart-1/10 text-chart-1 border-chart-1/20'\n case 'api':\n return 'bg-success/10 text-success border-success/20'\n case 'helpdesk':\n return 'bg-warning/10 text-warning border-warning/20'\n default:\n return 'bg-secondary text-secondary-foreground'\n }\n}\n\nfunction getScoreColorClass(score: number): string {\n if (score >= 0.9) return 'text-success'\n if (score >= 0.8) return 'text-info'\n return 'text-warning'\n}\n\nfunction getHeatmapCellClass(val: number, isSelected: boolean): string {\n let baseColor = ''\n if (val >= 0.2) {\n baseColor = 'bg-success/25 border-success/40 text-success hover:bg-success/35'\n } else if (val >= 0.08) {\n baseColor = 'bg-success/15 border-success/25 text-success hover:bg-success/25'\n } else if (val >= 0.0) {\n baseColor = 'bg-primary/10 border-primary/20 text-foreground hover:bg-primary/20'\n } else if (val >= -0.08) {\n baseColor = 'bg-warning/10 border-warning/25 text-warning hover:bg-warning/20'\n } else {\n baseColor = 'bg-destructive/20 border-destructive/35 text-destructive hover:bg-destructive/30'\n }\n\n const selectedRing = isSelected ? 'ring-2 ring-primary ring-offset-1 ring-offset-background z-10 font-bold' : ''\n return cn(baseColor, selectedRing)\n}\n\n/** `+0.128` / `-0.042`: the sign React prints in front of each float. */\nfunction signed(value: number, digits: number): string {\n return (value >= 0 ? '+' : '') + value.toFixed(digits)\n}\n\n/**\n * VectorEmbeddingsInspector -- the React block, 1:1. A vector-index console: index header\n * with metric badges, four telemetry cards, a similarity-search playground (query input,\n * Top-K and namespace selects, preset pills, ranked results with cosine scores) and a\n * 32-sample heatmap of the inspected vector with a per-dimension deep-dive and raw JSON.\n * All data is hardcoded sample data -- swap `allSearchResults` / `runSearch()` for your\n * vector store's query API.\n */\n@Component({\n changeDetection: ChangeDetectionStrategy.Eager,\n selector: 'ui-vector-embeddings-inspector, [ui-vector-embeddings-inspector]',\n standalone: true,\n // React renders the root
itself: keep the host out of layout.\n host: { class: 'contents' },\n imports: [\n LucideAngularModule,\n UiBadgeComponent,\n UiButtonComponent,\n UiCardComponent,\n UiCardHeaderComponent,\n UiCardTitleComponent,\n UiCardDescriptionComponent,\n UiCardContentComponent,\n UiInputComponent,\n UiProgressComponent,\n UiSelectComponent,\n UiSelectTriggerComponent,\n UiSelectValueComponent,\n UiSelectContentComponent,\n UiSelectItemComponent,\n UiSeparatorComponent,\n ],\n template: `\n
\n \n
\n
\n
\n
\n
\n
\n \n

\n {{ indexMetadata.name }}\n

\n
\n \n \n {{ indexMetadata.status }}\n \n
\n

\n Vector index telemetry, approximate nearest neighbor (ANN) similarity tester, and 1536-dimensional\n tensor inspector.\n

\n
\n\n
\n \n \n Test Similarity Search\n \n
\n
\n\n \n
\n \n \n Metric:\n {{ indexMetadata.metric }}\n \n\n \n \n Embedding:\n {{ indexMetadata.dimensionModel }}\n \n\n \n \n {{ indexMetadata.totalVectors }}\n \n\n \n \n Engine:\n {{ indexMetadata.engine }}\n \n
\n
\n
\n\n \n
\n @for (card of telemetryCards; track card.title) {\n
\n
\n

\n {{ card.title }}\n

\n \n
\n
\n
\n {{ card.value }}\n \n {{ card.badge }}\n \n
\n

{{ card.subtitle }}

\n
{{ card.metricDetails }}
\n
\n
\n }\n
\n\n \n
\n
\n
\n
\n
\n \n

Interactive Similarity Search Playground

\n
\n

\n Execute live vector similarity lookups against the HNSW index and inspect cosine distance scores.\n

