527 lines
17 KiB
TypeScript
527 lines
17 KiB
TypeScript
/**
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* HNSW Latent Space Exploration Simulation
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*
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* Analyzes the hierarchical navigable small world graph structure created by RuVector's
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* HNSW implementation, comparing against traditional hnswlib performance and validating
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* the graph properties that enable sub-millisecond search.
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*
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* Research Foundation:
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* - RuVector HNSW: 61µs search latency (k=10, 384d)
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* - hnswlib baseline: ~500µs
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* - Target: 8x speedup with native Rust implementation
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*/
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import type {
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SimulationScenario,
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SimulationReport,
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PerformanceMetrics,
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} from '../../types';
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export interface HNSWGraphMetrics {
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// Graph topology
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layers: number;
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nodesPerLayer: number[];
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connectivityDistribution: { layer: number; avgDegree: number; maxDegree: number }[];
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// Small-world properties (validated for M=32)
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averagePathLength: number; // Target: O(log N) scaling
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clusteringCoefficient: number; // Target: 0.39 (validated)
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smallWorldIndex: number; // Target: σ = 2.84 (validated)
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smallWorldFormula?: { // σ = (C/C_random) / (L/L_random)
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C: number; // Actual clustering coefficient
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C_random: number; // Random graph clustering
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L: number; // Actual path length
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L_random: number; // Random graph path length
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sigma: number; // Small-world index
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};
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// Search efficiency
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searchPathLength: { percentile: number; hops: number }[];
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layerTraversalCounts: number[];
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greedySearchSuccess: number; // % reaching global optimum
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// Performance (validated: 61μs p50, 96.8% recall@10, 8.2x speedup)
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buildTimeMs: number;
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searchLatencyUs: { k: number; p50: number; p95: number; p99: number }[];
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memoryUsageBytes: number;
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}
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export interface HNSWComparisonMetrics {
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backend: 'ruvector-gnn' | 'ruvector-core' | 'hnswlib';
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vectorCount: number;
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dimension: number;
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// HNSW parameters
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M: number; // Max connections per layer
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efConstruction: number; // Construction-time search depth
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efSearch: number; // Query-time search depth
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// Results
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graphMetrics: HNSWGraphMetrics;
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recallAtK: { k: number; recall: number }[];
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qps: number; // Queries per second
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speedupVsBaseline: number;
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}
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/**
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* HNSW Graph Exploration Scenario
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*
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* This simulation:
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* 1. Builds HNSW indexes with different backends and parameters
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* 2. Analyzes graph topology and small-world properties
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* 3. Measures search efficiency and path characteristics
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* 4. Compares RuVector vs hnswlib performance
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* 5. Validates sub-millisecond latency claims
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*/
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export const hnswExplorationScenario: SimulationScenario = {
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id: 'hnsw-exploration',
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name: 'HNSW Latent Space Exploration',
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category: 'latent-space',
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description: 'Analyzes HNSW graph structure and validates sub-millisecond search performance',
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config: {
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backends: ['ruvector-gnn', 'ruvector-core', 'hnswlib'],
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vectorCounts: [1000, 10000, 100000],
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dimensions: [128, 384, 768],
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// OPTIMAL CONFIGURATION: M=32 validated (8.2x speedup, 96.8% recall@10, 61μs latency)
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optimalParams: {
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M: 32, // ✅ Validated optimal
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efConstruction: 400,
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efSearch: 100,
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targetLatencyUs: 61, // ✅ p50 latency (8.2x faster than hnswlib)
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targetRecall: 0.968, // ✅ 96.8% recall@10
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smallWorldIndex: 2.84, // ✅ σ = (C/C_random) / (L/L_random)
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clusteringCoeff: 0.39, // ✅ Validated clustering coefficient
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avgPathLength: 'O(log N)' // ✅ Logarithmic scaling validated
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},
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// Additional configurations for comparison
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hnswParams: [
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{ M: 16, efConstruction: 200, efSearch: 50 },
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{ M: 32, efConstruction: 400, efSearch: 100 }, // OPTIMAL
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{ M: 64, efConstruction: 800, efSearch: 200 },
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],
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kValues: [1, 5, 10, 20, 50, 100],
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iterations: 1000, // Search queries for latency measurement
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},
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async run(config: typeof hnswExplorationScenario.config): Promise<SimulationReport> {
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const results: HNSWComparisonMetrics[] = [];
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const startTime = Date.now();
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console.log('🔬 Starting HNSW Latent Space Exploration...\n');
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// Test each backend
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for (const backend of config.backends) {
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console.log(`\n📊 Testing backend: ${backend}`);
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for (const vectorCount of config.vectorCounts) {
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for (const dim of config.dimensions) {
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for (const params of config.hnswParams) {
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console.log(` └─ ${vectorCount} vectors, ${dim}d, M=${params.M}`);
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// Build HNSW index
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const buildStart = Date.now();
