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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, and hybrid search for distributed AI systems.
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SKILL.md を表示中
| skill_id | when-using-advanced-vector-search-use-agentdb-advanced |
| name | advanced-agentdb-vector-search-implementation |
| description | Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, and hybrid search for distributed AI systems. |
| version | 1.0.0 |
| category | agentdb |
| subcategory | distributed-systems |
| trigger_pattern | when-using-advanced-vector-search |
| agents | ["ml-developer","backend-dev","performance-analyzer"] |
| complexity | advanced |
| estimated_duration | 8-12 hours |
| prerequisites | ["Basic AgentDB knowledge","Vector database concepts","Distributed systems understanding","TypeScript/Node.js proficiency"] |
| outputs | ["Multi-database AgentDB cluster","QUIC synchronization system","Custom distance metrics","Hybrid search implementation","Performance benchmarks"] |
| validation_criteria | ["150x faster search vs baseline","Multi-DB synchronization working","Custom metrics functional","Hybrid search accurate"] |
| evidence_based_techniques | ["Self-consistency validation","Program-of-thought decomposition","Chain-of-verification","Multi-agent consensus"] |
| metadata | {"author":"claude-flow","created":"2025-10-30T00:00:00.000Z","updated":"2025-10-30T00:00:00.000Z","tags":["agentdb","vector-search","distributed-systems","quic-sync","hybrid-search"]} |
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.
Use this skill when you need to:
Objective: Initialize multi-database AgentDB infrastructure with proper configuration
Agent: backend-dev
Steps:
npm install agentdb-advanced@latest
npm install @agentdb/quic-sync @agentdb/distributed
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: ,
: ,
:
}
});
primaryDB.();
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'
})
]);
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:
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');
Objective: Setup QUIC synchronization, multi-DB coordination, and advanced routing
Agent: ml-developer
Steps:
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)
});
});
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
});
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;
});
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:
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');
Objective: Create specialized distance functions for domain-specific similarity
Agent: ml-developer
Steps:
import { DistanceMetric } from 'agentdb-advanced';
interface CustomMetricConfig {
name: string;
weightedDimensions?: number[];
transformFunction?: (vector: number[]) => number[];
combineMetrics?: {
metrics: string[];
weights: number[];
};
}
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);
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);
// 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);
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:
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);
Objective: Apply indexing, caching, and optimization techniques for production performance
Agent: performance-analyzer
Steps:
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))
);
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 });
});
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);
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
});
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:
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);
Objective: Deploy to production with monitoring, alerting, and operational procedures
Agent: backend-dev
Steps:
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);
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'
);
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();
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);
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:
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);
#!/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!"
// 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);
// 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
});
Multi-Database Consistency (Self-Consistency)
Performance Targets (Benchmarking)
Custom Metrics Validity (Chain-of-Verification)
Production Readiness (Multi-Agent Consensus)
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
});