agentdb-advanced-features

const adapter = await createAgentDBAdapter({
  enableQUICSync: true,
  syncPort: 4433,              // QUIC server port
  syncPeers: ['host1:4433'],   // Peer addresses
  syncInterval: 1000,          // Sync interval (ms)
  syncBatchSize: 100,          // Patterns per batch
  maxRetries: 3,               // Retry failed syncs
  compression: true,           // Enable compression
});

# Node 1 (192.168.1.10)
AGENTDB_QUIC_SYNC=true \
AGENTDB_QUIC_PORT=4433 \
AGENTDB_QUIC_PEERS=192.168.1.11:4433,192.168.1.12:4433 \
node server.js
 
# Node 2 (192.168.1.11)
AGENTDB_QUIC_SYNC=true \
AGENTDB_QUIC_PORT=4433 \
AGENTDB_QUIC_PEERS=192.168.1.10:4433,192.168.1.12:4433 \
node server.js
 
# Node 3 (192.168.1.12)
AGENTDB_QUIC_SYNC=true \
AGENTDB_QUIC_PORT=4433 \
AGENTDB_QUIC_PEERS=192.168.1.10:4433,192.168.1.11:4433 \
node server.js

# CLI
npx agentdb@latest query .$vectors.db "[0.1,0.2,...]" -m cosine
 
# API
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  metric: 'cosine',
  k: 10,
});

# CLI
npx agentdb@latest query .$vectors.db "[0.1,0.2,...]" -m euclidean
 
# API
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  metric: 'euclidean',
  k: 10,
});

# CLI
npx agentdb@latest query .$vectors.db "[0.1,0.2,...]" -m dot
 
# API
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  metric: 'dot',
  k: 10,
});

// Implement custom distance function
function customDistance(vec1: number[], vec2: number[]): number {
  // Weighted Euclidean distance
  const weights = [1.0, 2.0, 1.5, ...];
  let sum = 0;
  for (let i = 0; i < vec1.length; i++) {
    sum += weights[i] * Math.pow(vec1[i] - vec2[i], 2);
  }
  return Math.sqrt(sum);
}
 
// Use in search (requires custom implementation)

// Store documents with metadata
await adapter.insertPattern({
  id: '',
  type: 'document',
  domain: 'research-papers',
  pattern_data: JSON.stringify({
    embedding: documentEmbedding,
    text: documentText,
    metadata: {
      author: 'Jane Smith',
      year: 2025,
      category: 'machine-learning',
      citations: 150,
    }
  }),
  confidence: 1.0,
  usage_count: 0,
  success_count: 0,
  created_at: Date.now(),
  last_used: Date.now(),
});
 
// Hybrid search: vector similarity + metadata filters
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'research-papers',
  k: 20,
  filters: {
    year: { $gte: 2023 },          // Published 2023 or later
    category: 'machine-learning',   // ML papers only
    citations: { $gte: 50 },       // Highly cited
  },
});

// Complex metadata queries
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'products',
  k: 50,
  filters: {
    price: { $gte: 10, $lte: 100 },      // Price range
    category: { $in: ['electronics', 'gadgets'] },  // Multiple categories
    rating: { $gte: 4.0 },                // High rated
    inStock: true,                        // Available
    tags: { $contains: 'wireless' },      // Has tag
  },
});

const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'content',
  k: 20,
  hybridWeights: {
    vectorSimilarity: 0.7,  // 70% weight on semantic similarity
    metadataScore: 0.3,     // 30% weight on metadata match
  },
  filters: {
    category: 'technology',
    recency: { $gte: Date.now() - 30 * 24 * 3600000 },  // Last 30 days
  },
});

// Separate databases for different domains
const knowledgeDB = await createAgentDBAdapter({
  dbPath: '.agentdb$knowledge.db',
});
 
const conversationDB = await createAgentDBAdapter({
  dbPath: '.agentdb$conversations.db',
});
 
const codeDB = await createAgentDBAdapter({
  dbPath: '.agentdb$code.db',
});
 
// Use appropriate database for each task
await knowledgeDB.insertPattern({ /* knowledge */ });
await conversationDB.insertPattern({ /* conversation */ });
await codeDB.insertPattern({ /* code */ });

// Shard by domain for horizontal scaling
const shards = {
  'domain-a': await createAgentDBAdapter({ dbPath: '.agentdb$shard-a.db' }),
  'domain-b': await createAgentDBAdapter({ dbPath: '.agentdb$shard-b.db' }),
  'domain-c': await createAgentDBAdapter({ dbPath: '.agentdb$shard-c.db' }),
};
 
