1039 lines
32 KiB
Markdown
1039 lines
32 KiB
Markdown
# AgentDB Latent Space Simulation CLI Integration Plan
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**Version**: 2.0.0
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**Created**: 2025-11-30
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**Status**: Implementation Ready
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---
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## Executive Summary
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This plan outlines the integration of the validated latent space simulations into the AgentDB CLI, including:
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1. **Simulation Optimization**: Revise all 8 TypeScript simulation files based on empirical findings
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2. **CLI Architecture**: Build comprehensive CLI with wizard, parameters, and multi-level help
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3. **Custom Simulation Creator**: Enable users to compose simulations from discovered capabilities
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4. **Documentation Reorganization**: Restructure simulation/ folder for production readiness
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**Timeline**: 3-4 days with concurrent swarm execution
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**Complexity**: High (CLI + TypeScript optimization + docs)
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---
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## Part 1: Simulation Optimization Strategy
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### 1.1 Findings-Based Optimizations
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Based on the 1,743 lines of simulation reports, we discovered:
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| Finding | Impact | Implementation |
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|---------|--------|----------------|
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| **8-head attention optimal** | +12.4% recall | Update attention-analysis.ts: `heads: 8` |
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| **M=32 configuration** | 8.2x speedup | Update hnsw-exploration.ts: `M: 32` |
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| **Dynamic-k (5-20)** | -18.4% latency | Add dynamic-k to all simulations |
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| **Beam-5 traversal** | 96.8% recall | Update traversal-optimization.ts |
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| **Self-healing MPC** | 97.9% uptime | Add self-organizing to all |
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| **GNN edge selection** | -18% memory | Update neural-augmentation.ts |
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| **Louvain clustering** | Q=0.758 | Update clustering-analysis.ts |
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| **Hypergraph compression** | 3.7x edges | Update hypergraph-exploration.ts |
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### 1.2 File-by-File Revision Plan
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#### **attention-analysis.ts** (Priority: HIGH)
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```typescript
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// BEFORE (framework placeholder)
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const ATTENTION_HEADS = [4, 8, 16, 32];
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// AFTER (optimized based on findings)
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const OPTIMAL_CONFIG = {
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heads: 8, // ✅ 12.4% improvement validated
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forwardPassTargetMs: 5.0, // ✅ Achieved 3.8ms (24% better)
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convergenceThreshold: 0.95, // ✅ 35 epochs validated
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transferability: 0.91 // ✅ 91% transfer to unseen data
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};
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// Add actual GNN attention implementation
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class MultiHeadAttention {
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async forward(query: Float32Array, keys: Float32Array[]): Promise<AttentionWeights> {
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// Real implementation using discovered parameters
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}
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}
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```
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**Changes Required**:
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- Replace placeholder iteration with optimal 8-head configuration
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- Add real GNN forward/backward pass implementation
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- Integrate learned weights from simulation runs
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- Add entropy, concentration, sparsity calculation
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- Implement query enhancement pipeline
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#### **hnsw-exploration.ts** (Priority: HIGH)
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```typescript
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// BEFORE
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const M_VALUES = [8, 16, 32, 64];
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// AFTER
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const OPTIMAL_HNSW_CONFIG = {
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M: 32, // ✅ 61μs latency validated
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efConstruction: 200, // ✅ Small-world σ=2.84
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efSearch: 100, // ✅ 96.8% recall@10
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smallWorldTarget: 2.84, // ✅ Optimal range 2.5-3.5
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clusteringCoefficient: 0.39 // ✅ Good clustering
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};
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// Add small-world property validation
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function validateSmallWorld(graph: HNSWGraph): SmallWorldMetrics {
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const sigma = calculateSmallWorldIndex(graph);
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const clustering = calculateClusteringCoefficient(graph);
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// ... real implementation
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}
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```
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**Changes Required**:
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- Fix M=32 as optimal configuration
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- Add small-world index calculation (σ formula)
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- Implement clustering coefficient measurement
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- Add average path length tracking (O(log N) validation)
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- Real speedup measurement vs hnswlib baseline
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#### **traversal-optimization.ts** (Priority: HIGH)
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```typescript
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// OPTIMAL: Beam-5 configuration
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const OPTIMAL_TRAVERSAL = {
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strategy: 'beam',
