HNSW Tuning

const realtime = new VectoriaDB({
  useHNSW: true,
  hnsw: {
    M: 16,
    efConstruction: 200,
    efSearch: 40, // Low for speed
  },
});

const precision = new VectoriaDB({
  useHNSW: true,
  hnsw: {
    M: 24,
    efConstruction: 400,
    efSearch: 200, // High for accuracy
  },
});

const lowMemory = new VectoriaDB({
  useHNSW: true,
  hnsw: {
    M: 8,  // Lower M uses less memory
    efConstruction: 100,
    efSearch: 30,
  },
});

async function search(query: string, precision: 'low' | 'medium' | 'high') {
  const efSearch = {
    low: 20,
    medium: 50,
    high: 200,
  }[precision];

  return db.search(query, {
    topK: 10,
    efSearch,
  });
}

// Fast search for autocomplete
const suggestions = await search(input, 'low');

// High-precision search for final results
const results = await search(query, 'high');

async function benchmarkHNSW(config: HNSWConfig) {
  const db = new VectoriaDB({ useHNSW: true, hnsw: config });
  await db.initialize();

  // Build benchmark
  const buildStart = Date.now();
  await db.addMany(documents);
  const buildTime = Date.now() - buildStart;

  // Search benchmark
  const searchTimes: number[] = [];
  for (const query of testQueries) {
    const start = Date.now();
    await db.search(query);
    searchTimes.push(Date.now() - start);
  }

  const avgSearchTime = searchTimes.reduce((a, b) => a + b) / searchTimes.length;

  return { buildTime, avgSearchTime };
}