Agentdb Advanced Features

# AgentDB Advanced Features

## What This Skill Does

Covers advanced AgentDB capabilities for distributed systems, multi-database coordination, custom distance metrics, hybrid search (vector + metadata), QUIC synchronization, and production deployment patterns. Enables building sophisticated AI systems with sub-millisecond cross-node communication and advanced search capabilities.

**Performance**: <1ms QUIC sync, hybrid search with filters, custom distance metrics.

## Prerequisites

- Node.js 18+
- AgentDB v1.0.7+ (via agentic-flow)
- Understanding of distributed systems (for QUIC sync)
- Vector search fundamentals

---

## QUIC Synchronization

### What is QUIC Sync?

QUIC (Quick UDP Internet Connections) enables sub-millisecond latency synchronization between AgentDB instances across network boundaries with automatic retry, multiplexing, and encryption.

**Benefits**:
- <1ms latency between nodes
- Multiplexed streams (multiple operations simultaneously)
- Built-in encryption (TLS 1.3)
- Automatic retry and recovery
- Event-based broadcasting

### Enable QUIC Sync

```typescript
import { createAgentDBAdapter } from 'agentic-flow$reasoningbank';

// Initialize with QUIC synchronization
const adapter = await createAgentDBAdapter({
  dbPath: '.agentdb$distributed.db',
  enableQUICSync: true,
  syncPort: 4433,
  syncPeers: [
    '192.168.1.10:4433',
    '192.168.1.11:4433',
    '192.168.1.12:4433',
  ],
});

// Patterns automatically sync across all peers
await adapter.insertPattern({
  // ... pattern data
});

// Available on all peers within ~1ms
```

### QUIC Configuration

```typescript
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
});
```

### Multi-Node Deployment

```bash
# 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
```

---

## Distance Metrics

### Cosine Similarity (Default)

Best for normalized vectors, semantic similarity:

```bash
# CLI
npx agentdb@latest query .$vectors.db "[0.1,0.2,...]" -m cosine

# API
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  metric: 'cosine',
  k: 10,
});
```

**Use Cases**:
- Text embeddings (BERT, GPT, etc.)
- Semantic search
- Document similarity
- Most general-purpose applications

**Formula**: `cos(θ) = (A · B) / (||A|| × ||B||)`
**Range**: [-1, 1] (1 = identical, -1 = opposite)

### Euclidean Distance (L2)

Best for spatial data, geometric similarity:

```bash
# CLI
npx agentdb@latest query .$vectors.db "[0.1,0.2,...]" -m euclidean

# API
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  metric: 'euclidean',
  k: 10,
});
```

**Use Cases**:
- Image embeddings
- Spatial data
- Computer vision
- When vector magnitude matters

**Formula**: `d = √(Σ(ai - bi)²)`
**Range**: [0, ∞] (0 = identical, ∞ = very different)

### Dot Product

Best for pre-normalized vectors, fast computation:

```bash
# CLI
npx agentdb@latest query .$vectors.db "[0.1,0.2,...]" -m dot

# API
const result = await adapter.ret