---

name: advanced-agentdb-vector-search-implementation description: Skill guidance for advanced agentdb vector search implementation.

Advanced AgentDB Vector Search Implementation

Overview

Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration for building distributed AI systems, multi-agent coordination, and advanced vector search applications.

When to Use This Skill

Use this skill when you need to:

• Build distributed vector search systems
• Implement multi-agent coordination with shared memory
• Create custom similarity metrics for specialized domains
• Deploy hybrid search combining vector and traditional methods
• Scale AgentDB to production with high availability
• Synchronize multiple AgentDB instances in real-time

SOP Framework: 5-Phase Advanced Vector Search Deployment

Phase 1: Setup AgentDB Infrastructure (2-3 hours)

Objective: Initialize multi-database AgentDB infrastructure with proper configuration

Agent: backend-dev

Steps:

1. Install AgentDB with advanced features

npm install agentdb-advanced@latest
npm install @agentdb/quic-sync @agentdb/distributed

2. Initialize primary database

import { AgentDB } from 'agentdb-advanced';
import { QUICSync } from '@agentdb/quic-sync';

const primaryDB = new AgentDB({
  name: 'primary-vector-db',
  dimensions: 1536, // OpenAI embedding size
  indexType: 'hnsw',
  distanceMetric: 'cosine',
  persistPath: './data/primary',
  advanced: {
    enableQUIC: true,
    multiDB: true,
    hybridSearch: true
  }
});

await primaryDB.initialize();

3. Configure replica databases

const replicas = await Promise.all([
  AgentDB.createReplica('replica-1', {
    primary: primaryDB,
    syncMode: 'quic',
    persistPath: './data/replica-1'
  }),
  AgentDB.createReplica('replica-2', {
    primary: primaryDB,
    syncMode: 'quic',
    persistPath: './data/replica-2'
  })
]);

4. Setup health monitoring

const monitor = primaryDB.createMonitor({
  checkInterval: 5000,
  metrics: ['latency', 'throughput', 'replication-lag'],
  alerts: {
    replicationLag: 1000, // ms
    errorRate: 0.01
  }
});

monitor.on('alert', (alert) => {
  console.error('Database alert:', alert);
});

Memory Pattern:

await agentDB.memory.store('agentdb/infrastructure/config', {
  primary: primaryDB.id,
  replicas: replicas.map(r => r.id),
  syncMode: 'quic',
  timestamp: Date.now()
});

Validation:

• Primary database initialized
• Replicas connected and syncing
• Health monitor active
• Configuration stored in memory

Evidence-Based Validation:

// Self-consistency check across replicas
const testVector = Array(1536).fill(0).map(() => Math.random());
await primaryDB.insert({ id: 'test-1', vector: testVector });

// Wait for sync
await new Promise(resolve => setTimeout(resolve, 100));

// Verify consistency
const checks = await Promise.all(
  replicas.map(r => r.get('test-1'))
);

const consistent = checks.every(c =>
  c && vectorEquals(c.vector, testVector)
);

console.log('Consistency check:', consistent ? 'PASS' : 'FAIL');

Phase 2: Configure Advanced Features (2-3 hours)

Objective: Setup QUIC synchronization, multi-DB coordination, and advanced routing

Agent: ml-developer

Steps:

1. Configure QUIC synchronization

import { QUICConfig } from '@agentdb/quic-sync';

const quicSync = new QUICSync({
  primary: primaryDB,
  replicas: replicas,
  config: {
    maxStreams: 100,
    idleTimeout: 30000,
    keepAlive: 5000,
    congestionControl: 'cubic',
    prioritization: 'weighted-round-robin'
  }
});

await quicSync.start();

// Monitor sync performance
quicSync.on('sync-complete', (stats) => {
  console.log('Sync stats:', {
    duration: stats.duration,
    vectorsSynced: stats.count,
    throughput: stats.count / (stats.duration / 1000)
  });
});

2. Implement multi-database router

import { MultiDBRouter } from '@agentdb/distributed';

const router = new MultiDBRouter({
  databases: [primaryDB, ...replicas],
  strategy: 'load-balanced', // or 'nearest', 'round-robin'
  healthCheck: {
    interval: 5000,
    timeout: 1000
  }
});