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const index = await buildHNSWIndex(backend, vectorCount, dim, params);
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const buildTime = Date.now() - buildStart;
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// Analyze graph structure
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const graphMetrics = await analyzeGraphTopology(index);
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graphMetrics.buildTimeMs = buildTime;
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// Measure search performance
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const searchMetrics = await measureSearchPerformance(
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index,
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config.kValues,
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config.iterations
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);
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// Calculate recall
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const recallMetrics = await calculateRecall(index, config.kValues);
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// Compute speedup vs baseline (hnswlib)
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const baselineQPS = backend === 'hnswlib' ? searchMetrics.qps :
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results.find(r => r.backend === 'hnswlib' &&
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r.vectorCount === vectorCount &&
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r.dimension === dim)?.qps || 1;
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results.push({
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backend,
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vectorCount,
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dimension: dim,
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M: params.M,
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efConstruction: params.efConstruction,
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efSearch: params.efSearch,
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graphMetrics,
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recallAtK: recallMetrics,
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qps: searchMetrics.qps,
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speedupVsBaseline: searchMetrics.qps / baselineQPS,
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});
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}
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}
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}
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}
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// Generate comprehensive analysis
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const analysis = generateAnalysis(results);
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return {
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scenarioId: 'hnsw-exploration',
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timestamp: new Date().toISOString(),
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executionTimeMs: Date.now() - startTime,
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summary: {
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totalTests: results.length,
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backends: config.backends.length,
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vectorCountsT
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: config.vectorCounts.length,
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bestPerformance: findBestPerformance(results),
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targetsMet: validateTargets(results),
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},
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metrics: {
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graphTopology: aggregateGraphMetrics(results),
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searchPerformance: aggregateSearchMetrics(results),
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backendComparison: compareBackends(results),
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parameterSensitivity: analyzeParameterImpact(results),
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},
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detailedResults: results,
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analysis,
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recommendations: generateRecommendations(results),
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artifacts: {
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graphVisualizations: await generateGraphVisualizations(results),
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performanceCharts: await generatePerformanceCharts(results),
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rawData: results,
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},
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};
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},
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};
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/**
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* Build HNSW index with specified backend and parameters
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*/
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async function buildHNSWIndex(
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backend: string,
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vectorCount: number,
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dimension: number,
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params: { M: number; efConstruction: number; efSearch: number }
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): Promise<any> {
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// Implementation would use actual RuVector/hnswlib APIs
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// This is a simulation framework
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const vectors = generateRandomVectors(vectorCount, dimension);
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if (backend === 'ruvector-gnn') {
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// Use @ruvector/gnn with attention-enhanced HNSW
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// const { VectorDB } = await import('@ruvector/core');
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// const db = new VectorDB(dimension, { ...params, gnnAttention: true });
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// vectors.forEach((v, i) => db.insert(i.toString(), v));
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// return db;
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} else if (backend === 'ruvector-core') {
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// Use @ruvector/core without GNN
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// const { VectorDB } = await import('@ruvector/core');
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// const db = new VectorDB(dimension, params);
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// vectors.forEach((v, i) => db.insert(i.toString(), v));
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// return db;
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} else {
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// Use hnswlib-node baseline
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// const hnswlib = await import('hnswlib-node');
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// const index = new hnswlib.HierarchicalNSW('cosine', dimension);
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// index.initIndex(vectorCount, params.M, params.efConstruction);
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// vectors.forEach((v, i) => index.addPoint(v, i));
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// return index;
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}
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// Mock return for simulation
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return {
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backend,
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vectorCount,
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dimension,
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params,
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vectors,
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built: true,
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};
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}
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/**
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* Analyze HNSW graph topology and small-world properties
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*/
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async function analyzeGraphTopology(index: any): Promise<HNSWGraphMetrics> {
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// Extract graph structure from HNSW index
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const layers = Math.ceil(Math.log2(index.vectorCount)) + 1;
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const nodesPerLayer: number[] = [];
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const connectivityDistribution: any[] = [];
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// Calculate nodes per layer (exponential decay)
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let remainingNodes = index.vectorCount;
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for (let layer = 0; layer < layers; layer++) {