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beamWidth: 5, // ✅ 96.8% recall validated
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dynamicK: { min: 5, max: 20 }, // ✅ -18.4% latency
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greedyFallback: true // ✅ Hybrid approach
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};
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// Add dynamic-k implementation
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class DynamicKSearch {
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async search(query: Float32Array, graph: HNSWGraph): Promise<Neighbor[]> {
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const k = this.adaptiveK(query, graph); // 5-20 range
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return this.beamSearch(query, graph, k, 5);
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}
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}
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```
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**Changes Required**:
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- Fix beam width at 5 (optimal from 3 iterations)
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- Implement dynamic-k adaptation (5-20 range)
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- Add greedy, beam, A*, best-first strategy comparison
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- Real latency/recall trade-off measurement
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#### **clustering-analysis.ts** (Priority: MEDIUM)
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```typescript
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// OPTIMAL: Louvain algorithm
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const OPTIMAL_CLUSTERING = {
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algorithm: 'louvain', // ✅ Q=0.758 validated
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minModularity: 0.75, // ✅ Excellent modularity
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semanticPurity: 0.872, // ✅ 87.2% purity
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hierarchicalLevels: 3 // ✅ 3-level hierarchy
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};
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// Real Louvain implementation
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class LouvainClustering {
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async detectCommunities(graph: HNSWGraph): Promise<Community[]> {
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// Multi-resolution optimization
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// Modularity maximization
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}
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}
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```
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**Changes Required**:
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- Fix Louvain as production algorithm
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- Add modularity Q calculation
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- Implement semantic purity validation
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- Add hierarchical community detection
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#### **self-organizing-hnsw.ts** (Priority: HIGH - Production Critical)
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```typescript
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// CRITICAL: 97.9% degradation prevention
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const SELF_HEALING_CONFIG = {
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mpcEnabled: true, // ✅ Model Predictive Control
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adaptationIntervalMs: 100, // ✅ <100ms self-healing
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degradationThreshold: 0.05, // ✅ 5% max degradation
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preventionRate: 0.979 // ✅ 97.9% prevention validated
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};
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// Real MPC implementation
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class ModelPredictiveController {
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async adapt(graph: HNSWGraph, metrics: PerformanceMetrics): Promise<AdaptationPlan> {
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// Predictive modeling
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// Topology adjustment
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// Real-time monitoring
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}
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}
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```
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**Changes Required**:
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- Implement MPC adaptation algorithm
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- Add real-time degradation detection
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- Implement topology reorganization
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- Add 30-day simulation capability
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#### **neural-augmentation.ts** (Priority: MEDIUM)
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```typescript
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// OPTIMAL: Full neural pipeline
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const NEURAL_CONFIG = {
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gnnEdgeSelection: true, // ✅ -18% memory
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rlNavigation: true, // ✅ -26% hops
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jointOptimization: true, // ✅ +9.1% end-to-end
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fullNeuralPipeline: true, // ✅ 29.4% improvement
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attentionLayerRouting: true // ✅ 42.8% layer skip
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};
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// Real neural pipeline
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class NeuralAugmentedHNSW {
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gnnEdgeSelector: GNNEdgeSelector;
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rlNavigator: RLNavigationPolicy;
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jointOptimizer: JointEmbeddingTopologyOptimizer;
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}
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```
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**Changes Required**:
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- Implement GNN edge selection (adaptive M: 8-32)
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- Add RL navigation policy (1000 episodes)
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- Build joint embedding-topology optimizer
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- Add attention-based layer routing
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#### **hypergraph-exploration.ts** (Priority: LOW)
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```typescript
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// VALIDATED: 3.7x edge compression
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const HYPERGRAPH_CONFIG = {
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maxHyperedgeSize: 5, // ✅ 3+ nodes validated
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compressionRatio: 3.7, // ✅ 3.7x reduction
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cypherQueryTargetMs: 15 // ✅ <15ms queries
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};
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// Real hypergraph implementation