// Query routing
const searchResults = await router.search({
  vector: queryVector,
  topK: 10,
  strategy: 'fan-out-merge' // Query all, merge results
});

3. Setup distributed coordination

import { DistributedCoordinator } from '@agentdb/distributed';

const coordinator = new DistributedCoordinator({
  databases: [primaryDB, ...replicas],
  consensus: 'raft', // or 'gossip', 'quorum'
  leaderElection: true
});

await coordinator.start();

// Handle leadership changes
coordinator.on('leader-elected', (leader) => {
  console.log('New leader:', leader.id);
  primaryDB = leader;
});

4. Configure failover policies

const failoverPolicy = {
  maxRetries: 3,
  retryDelay: 1000,
  fallbackStrategy: 'replica-promotion',
  autoRecovery: true
};

router.setFailoverPolicy(failoverPolicy);

Memory Pattern:

await agentDB.memory.store('agentdb/advanced/quic-config', {
  syncMode: 'quic',
  streams: quicSync.activeStreams,
  routingStrategy: 'load-balanced',
  coordinator: coordinator.id
});

Validation:

• QUIC sync operational
• Router distributing load
• Coordinator elected leader
• Failover tested

Evidence-Based Validation:

// Program-of-thought: Test multi-DB coordination
async function validateCoordination() {
  // Step 1: Insert on primary
  const testId = 'coord-test-' + Date.now();
  await primaryDB.insert({ id: testId, vector: testVector });

  // Step 2: Wait for QUIC sync
  await quicSync.waitForSync(testId, { timeout: 2000 });

  // Step 3: Query through router
  const results = await router.search({
    vector: testVector,
    topK: 1,
    filter: { id: testId }
  });

  // Step 4: Verify result from any replica
  return results[0]?.id === testId;
}

const coordValid = await validateCoordination();
console.log('Coordination validation:', coordValid ? 'PASS' : 'FAIL');

Phase 3: Implement Custom Distance Metrics (2-3 hours)

Objective: Create specialized distance functions for domain-specific similarity

Agent: ml-developer

Steps:

1. Define custom metric interface

import { DistanceMetric } from 'agentdb-advanced';

interface CustomMetricConfig {
  name: string;
  weightedDimensions?: number[];
  transformFunction?: (vector: number[]) => number[];
  combineMetrics?: {
    metrics: string[];
    weights: number[];
  };
}

2. Implement weighted Euclidean distance

const weightedEuclidean: DistanceMetric = {
  name: 'weighted-euclidean',
  compute: (a: number[], b: number[], weights?: number[]) => {
    if (!weights) weights = Array(a.length).fill(1);

    let sum = 0;
    for (let i = 0; i < a.length; i++) {
      sum += weights[i] * Math.pow(a[i] - b[i], 2);
    }
    return Math.sqrt(sum);
  },
  normalize: true
};

primaryDB.registerMetric(weightedEuclidean);

3. Create hybrid metric (vector + scalar)

const hybridSimilarity: DistanceMetric = {
  name: 'hybrid-similarity',
  compute: (a: number[], b: number[], metadata?: any) => {
    // Vector similarity (cosine)
    const dotProduct = a.reduce((sum, val, i) => sum + val * b[i], 0);
    const magA = Math.sqrt(a.reduce((sum, val) => sum + val * val, 0));
    const magB = Math.sqrt(b.reduce((sum, val) => sum + val * val, 0));
    const cosineSim = dotProduct / (magA * magB);

    // Scalar similarity (if metadata present)
    let scalarSim = 0;
    if (metadata) {
      scalarSim = 1 - Math.abs(metadata.timestamp - Date.now()) / 1e9;
    }

    // Combine (70% vector, 30% scalar)
    return 0.7 * (1 - cosineSim) + 0.3 * (1 - scalarSim);
  }
};

primaryDB.registerMetric(hybridSimilarity);

4. Implement domain-specific metrics

// Example: Code similarity metric
const codeSimilarity: DistanceMetric = {
  name: 'code-similarity',
  compute: (a: number[], b: number[], metadata?: any) => {
    // Vector component
    const vectorDist = cosineDistance(a, b);

    // Syntactic similarity
    const syntaxSim = metadata?.ast_similarity || 0;

    // Semantic similarity
    const semanticSim = metadata?.semantic_similarity || 0;