// Route queries to appropriate shard
function getDBForDomain(domain: string) {
  const shardKey = domain.split('-')[0];  // Extract shard key
  return shards[shardKey] || shards['domain-a'];
}
 
// Insert to correct shard
const db = getDBForDomain('domain-a-task');
await db.insertPattern({ /* ... */ });

// Without MMR: Similar results may be redundant
const standardResults = await adapter.retrieveWithReasoning(queryEmbedding, {
  k: 10,
  useMMR: false,
});
 
// With MMR: Diverse, non-redundant results
const diverseResults = await adapter.retrieveWithReasoning(queryEmbedding, {
  k: 10,
  useMMR: true,
  mmrLambda: 0.5,  // Balance relevance (0) vs diversity (1)
});

const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'problem-solving',
  k: 10,
  synthesizeContext: true,  // Enable context synthesis
});
 
// ContextSynthesizer creates coherent narrative
console.log('Synthesized Context:', result.context);
// "Based on 10 similar problem-solving attempts, the most effective
//  approach involves: 1) analyzing root cause, 2) brainstorming solutions,
//  3) evaluating trade-offs, 4) implementing incrementally. Success rate: 85%"
 
console.log('Patterns:', result.patterns);
// Extracted common patterns across memories

// Singleton pattern for shared adapter
class AgentDBPool {
  private static instance: AgentDBAdapter;
 
  static async getInstance() {
    if (!this.instance) {
      this.instance = await createAgentDBAdapter({
        dbPath: '.agentdb$production.db',
        quantizationType: 'scalar',
        cacheSize: 2000,
      });
    }
    return this.instance;
  }
}
 
// Use in application
const db = await AgentDBPool.getInstance();
const results = await db.retrieveWithReasoning(queryEmbedding, { k: 10 });

async function safeRetrieve(queryEmbedding: number[], options: any) {
  try {
    const result = await adapter.retrieveWithReasoning(queryEmbedding, options);
    return result;
  } catch (error) {
    if (error.code === 'DIMENSION_MISMATCH') {
      console.error('Query embedding dimension mismatch');
      // Handle dimension error
    } else if (error.code === 'DATABASE_LOCKED') {
      // Retry with exponential backoff
      await new Promise(resolve => setTimeout(resolve, 100));
      return safeRetrieve(queryEmbedding, options);
    }
    throw error;
  }
}

// Performance monitoring
const startTime = Date.now();
const result = await adapter.retrieveWithReasoning(queryEmbedding, { k: 10 });
const latency = Date.now() - startTime;
 
if (latency > 100) {
  console.warn('Slow query detected:', latency, 'ms');
}
 
// Log statistics
const stats = await adapter.getStats();
console.log('Database Stats:', {
  totalPatterns: stats.totalPatterns,
  dbSize: stats.dbSize,
  cacheHitRate: stats.cacheHitRate,
  avgSearchLatency: stats.avgSearchLatency,
});

# Export with compression
npx agentdb@latest export .$vectors.db .$backup.json.gz --compress
 
# Import from backup
npx agentdb@latest import .$backup.json.gz --decompress
 
# Merge databases
npx agentdb@latest merge .$db1.sqlite .$db2.sqlite .$merged.sqlite

# Vacuum database (reclaim space)
sqlite3 .agentdb$vectors.db "VACUUM;"
 
# Analyze for query optimization
sqlite3 .agentdb$vectors.db "ANALYZE;"
 
# Rebuild indices
npx agentdb@latest reindex .$vectors.db

# AgentDB configuration
AGENTDB_PATH=.agentdb$reasoningbank.db
AGENTDB_ENABLED=true
 
# Performance tuning
AGENTDB_QUANTIZATION=binary     # binary|scalar|product|none
AGENTDB_CACHE_SIZE=2000
AGENTDB_HNSW_M=16
AGENTDB_HNSW_EF=100
 
# Learning plugins
AGENTDB_LEARNING=true
 
# Reasoning agents
AGENTDB_REASONING=true
 
# QUIC synchronization
AGENTDB_QUIC_SYNC=true
AGENTDB_QUIC_PORT=4433
AGENTDB_QUIC_PEERS=host1:4433,host2:4433

# Check firewall allows UDP port 4433
# NOTE: Requires administrator privileges - for reference only
sudo ufw allow 4433/udp
 
# Verify peers are reachable
ping host1
 
# Check QUIC logs
DEBUG=agentdb:quic node server.js

// Relax filters
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  k: 100,  // Increase k
  filters: {
    // Remove or relax filters
  },
});

// Disable automatic optimization
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  optimizeMemory: false,  // Disable auto-consolidation
  k: 10,
});