\n
\n
\n Index Latency:\n {{ searchExecutionLatency() }}\n
\n
\n
\n\n
\n \n
\n
\n
\n \n \n \n @if (searchQuery()) {\n \n ✕\n \n }\n
\n\n \n
\n \n \n \n Top 3 Results\n Top 5 Results\n Top 10 Results\n \n \n\n \n \n \n \n All Namespaces (4)\n docs (240k)\n blog (84k)\n api (40k)\n helpdesk (118k)\n \n \n\n \n @if (!isSearching()) {\n \n } @else {\n \n }\n Search\n \n
\n
\n\n \n
\n Try Query:\n @for (preset of queryPresets; track preset) {\n \n {{ preset }}\n \n }\n
\n
\n\n \n \n
\n \n
\n Query Vector Generated:\n \n Vectorized in\n {{ embeddingLatency() }} via\n text-embedding-3-small (1536 float32 values)\n
\n
\n \n \n Inspect Query Vector\n \n
\n\n \n\n \n
\n
\n

\n Similarity Search Results ({{ visibleResults().length }} matches)\n

\n \n Metric: Cosine Similarity · Threshold ≥ 0.70\n \n
\n\n
\n @for (result of visibleResults(); track result.id; let index = $index) {\n \n
\n \n
\n
\n \n #{{ index + 1 }}\n \n

{{ result.title }}

\n \n {{ result.namespace }}\n \n
\n \n {{ result.vectorId }}\n \n \n @if (copiedVectorId() === result.vectorId) {\n \n } @else {\n \n }\n \n
\n
\n\n \n
\n
\n Cosine Similarity:\n \n {{ result.score.toFixed(3) }}\n \n
\n
\n Distance:\n \n {{ result.cosineDistance.toFixed(3) }}\n \n
\n
\n
\n\n \n
\n
\n Similarity Match\n {{ (result.score * 100).toFixed(1) }}%\n
\n \n
\n\n \n \n

\n Chunk #{{ result.chunkIndex }} of {{ result.totalChunks }} · {{ result.filePath }}\n

\n

{{ result.snippet }}

\n
\n\n \n
\n
\n \n \n {{ result.tokenCount }} tokens\n \n •\n Updated {{ result.updatedAt }}\n
\n\n \n \n Inspect 1536-Dim Heatmap\n \n
\n
\n
\n }\n
\n
\n
\n \n\n \n
\n
\n
\n
\n
\n \n

Vector Dimension Array Visualizer

\n
\n

\n Visual color-coded 32-sample heatmap of the 1536 float values. Select dimensions to inspect activation\n magnitude.\n

\n
\n\n
\n \n \n \n {{ rawJsonExpanded() ? 'Hide Raw JSON' : 'View Raw Floats' }}\n \n
\n
\n\n \n
\n Inspecting:\n \n Query Vector\n \n @for (res of visibleResults(); track res.id; let idx = $index) {\n \n #{{ idx + 1 }} {{ res.vectorId }}\n \n }\n
\n
\n\n
\n \n \n
\n
{{ activeVectorData().label }}
\n
{{ activeVectorData().source }}
\n
\n
\n
\n L2 Norm:&ngsp;1.0000 (Unit)\n
\n
\n Dimensions:&ngsp;1,536 floats\n
\n
\n Format:&ngsp;FP32 Dense\n
\n
\n
\n\n \n
\n
\n \n Sampled 32 Float Dimensions (1 of every 48 tensor indices):\n \n
\n \n \n < -0.10\n \n \n \n -0.05\n \n \n \n ~0.00\n \n \n \n > +0.10\n \n
\n
\n\n
\n @for (sample of dimensionSamples(); track sample.index) {\n \n d{{ pad4(sample.dimIndex) }}\n {{\n signed(sample.value, 3)\n }}\n \n }\n
\n
\n\n \n
\n
\n
\n
\n \n {{ currentDimension().rawDimName }}\n \n \n Dimension #{{ currentDimension().dimIndex }} / 1536\n \n \n {{ currentDimension().activation }}\n \n
\n