    // Weighted combination
    return 0.5 * vectorDist + 0.3 * (1 - syntaxSim) + 0.2 * (1 - semanticSim);
  }
};

primaryDB.registerMetric(codeSimilarity);

5. Benchmark custom metrics

async function benchmarkMetrics() {
  const testVectors = generateTestVectors(1000);
  const queryVector = testVectors[0];

  const metrics = ['cosine', 'euclidean', 'weighted-euclidean', 'hybrid-similarity'];
  const results: Record<string, any> = {};

  for (const metric of metrics) {
    const start = performance.now();
    const searchResults = await primaryDB.search({
      vector: queryVector,
      topK: 10,
      metric: metric
    });
    const duration = performance.now() - start;

    results[metric] = {
      duration,
      results: searchResults.length,
      accuracy: calculateAccuracy(searchResults)
    };
  }

  return results;
}

const benchmark = await benchmarkMetrics();
await agentDB.memory.store('agentdb/metrics/benchmark', benchmark);

Memory Pattern:

await agentDB.memory.store('agentdb/custom-metrics/registry', {
  metrics: ['weighted-euclidean', 'hybrid-similarity', 'code-similarity'],
  benchmark: benchmark,
  recommended: 'hybrid-similarity'
});

Validation:

• Custom metrics registered
• Metrics produce valid distances
• Benchmark results collected
• Best metric identified

Evidence-Based Validation:

// Chain-of-verification: Validate metric properties
async function verifyMetricProperties(metric: string) {
  const checks = {
    nonNegativity: true,
    symmetry: true,
    triangleInequality: true
  };

  const testVectors = [
    Array(1536).fill(0).map(() => Math.random()),
    Array(1536).fill(0).map(() => Math.random()),
    Array(1536).fill(0).map(() => Math.random())
  ];

  // Check non-negativity
  const d1 = await primaryDB.distance(testVectors[0], testVectors[1], metric);
  checks.nonNegativity = d1 >= 0;

  // Check symmetry: d(a,b) = d(b,a)
  const d2 = await primaryDB.distance(testVectors[1], testVectors[0], metric);
  checks.symmetry = Math.abs(d1 - d2) < 1e-6;

  // Check triangle inequality: d(a,c) <= d(a,b) + d(b,c)
  const dac = await primaryDB.distance(testVectors[0], testVectors[2], metric);
  const dbc = await primaryDB.distance(testVectors[1], testVectors[2], metric);
  checks.triangleInequality = dac <= d1 + dbc + 1e-6;

  return checks;
}

const metricValid = await verifyMetricProperties('hybrid-similarity');
console.log('Metric validation:', metricValid);

Phase 4: Optimize Performance (2-3 hours)

Objective: Apply indexing, caching, and optimization techniques for production performance

Agent: performance-analyzer

Steps:

1. Configure HNSW indexing

await primaryDB.createIndex({
  type: 'hnsw',
  params: {
    M: 16, // Number of connections per layer
    efConstruction: 200, // Construction time accuracy
    efSearch: 100, // Search time accuracy
    maxElements: 1000000
  }
});

// Enable index for all replicas
await Promise.all(
  replicas.map(r => r.syncIndex(primaryDB))
);

2. Implement query caching

import { QueryCache } from '@agentdb/optimization';

const cache = new QueryCache({
  maxSize: 10000,
  ttl: 3600000, // 1 hour
  strategy: 'lru',
  hashFunction: 'xxhash64'
});

primaryDB.setCache(cache);

// Cache hit monitoring
cache.on('hit', (key, entry) => {
  console.log('Cache hit:', { key, age: Date.now() - entry.timestamp });
});

3. Enable quantization

import { Quantization } from '@agentdb/optimization';

const quantizer = new Quantization({
  method: 'product-quantization',
  codebookSize: 256,
  subvectors: 8,
  compressionRatio: 4 // 4x memory reduction
});

await primaryDB.applyQuantization(quantizer);

// Verify accuracy after quantization
const accuracyTest = await benchmarkAccuracy(primaryDB, testQueries);
console.log('Post-quantization accuracy:', accuracyTest);

4. Batch operations

import { BatchProcessor } from '@agentdb/optimization';

const batchProcessor = new BatchProcessor({
  batchSize: 1000,
  flushInterval: 5000,
  parallelBatches: 4
});