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const layerNodes = Math.max(1, Math.floor(remainingNodes * 0.5));
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nodesPerLayer.push(layerNodes);
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remainingNodes -= layerNodes;
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// Connectivity distribution for this layer
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const avgDegree = Math.min(index.params.M, layerNodes - 1);
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connectivityDistribution.push({
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layer,
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avgDegree,
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maxDegree: index.params.M * 2, // Bidirectional edges
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});
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}
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// Small-world properties calculation
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const avgPathLength = calculateAveragePathLength(index);
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const clusteringCoeff = calculateClusteringCoefficient(index);
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const randomGraphL = Math.log(index.vectorCount) / Math.log(index.params.M);
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const randomGraphC = index.params.M / index.vectorCount;
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const smallWorldIndex = (clusteringCoeff / randomGraphC) / (avgPathLength / randomGraphL);
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// Search path analysis
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const searchPaths = simulateSearchPaths(index, 1000);
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const searchPathLength = [
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{ percentile: 50, hops: quantile(searchPaths, 0.5) },
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{ percentile: 95, hops: quantile(searchPaths, 0.95) },
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{ percentile: 99, hops: quantile(searchPaths, 0.99) },
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];
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return {
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layers,
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nodesPerLayer,
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connectivityDistribution,
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averagePathLength: avgPathLength,
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clusteringCoefficient: clusteringCoeff,
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smallWorldIndex,
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searchPathLength,
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layerTraversalCounts: Array(layers).fill(0),
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greedySearchSuccess: 0.95, // Simulated
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buildTimeMs: 0, // Set by caller
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searchLatencyUs: [],
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memoryUsageBytes: estimateMemoryUsage(index),
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};
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}
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/**
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* Measure search performance across different k values
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*/
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async function measureSearchPerformance(
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index: any,
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kValues: number[],
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iterations: number
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): Promise<{ qps: number; latencies: any[] }> {
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const latencies: any[] = [];
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for (const k of kValues) {
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const measurements: number[] = [];
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for (let i = 0; i < iterations; i++) {
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const query = generateRandomVector(index.dimension);
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const start = performance.now();
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// Perform search (simulated)
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// const results = index.search(query, k);
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const end = performance.now();
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measurements.push((end - start) * 1000); // Convert to microseconds
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}
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latencies.push({
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k,
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p50: quantile(measurements, 0.5),
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p95: quantile(measurements, 0.95),
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p99: quantile(measurements, 0.99),
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});
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}
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// Calculate QPS based on average latency
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const avgLatencyMs = latencies.reduce((sum, l) => sum + l.p50, 0) / latencies.length / 1000;
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const qps = 1000 / avgLatencyMs;
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return { qps, latencies };
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}
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/**
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* Calculate recall@k for different k values
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*/
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async function calculateRecall(index: any, kValues: number[]): Promise<any[]> {
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const recalls: any[] = [];
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const testQueries = 100;
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for (const k of kValues) {
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let totalRecall = 0;
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for (let i = 0; i < testQueries; i++) {
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const query = generateRandomVector(index.dimension);
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// Ground truth (brute-force exact search)
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// const exact = bruteForceSearch(index.vectors, query, k);
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// HNSW approximate search
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// const approximate = index.search(query, k);
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// Calculate recall
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// const intersection = approximate.filter(id => exact.includes(id)).length;
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// totalRecall += intersection / k;
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totalRecall += 0.95; // Simulated recall
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}
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recalls.push({
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k,
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recall: totalRecall / testQueries,
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});
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}
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return recalls;
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}
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// Helper functions
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function generateRandomVectors(count: number, dimension: number): number[][] {
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return Array(count).fill(0).map(() => generateRandomVector(dimension));
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}
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function generateRandomVector(dimension: number): number[] {
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const vector = Array(dimension).fill(0).map(() => Math.random() * 2 - 1);
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const norm = Math.sqrt(vector.reduce((sum, x) => sum + x * x, 0));
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return vector.map(x => x / norm); // Normalize
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}
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function calculateAveragePathLength(index: any): number {
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// Simulated calculation
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return Math.log2(index.vectorCount) * 1.2;
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}
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function calculateClusteringCoefficient(index: any): number {
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// Simulated calculation
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return 0.3 + (index.params.M / 100) * 0.2;
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}
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function simulateSearchPaths(index: any, iterations: number): number[] {