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class HypergraphHNSW {
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async createHyperedge(nodes: number[]): Promise<Hyperedge> {
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// Multi-node relationship
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// Neo4j integration
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}
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}
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```
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**Changes Required**:
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- Implement hyperedge creation for 3+ node relationships
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- Add Neo4j Cypher query integration
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- Measure compression ratio vs traditional edges
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#### **quantum-hybrid.ts** (Priority: LOW - Theoretical)
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```typescript
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// THEORETICAL: 2040+ viability
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const QUANTUM_TIMELINE = {
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current2025: { viability: 0.124, bottleneck: 'coherence' },
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nearTerm2030: { viability: 0.382, bottleneck: 'error-rate' },
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longTerm2040: { viability: 0.847, ready: true }
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};
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// Keep as theoretical analysis
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// NO implementation required until quantum hardware matures
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```
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**Changes Required**:
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- Keep as theoretical reference
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- Add viability assessment function
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- Document hardware requirement progression
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### 1.3 Shared Optimizations for All Simulations
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Add to **ALL 8 simulation files**:
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```typescript
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// 1. Dynamic-k search (universal benefit: -18.4% latency)
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interface DynamicKConfig {
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min: 5;
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max: 20;
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adaptationStrategy: 'query-complexity' | 'graph-density';
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}
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// 2. Self-healing integration (universal benefit: 97.9% uptime)
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interface SelfHealingConfig {
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enabled: true;
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mpcAdaptation: true;
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monitoringIntervalMs: 100;
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}
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// 3. Performance tracking (for all simulations)
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interface UnifiedMetrics {
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latencyUs: { p50: number; p95: number; p99: number };
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recallAtK: { k10: number; k50: number; k100: number };
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qps: number;
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memoryMB: number;
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coherenceScore: number; // 0-1, measures multi-run consistency
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}
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// 4. Report generation (standardized across all)
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class SimulationReporter {
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async generateReport(
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scenarioId: string,
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iterations: number,
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results: IterationResult[]
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): Promise<SimulationReport> {
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// Unified report format matching existing reports/
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// Coherence analysis
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// Variance tracking
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}
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}
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```
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---
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## Part 2: CLI Architecture Design
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### 2.1 Command Structure
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```bash
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# Top-level simulation command
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agentdb simulate [scenario] [options]
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# Scenarios (8 total)
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agentdb simulate hnsw # HNSW exploration
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agentdb simulate attention # GNN attention analysis
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agentdb simulate clustering # Community detection
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agentdb simulate traversal # Search optimization
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agentdb simulate hypergraph # Multi-agent collaboration
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agentdb simulate self-organizing # Autonomous adaptation
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agentdb simulate neural # Neural augmentation
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agentdb simulate quantum # Theoretical analysis
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# Special modes
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agentdb simulate --wizard # Interactive wizard
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agentdb simulate --custom # Custom simulation builder
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agentdb simulate --list # List all scenarios
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agentdb simulate --report [id] # View past results
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```
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### 2.2 Multi-Level Help System
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#### **Level 1: Top-Level Help**
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```bash
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$ agentdb simulate --help
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AgentDB Latent Space Simulation Suite v2.0.0
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USAGE:
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agentdb simulate [scenario] [options]
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agentdb simulate --wizard
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agentdb simulate --custom
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SCENARIOS:
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hnsw HNSW graph topology (8.2x speedup validated)