// Batch inserts
const vectors = generateVectors(10000);
await batchProcessor.insertBatch(primaryDB, vectors);

// Batch searches
const queries = generateQueries(100);
const results = await batchProcessor.searchBatch(primaryDB, queries, {
  topK: 10,
  parallel: true
});

5. Performance benchmarking

async function comprehensiveBenchmark() {
  const benchmark = {
    insertThroughput: 0,
    searchLatency: 0,
    searchThroughput: 0,
    memoryUsage: 0,
    cacheHitRate: 0
  };

  // Insert throughput
  const insertStart = performance.now();
  await batchProcessor.insertBatch(primaryDB, generateVectors(10000));
  benchmark.insertThroughput = 10000 / ((performance.now() - insertStart) / 1000);

  // Search latency (p50, p95, p99)
  const latencies: number[] = [];
  for (let i = 0; i < 1000; i++) {
    const start = performance.now();
    await primaryDB.search({ vector: generateQuery(), topK: 10 });
    latencies.push(performance.now() - start);
  }
  latencies.sort((a, b) => a - b);
  benchmark.searchLatency = {
    p50: latencies[Math.floor(latencies.length * 0.5)],
    p95: latencies[Math.floor(latencies.length * 0.95)],
    p99: latencies[Math.floor(latencies.length * 0.99)]
  };

  // Memory usage
  benchmark.memoryUsage = await primaryDB.getMemoryUsage();

  // Cache hit rate
  const cacheStats = cache.getStats();
  benchmark.cacheHitRate = cacheStats.hits / (cacheStats.hits + cacheStats.misses);

  return benchmark;
}

const perfResults = await comprehensiveBenchmark();
await agentDB.memory.store('agentdb/optimization/benchmark', perfResults);

Memory Pattern:

await agentDB.memory.store('agentdb/optimization/config', {
  indexing: { type: 'hnsw', params: {...} },
  caching: { enabled: true, hitRate: perfResults.cacheHitRate },
  quantization: { method: 'product-quantization', ratio: 4 },
  performance: perfResults
});

Validation:

• HNSW index built and synced
• Cache operational with good hit rate
• Quantization maintains accuracy
• Performance meets targets (>150x improvement)

Evidence-Based Validation:

// Multi-agent consensus on performance targets
async function validatePerformanceTargets() {
  const targets = {
    searchLatencyP95: 10, // ms
    insertThroughput: 50000, // vectors/sec
    memoryEfficiency: 4, // compression ratio
    cacheHitRate: 0.7 // 70%
  };

  const results = await comprehensiveBenchmark();

  const validations = {
    latency: results.searchLatency.p95 <= targets.searchLatencyP95,
    throughput: results.insertThroughput >= targets.insertThroughput,
    memory: results.memoryUsage.compressionRatio >= targets.memoryEfficiency,
    cache: results.cacheHitRate >= targets.cacheHitRate
  };

  const allPass = Object.values(validations).every(v => v);

  return { validations, allPass, results };
}

const perfValidation = await validatePerformanceTargets();
console.log('Performance validation:', perfValidation);

Phase 5: Deploy and Monitor (2-3 hours)

Objective: Deploy to production with monitoring, alerting, and operational procedures

Agent: backend-dev

Steps:

1. Setup production configuration

const productionConfig = {
  cluster: {
    primary: {
      host: process.env.PRIMARY_HOST,
      port: parseInt(process.env.PRIMARY_PORT),
      replicas: 2
    },
    replicas: [
      { host: process.env.REPLICA1_HOST, port: parseInt(process.env.REPLICA1_PORT) },
      { host: process.env.REPLICA2_HOST, port: parseInt(process.env.REPLICA2_PORT) }
    ]
  },
  monitoring: {
    enabled: true,
    exporters: ['prometheus', 'cloudwatch'],
    alerts: {
      replicationLag: 1000,
      errorRate: 0.01,
      latencyP95: 50
    }
  },
  backup: {
    enabled: true,
    interval: 3600000, // 1 hour
    retention: 7 // days
  }
};

await deployCluster(productionConfig);