\n Tensor index {{ currentDimension().rawDimName }} mapped from\n 1536-dimensional OpenAI text-embedding-3-small vector space.\n

\n
\n\n \n
\n
\n
Float Value
\n
\n {{ signed(currentDimension().value, 6) }}\n
\n
\n
\n
Relative Magnitude
\n
\n {{ currentDimension().magnitudePercent }}%\n
\n
\n
\n
Sign / Polarity
\n
{{ currentDimension().sign }}
\n
\n
\n
\n\n \n
\n
\n Dimension Activation Magnitude vs Max Dimension (+0.325)\n {{ currentDimension().magnitudePercent }}%\n
\n \n
\n
\n\n \n @if (rawJsonExpanded()) {\n
\n
Raw FP32 Float Array (32 Sample Tensor):
\n {{ rawJson() }}\n
\n }\n
\n \n \n `,\n})\nexport class UiVectorEmbeddingsInspectorComponent {\n @Input('class') className?: string\n\n protected readonly Binary = Binary\n protected readonly Check = Check\n protected readonly Compass = Compass\n protected readonly Copy = Copy\n protected readonly Cpu = Cpu\n protected readonly Database = Database\n protected readonly Eye = Eye\n protected readonly FileCode = FileCode\n protected readonly FileText = FileText\n protected readonly HardDrive = HardDrive\n protected readonly RefreshCw = RefreshCw\n protected readonly Search = Search\n protected readonly Sparkles = Sparkles\n\n protected readonly cn = cn\n protected readonly signed = signed\n protected readonly indexMetadata = indexMetadata\n protected readonly telemetryCards = telemetryCards\n protected readonly queryPresets = queryPresets\n\n readonly searchQuery = signal('How do I configure OKLCH color palettes in Tailwind v4?')\n readonly topK = signal('3')\n readonly selectedNamespace = signal('all')\n readonly isSearching = signal(false)\n readonly searchExecutionLatency = signal('3.4ms')\n readonly embeddingLatency = signal('18.2ms')\n readonly copiedVectorId = signal(null)\n readonly copiedFloats = signal(false)\n readonly rawJsonExpanded = signal(false)\n\n /** 'query' or a search result id. */\n readonly activeVisualizerTarget = signal('query')\n readonly selectedDimensionIndex = signal(10)\n\n readonly visibleResults = computed(() => {\n let list = allSearchResults\n const ns = this.selectedNamespace()\n if (ns !== 'all') {\n list = list.filter((r) => r.namespace === ns)\n }\n const k = parseInt(this.topK(), 10) || 3\n return list.slice(0, k)\n })\n\n readonly activeVectorData = computed(() => {\n const target = this.activeVisualizerTarget()\n if (target === 'query') {\n return {\n label: 'Query Embedding Vector (Prompt Projection)',\n vectorId: '#vec_query_input',\n sample: queryVectorSample,\n source: 'OpenAI text-embedding-3-small · Live query tensor',\n }\n }\n const found = allSearchResults.find((r) => r.id === target)\n if (found) {\n return {\n label: `Doc Vector: ${found.title}`,\n vectorId: found.vectorId,\n sample: found.vectorSample,\n source: `${found.namespace} namespace · ${found.filePath}`,\n }\n }\n return {\n label: 'Query Embedding Vector',\n vectorId: '#vec_query_input',\n sample: queryVectorSample,\n source: 'Live query tensor',\n }\n })\n\n readonly dimensionSamples = computed(() =>\n this.activeVectorData().sample.map((val, idx) => ({\n index: idx,\n dimIndex: idx * 48,\n value: val,\n rawDimName: `dim_${String(idx * 48).padStart(4, '0')}`,\n })),\n )\n\n readonly currentDimension = computed(() => {\n const samples = this.dimensionSamples()\n const target = samples[this.selectedDimensionIndex()] ?? samples[0]!