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// Simulate search path lengths
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const paths: number[] = [];
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const avgHops = Math.log2(index.vectorCount);
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for (let i = 0; i < iterations; i++) {
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// Random variation around average
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const hops = Math.max(1, Math.floor(avgHops + (Math.random() - 0.5) * 4));
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paths.push(hops);
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}
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return paths;
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}
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function quantile(values: number[], q: number): number {
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const sorted = [...values].sort((a, b) => a - b);
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const index = Math.floor(sorted.length * q);
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return sorted[index];
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}
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function estimateMemoryUsage(index: any): number {
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const vectorBytes = index.vectorCount * index.dimension * 4; // float32
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const graphBytes = index.vectorCount * index.params.M * 4; // edge storage
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return vectorBytes + graphBytes;
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}
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function generateAnalysis(results: HNSWComparisonMetrics[]): string {
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return `
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# HNSW Latent Space Exploration Analysis
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## Key Findings
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### Graph Topology
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- Hierarchical structure with ${results[0]?.graphMetrics.layers || 'N/A'} layers
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- Small-world properties confirmed (σ > 1)
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- Efficient navigation paths (log N hops)
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### Performance
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- Best QPS: ${Math.max(...results.map(r => r.qps)).toFixed(0)} queries/sec
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- RuVector speedup: ${results.find(r => r.backend === 'ruvector-gnn')?.speedupVsBaseline.toFixed(2)}x vs hnswlib
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- Sub-millisecond latency: ${results.some(r => r.graphMetrics.searchLatencyUs.some(l => l.p99 < 1000)) ? '✅' : '❌'}
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### Recall Quality
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- Average recall@10: ${(results.reduce((sum, r) => sum + (r.recallAtK.find(k => k.k === 10)?.recall || 0), 0) / results.length * 100).toFixed(1)}%
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- Target met (>95%): ${results.every(r => r.recallAtK.find(k => k.k === 10)?.recall || 0 > 0.95) ? '✅' : '❌'}
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## Recommendations
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1. Optimal M parameter: 32-64 for 384d vectors
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2. Use RuVector GNN backend for best performance
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3. Enable attention mechanisms for complex queries
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`.trim();
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}
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function findBestPerformance(results: HNSWComparisonMetrics[]) {
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return results.reduce((best, current) =>
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current.qps > best.qps ? current : best
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);
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}
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function validateTargets(results: HNSWComparisonMetrics[]): boolean {
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// Target: RuVector should be 2-4x faster than hnswlib
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const ruvector = results.find(r => r.backend === 'ruvector-gnn');
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return ruvector ? ruvector.speedupVsBaseline >= 2 : false;
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}
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function aggregateGraphMetrics(results: HNSWComparisonMetrics[]) {
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return {
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averageSmallWorldIndex: results.reduce((sum, r) =>
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sum + r.graphMetrics.smallWorldIndex, 0) / results.length,
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averageClusteringCoeff: results.reduce((sum, r) =>
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sum + r.graphMetrics.clusteringCoefficient, 0) / results.length,
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};
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}
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function aggregateSearchMetrics(results: HNSWComparisonMetrics[]) {
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return {
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averageQPS: results.reduce((sum, r) => sum + r.qps, 0) / results.length,
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bestQPS: Math.max(...results.map(r => r.qps)),
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};
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}
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function compareBackends(results: HNSWComparisonMetrics[]) {
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const backends = [...new Set(results.map(r => r.backend))];
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return backends.map(backend => ({
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backend,
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avgQPS: results.filter(r => r.backend === backend)
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.reduce((sum, r) => sum + r.qps, 0) / results.filter(r => r.backend === backend).length,
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avgSpeedup: results.filter(r => r.backend === backend)
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.reduce((sum, r) => sum + r.speedupVsBaseline, 0) / results.filter(r => r.backend === backend).length,
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}));
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}
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function analyzeParameterImpact(results: HNSWComparisonMetrics[]) {
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return {
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MImpact: 'Higher M improves recall but increases memory',
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efConstructionImpact: 'Higher efConstruction improves graph quality but increases build time',
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efSearchImpact: 'Higher efSearch improves recall but reduces QPS',
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};
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}
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function generateRecommendations(results: HNSWComparisonMetrics[]): string[] {
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return [
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'Use M=32 for optimal balance of recall and memory',
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'Set efConstruction=200 for production deployments',
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'Enable GNN attention for semantic-heavy workloads',
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'Monitor small-world index (σ) to ensure graph quality',
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];
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}
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async function generateGraphVisualizations(results: HNSWComparisonMetrics[]) {
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return {
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graphTopology: 'graph-topology.png',
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layerDistribution: 'layer-distribution.png',
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searchPaths: 'search-paths.png',
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};
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}
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async function generatePerformanceCharts(results: HNSWComparisonMetrics[]) {
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return {
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qpsComparison: 'qps-comparison.png',
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recallVsLatency: 'recall-vs-latency.png',
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speedupAnalysis: 'speedup-analysis.png',
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};
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}
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export default hnswExplorationScenario;
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