2. Implement monitoring dashboards

import { MetricsExporter } from '@agentdb/monitoring';

const exporter = new MetricsExporter({
  exporters: [
    {
      type: 'prometheus',
      port: 9090,
      metrics: [
        'agentdb_search_latency',
        'agentdb_insert_throughput',
        'agentdb_replication_lag',
        'agentdb_cache_hit_rate',
        'agentdb_memory_usage'
      ]
    },
    {
      type: 'cloudwatch',
      namespace: 'AgentDB/Production',
      region: 'us-east-1'
    }
  ]
});

await exporter.start();

// Custom metrics
exporter.registerMetric('agentdb_custom_queries', 'counter',
  'Custom metric queries executed'
);

3. Configure alerting

import { AlertManager } from '@agentdb/monitoring';

const alertManager = new AlertManager({
  channels: [
    { type: 'email', recipients: ['ops@company.com'] },
    { type: 'slack', webhook: process.env.SLACK_WEBHOOK },
    { type: 'pagerduty', apiKey: process.env.PAGERDUTY_KEY }
  ],
  rules: [
    {
      metric: 'agentdb_replication_lag',
      condition: '> 1000',
      severity: 'critical',
      message: 'Replication lag exceeds 1 second'
    },
    {
      metric: 'agentdb_search_latency_p95',
      condition: '> 50',
      severity: 'warning',
      message: 'Search latency P95 exceeds 50ms'
    },
    {
      metric: 'agentdb_error_rate',
      condition: '> 0.01',
      severity: 'critical',
      message: 'Error rate exceeds 1%'
    }
  ]
});

await alertManager.start();

4. Implement health checks

import express from 'express';

const healthApp = express();

healthApp.get('/health', async (req, res) => {
  const health = {
    status: 'healthy',
    timestamp: Date.now(),
    databases: await Promise.all([
      primaryDB.healthCheck(),
      ...replicas.map(r => r.healthCheck())
    ]),
    quic: quicSync.isHealthy(),
    coordinator: coordinator.getStatus()
  };

  const allHealthy = health.databases.every(db => db.status === 'healthy');

  res.status(allHealthy ? 200 : 503).json(health);
});

healthApp.get('/metrics', async (req, res) => {
  const metrics = await exporter.getMetrics();
  res.set('Content-Type', 'text/plain');
  res.send(metrics);
});

healthApp.listen(8080);

5. Create operational runbook

const runbook = {
  deployment: {
    steps: [
      '1. Verify configuration in production.env',
      '2. Deploy primary database first',
      '3. Deploy replicas with QUIC sync enabled',
      '4. Verify replication lag < 100ms',
      '5. Enable monitoring and alerting',
      '6. Run smoke tests',
      '7. Gradually increase traffic'
    ]
  },
  troubleshooting: {
    'High replication lag': [
      'Check network connectivity between nodes',
      'Verify QUIC streams are not saturated',
      'Consider increasing QUIC maxStreams',
      'Check primary database load'
    ],
    'Slow search queries': [
      'Verify HNSW index is built',
      'Check cache hit rate',
      'Review query patterns',
      'Consider adjusting efSearch parameter'
    ],
    'Leader election failure': [
      'Check coordinator logs',
      'Verify quorum availability',
      'Check network partitions',
      'Manually trigger election if needed'
    ]
  },
  backup: {
    schedule: 'Hourly incremental, daily full',
    retention: '7 days',
    restore: [
      '1. Stop affected database instance',
      '2. Download backup from S3',
      '3. Restore data directory',
      '4. Start database with --recovery flag',
      '5. Verify data integrity',
      '6. Rejoin cluster'
    ]
  }
};

await agentDB.memory.store('agentdb/production/runbook', runbook);

Memory Pattern:

await agentDB.memory.store('agentdb/production/deployment', {
  config: productionConfig,
  deployed: Date.now(),
  monitoring: {
    dashboards: ['prometheus:3000', 'grafana:3001'],
    alerts: alertManager.getRules()
  },
  runbook: runbook
});

Validation:

• Production cluster deployed
• Monitoring active and exporting metrics
• Alerts configured and tested
• Health checks returning 200
• Runbook documented

Evidence-Based Validation:

// Self-consistency check: Production readiness
async function validateProductionReadiness() {
  const checks = {
    deployment: false,
    monitoring: false,
    alerting: false,
    healthChecks: false,
    backup: false,
    documentation: false
  };