\n const val = target.value\n const absVal = Math.abs(val)\n const maxVal = 0.325\n const magnitudePercent = Math.min(100, Math.round((absVal / maxVal) * 100))\n return {\n ...target,\n magnitudePercent,\n sign: val >= 0 ? 'Positive (+)' : 'Negative (-)',\n activation: absVal > 0.2 ? 'Strong Activation' : absVal > 0.08 ? 'Moderate Activation' : 'Near Zero / Baseline',\n }\n })\n\n readonly rawJson = computed(() => JSON.stringify(this.activeVectorData().sample, null, 2))\n\n namespaceBadgeClass(namespace: VectorNamespace): string {\n return getNamespaceBadgeClass(namespace)\n }\n\n scoreColorClass(score: number): string {\n return getScoreColorClass(score)\n }\n\n heatmapCellClass(sample: VectorDimensionSample): string {\n return cn(\n 'group flex cursor-pointer flex-col items-center justify-center rounded-md border p-1.5 text-center transition-colors select-none',\n getHeatmapCellClass(sample.value, this.selectedDimensionIndex() === sample.index),\n )\n }\n\n targetTabClass(base: string, target: string): string {\n return cn(\n base,\n this.activeVisualizerTarget() === target &&\n 'bg-primary text-primary-foreground hover:bg-primary/90 hover:text-primary-foreground',\n )\n }\n\n pad4(n: number): string {\n return String(n).padStart(4, '0')\n }\n\n runSearch(): void {\n this.isSearching.set(true)\n setTimeout(() => {\n this.isSearching.set(false)\n this.searchExecutionLatency.set((2.8 + Math.random() * 1.8).toFixed(1) + 'ms')\n this.embeddingLatency.set((16.5 + Math.random() * 3.5).toFixed(1) + 'ms')\n }, 320)\n }\n\n onQueryKeydown(event: KeyboardEvent): void {\n if (event.key === 'Enter') this.runSearch()\n }\n\n onTopKChange(val: string): void {\n this.topK.set(val)\n this.runSearch()\n }\n\n onNamespaceChange(val: string): void {\n this.selectedNamespace.set(val)\n this.runSearch()\n }\n\n selectPreset(preset: string): void {\n this.searchQuery.set(preset)\n this.runSearch()\n }\n\n copyVectorId(id: string): void {\n navigator.clipboard?.writeText(id)\n this.copiedVectorId.set(id)\n setTimeout(() => this.copiedVectorId.set(null), 2000)\n }\n\n copyFloatsArray(): void {\n const jsonStr = JSON.stringify(this.activeVectorData().sample, null, 2)\n navigator.clipboard?.writeText(jsonStr)\n this.copiedFloats.set(true)\n setTimeout(() => this.copiedFloats.set(false), 2000)\n }\n}\n", "type": "registry:block", "target": "~/src/app/components/blocks/vector-embeddings-inspector/vector-embeddings-inspector.component.ts" }, { "path": "packages/registry-angular/blocks/vector-embeddings-inspector/index.ts", "content": "export {\n UiVectorEmbeddingsInspectorComponent,\n type VectorNamespace,\n type VectorSearchResultItem,\n type VectorDimensionSample,\n} from './vector-embeddings-inspector.component'\n", "type": "registry:block", "target": "~/src/app/components/blocks/vector-embeddings-inspector/index.ts" } ], "dependencies": [ "lucide-angular" ], "devDependencies": [], "registryDependencies": [ "https://uipkge.dev/r/angular/badge.json", "https://uipkge.dev/r/angular/button.json", "https://uipkge.dev/r/angular/card.json", "https://uipkge.dev/r/angular/input.json", "https://uipkge.dev/r/angular/progress.json", "https://uipkge.dev/r/angular/select.json", "https://uipkge.dev/r/angular/separator.json" ], "description": "Pinecone & Qdrant style vector database index inspector, similarity search tester, and dimensionality explorer with cosine distance scoring, vector heatmap visualization, and namespace filtering.", "categories": [ "ai", "dashboard", "database", "app" ] }