  // Check deployment
  const clusterStatus = await coordinator.getClusterStatus();
  checks.deployment = clusterStatus.healthy && clusterStatus.nodes.length >= 3;

  // Check monitoring
  const metrics = await exporter.getMetrics();
  checks.monitoring = metrics.length > 0;

  // Check alerting
  const alertStatus = await alertManager.getStatus();
  checks.alerting = alertStatus.active && alertStatus.channels.length > 0;

  // Check health endpoint
  const healthResponse = await fetch('http://localhost:8080/health');
  checks.healthChecks = healthResponse.status === 200;

  // Check backup configuration
  const backupStatus = await checkBackupStatus();
  checks.backup = backupStatus.enabled && backupStatus.lastBackup !== null;

  // Check documentation
  const docs = await agentDB.memory.retrieve('agentdb/production/runbook');
  checks.documentation = docs !== null;

  const readiness = Object.values(checks).every(c => c);

  return { checks, readiness };
}

const prodReadiness = await validateProductionReadiness();
console.log('Production readiness:', prodReadiness);

Integration Scripts

Complete Deployment Script

#!/bin/bash
# deploy-advanced-agentdb.sh

set -e

echo "Advanced AgentDB Deployment Script"
echo "==================================="

# Phase 1: Infrastructure Setup
echo "Phase 1: Setting up infrastructure..."
npm install agentdb-advanced @agentdb/quic-sync @agentdb/distributed @agentdb/optimization @agentdb/monitoring

# Phase 2: Initialize databases
echo "Phase 2: Initializing databases..."
node -e "
const { AgentDB } = require('agentdb-advanced');
const primary = new AgentDB({
  name: 'primary',
  dimensions: 1536,
  advanced: { enableQUIC: true, multiDB: true }
});
await primary.initialize();
console.log('Primary database initialized');
"

# Phase 3: Deploy replicas
echo "Phase 3: Deploying replicas..."
for i in 1 2; do
  node -e "
  const { AgentDB } = require('agentdb-advanced');
  const replica = await AgentDB.createReplica('replica-$i', {
    syncMode: 'quic'
  });
  console.log('Replica $i deployed');
  "
done

# Phase 4: Configure monitoring
echo "Phase 4: Configuring monitoring..."
node -e "
const { MetricsExporter } = require('@agentdb/monitoring');
const exporter = new MetricsExporter({
  exporters: [{ type: 'prometheus', port: 9090 }]
});
await exporter.start();
console.log('Monitoring configured');
"

# Phase 5: Run validation
echo "Phase 5: Running validation..."
npm run test:integration

echo "Deployment complete!"

Quick Start Script

// quickstart-advanced.ts
import { setupAdvancedAgentDB } from './setup';

async function quickStart() {
  console.log('Starting Advanced AgentDB Quick Setup...');

  // 1. Setup infrastructure
  const { primary, replicas, router } = await setupAdvancedAgentDB({
    dimensions: 1536,
    replicaCount: 2,
    enableQUIC: true
  });

  // 2. Load sample data
  console.log('Loading sample data...');
  const vectors = generateSampleVectors(10000);
  await router.insertBatch(vectors);

  // 3. Test searches
  console.log('Testing searches...');
  const query = vectors[0];
  const results = await router.search({
    vector: query,
    topK: 10,
    metric: 'cosine'
  });
  console.log('Search results:', results.length);

  // 4. Verify replication
  console.log('Verifying replication...');
  const syncStatus = await router.getSyncStatus();
  console.log('Replication lag:', syncStatus.lag, 'ms');

  console.log('Quick setup complete!');
}

quickStart().catch(console.error);

Memory Coordination Patterns

// Store cluster configuration
await memory.store('agentdb/cluster/config', {
  topology: 'distributed',
  nodes: [primary, ...replicas],
  syncMode: 'quic',
  timestamp: Date.now()
});

// Store performance metrics
await memory.store('agentdb/metrics/latest', {
  searchLatency: perfResults.searchLatency,
  throughput: perfResults.insertThroughput,
  cacheHitRate: perfResults.cacheHitRate
});

// Store custom metrics registry
await memory.store('agentdb/metrics/custom', {
  registered: ['weighted-euclidean', 'hybrid-similarity'],
  active: 'hybrid-similarity',
  benchmarks: benchmark
});

// Store operational state
await memory.store('agentdb/operations/state', {
  deployed: true,
  healthy: true,
  leader: coordinator.getLeader(),
  lastBackup: backupTimestamp
});

Evidence-Based Success Criteria

1. Multi-Database Consistency (Self-Consistency)

   • All replicas return identical results for same query
   • Replication lag < 100ms
   • Zero data loss during failover

2. Performance Targets (Benchmarking)

   • Search latency P95 < 10ms
   • Insert throughput > 50,000 vectors/sec
   • Memory efficiency 4x compression
   • Cache hit rate > 70%

3. Custom Metrics Validity (Chain-of-Verification)

   • Metrics satisfy mathematical properties
   • Metrics improve domain-specific accuracy
   • Metrics perform within latency budget

4. Production Readiness (Multi-Agent Consensus)

   • Monitoring exports all required metrics
   • Alerts fire correctly in test scenarios
   • Health checks pass consistently
   • Runbook covers common failure modes

Troubleshooting Guide

Issue: High replication lag

// Diagnose
const diagnostics = await quicSync.diagnose();
console.log('QUIC diagnostics:', diagnostics);

// Fix: Increase stream capacity
await quicSync.reconfigure({
  maxStreams: 200,
  congestionControl: 'bbr'
});

Issue: Slow queries after quantization

// Check accuracy loss
const accuracy = await benchmarkAccuracy(primaryDB, testQueries);
if (accuracy < 0.95) {
  // Adjust quantization parameters
  await primaryDB.applyQuantization({
    method: 'product-quantization',
    compressionRatio: 2 // Reduce compression
  });
}

Issue: Cache thrashing

// Analyze cache patterns
const cacheStats = cache.getDetailedStats();
console.log('Cache stats:', cacheStats);

// Adjust cache size or TTL
cache.reconfigure({
  maxSize: 20000, // Increase size
  ttl: 7200000 // Increase TTL to 2 hours
});

Success Metrics

• 150x faster search vs baseline
• 4-32x memory reduction with quantization
• Multi-database synchronization < 100ms lag
• Custom metrics improve accuracy by 15-30%
• 99.9% uptime in production
• Health checks pass continuously

MCP Requirements

This skill operates using AgentDB's npm package and API only. No additional MCP servers required.

All AgentDB advanced operations are performed through:

• npm CLI: npx agentdb@latest
• TypeScript/JavaScript API: import { AgentDB, QUICSync, DistributedCoordinator } from 'agentdb-advanced'

Additional Resources

• AgentDB Advanced Documentation: https://agentdb.dev/docs/advanced
• QUIC Synchronization Guide: https://agentdb.dev/docs/quic
• Custom Metrics Tutorial: https://agentdb.dev/docs/metrics
• Production Deployment Best Practices: https://agentdb.dev/docs/production

Core Principles

Advanced AgentDB Vector Search Implementation operates on 3 fundamental principles:

Principle 1: QUIC Synchronization - Low-Latency Replication for Distributed Consistency

Traditional TCP-based replication suffers from head-of-line blocking (one lost packet stalls entire stream) and connection setup overhead (3-way handshake adds 50-150ms latency). QUIC's multiplexed streams and 0-RTT connection resumption enable sub-100ms replication lag across distributed databases, maintaining consistency without sacrificing performance.

In practice:

• Configure maxStreams=100 to parallelize replication of 100 concurrent vector updates without blocking
• Use cubic congestion control for high-bandwidth datacenter links, BBR for variable network conditions
• Set keepAlive=5000ms to detect replica failures quickly and trigger failover within 10 seconds
• Monitor replication lag continuously - spikes above 100ms indicate network saturation or replica overload requiring capacity scaling

Principle 2: Custom Distance Metrics - Domain-Specific Similarity Beyond Cosine/Euclidean

Generic distance metrics (cosine, Euclidean) assume all vector dimensions contribute equally to similarity. Domain-specific applications require weighted dimensions (emphasizing recent timestamps for time-series), hybrid metrics (combining vector and scalar features), or specialized functions (code similarity using AST structure + embeddings).

In practice:

• Implement weighted distance metrics assigning importance to specific dimensions (e.g., 2x weight to entity names in knowledge graphs)
• Create hybrid metrics combining vector similarity (70% weight) with scalar features like timestamps, popularity scores, or categorical matches (30% weight)
• Validate custom metrics satisfy mathematical properties (non-negativity, symmetry, triangle inequality) to ensure HNSW index correctness
• Benchmark custom metrics against baselines - improvement in domain-specific accuracy (precision@10, recall@100) justifies added complexity

Principle 3: Fan-Out Merge Routing - Distribute Queries Across Replicas for Load Balancing

Single-node vector search creates bottlenecks at scale. Fan-out routing distributes queries across multiple replicas in parallel, merges results, and balances load to maximize throughput. This enables horizontal scaling where adding replicas proportionally increases query capacity.

In practice:

• Use fan-out-merge strategy for read-heavy workloads: query all replicas concurrently, merge top-K results client-side
• Apply load-balanced routing for mixed read/write workloads: direct queries to least-loaded replica based on health checks
• Implement nearest-region routing for geo-distributed deployments: query local replicas first, fallback to remote on failure
• Monitor per-replica query latency - imbalanced load (>20% variance) indicates routing strategy needs adjustment or replica capacity rebalancing

Common Anti-Patterns

Anti-Pattern	Problem	Solution
Synchronous Replication Blocking Writes	Requiring all replicas to acknowledge before write returns creates latency spikes (200ms+ for 3 replicas across regions). Writes become bottlenecked by slowest replica or network partition.	Use asynchronous QUIC replication with eventual consistency. Primary returns immediately after local write, QUIC streams propagate to replicas in background. Accept <100ms replication lag for 10x write throughput improvement. Implement read-your-writes consistency where needed.
Custom Metric Without Validation - Break HNSW Index Assumptions	HNSW indexes assume distance metrics satisfy triangle inequality and symmetry. Violating these properties causes index corruption, incorrect nearest neighbors, and silent accuracy degradation.	Implement comprehensive metric validation (Phase 3 chain-of-verification): test non-negativity d(a,b)>=0, symmetry d(a,b)=d(b,a), triangle inequality d(a,c)<=d(a,b)+d(b,c). Run validation on 1000+ random vector triples before deploying metric to production.
Query All Replicas Always - Waste Network and Compute on Low-Traffic Systems	Fan-out-merge routing sends every query to all replicas regardless of load. For low-traffic systems (100 QPS), this wastes 3x network bandwidth and compute capacity while adding merge latency overhead.	Implement adaptive routing: use single-replica for low load (<1000 QPS), load-balanced for medium (1K-10K QPS), fan-out-merge only for high load (>10K QPS). Monitor query latency distribution and switch strategies based on p95 latency targets.

Conclusion

Advanced AgentDB Vector Search Implementation enables production-scale distributed vector databases through three sophisticated capabilities: QUIC synchronization for sub-100ms replication lag, custom distance metrics for domain-specific similarity, and multi-database routing for horizontal scalability. The 5-phase SOP systematically guides infrastructure setup, advanced feature configuration, custom metric implementation, performance optimization, and production deployment with comprehensive monitoring and operational runbooks. This transforms basic vector search into enterprise-grade systems handling millions of queries per second with 99.9%+ uptime.

This skill is critical when scaling beyond single-node deployments to handle massive query volumes (>10K QPS), implementing high-availability architectures with automatic failover, or building domain-specific search requiring specialized similarity functions. The key architectural decision is choosing the right synchronization model - synchronous replication guarantees consistency but limits write throughput, asynchronous QUIC replication enables high-speed writes with eventual consistency trade-offs. Most production systems choose asynchronous with <100ms replication lag as the optimal balance.

Custom distance metrics unlock powerful domain applications but require rigorous validation. Generic cosine similarity fails for tasks like code search (needs AST structure), document retrieval (needs recency weighting), or fraud detection (needs anomaly scoring). The metric validation framework prevents silent accuracy degradation by verifying mathematical properties before production deployment. Combined with HNSW indexing, quantization, and caching from the optimization skill, you achieve both specialized similarity functions and sub-10ms query latency - enabling intelligent search experiences that feel magical to users while remaining cost-effective at scale. The comprehensive monitoring, alerting, and runbooks ensure systems remain operational through network partitions, replica failures, and traffic spikes that inevitably occur in production environments.