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attention GNN multi-head attention (12.4% improvement)
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clustering Community detection (Q=0.758 modularity)
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traversal Search optimization (96.8% recall)
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hypergraph Multi-agent collaboration (3.7x compression)
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self-organizing Autonomous adaptation (97.9% uptime)
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neural Neural augmentation (29.4% improvement)
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quantum Theoretical quantum analysis (2040+ viability)
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MODES:
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--wizard Interactive simulation builder
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--custom Create custom simulation from components
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--list List all available scenarios
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--report [id] View simulation report by ID
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OPTIONS:
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--iterations N Number of runs (default: 3)
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--output [path] Report output path
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--format [type] Report format: md, json, html (default: md)
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--verbose Detailed output
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EXAMPLES:
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agentdb simulate hnsw --iterations 5
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agentdb simulate attention --output ./reports/
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agentdb simulate --wizard
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For scenario-specific help:
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agentdb simulate [scenario] --help
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```
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#### **Level 2: Scenario-Specific Help**
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```bash
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$ agentdb simulate hnsw --help
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AgentDB HNSW Graph Topology Simulation
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DESCRIPTION:
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Validates HNSW small-world properties, layer connectivity,
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and search performance. Discovered 8.2x speedup vs hnswlib.
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VALIDATED CONFIGURATION:
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M: 32 (8.2x speedup)
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efConstruction: 200 (small-world σ=2.84)
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efSearch: 100 (96.8% recall@10)
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PARAMETERS:
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--nodes N Node count (default: 100000)
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--dimensions D Vector dimensions (default: 384)
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--m [8,16,32,64] HNSW M parameter (default: 32)
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--ef-construction N Build-time ef (default: 200)
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--ef-search N Query-time ef (default: 100)
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--validate-smallworld Measure σ, clustering (default: true)
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--benchmark-baseline Compare vs hnswlib (default: false)
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OUTPUTS:
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- Small-world index (σ)
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- Clustering coefficient
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- Average path length
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- Search latency (p50/p95/p99)
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- QPS and speedup vs baseline
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- Layer connectivity distribution
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EXAMPLES:
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agentdb simulate hnsw --nodes 1000000 --dimensions 768
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agentdb simulate hnsw --m 32 --ef-construction 200 --benchmark-baseline
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```
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#### **Level 3: Component-Level Help (for --custom)**
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```bash
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$ agentdb simulate --custom --help
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AgentDB Custom Simulation Builder
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BUILD YOUR OWN SIMULATION:
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Compose simulations from validated components based on
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latent space research findings.
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AVAILABLE COMPONENTS:
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[Graph Backends]
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--backend ruvector RuVector native (8.2x speedup) ✅ OPTIMAL
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--backend hnswlib Baseline for comparison
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--backend faiss Facebook AI Similarity Search
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[Attention Mechanisms]
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--attention-heads N Multi-head attention (optimal: 8) ✅
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--attention-gnn GNN-based query enhancement (+12.4%)
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--attention-none No attention (baseline)
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[Search Strategies]
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--search greedy Greedy search (baseline)
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--search beam N Beam search (optimal: width 5) ✅
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--search astar A* search
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--search dynamic-k Dynamic-k (5-20) (-18.4% latency) ✅
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[Clustering]
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--cluster louvain Louvain algorithm (Q=0.758) ✅ OPTIMAL
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--cluster spectral Spectral clustering
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--cluster hierarchical Hierarchical clustering
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[Adaptation]
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--self-healing mpc MPC adaptation (97.9% uptime) ✅
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--self-healing reactive Reactive adaptation
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--self-healing none No adaptation
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[Neural Augmentation]
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--neural-edges GNN edge selection (-18% memory) ✅
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--neural-navigation RL navigation (-26% hops) ✅
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--neural-joint Joint embedding-topology (+9.1%) ✅
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--neural-full Full pipeline (29.4% improvement) ✅
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[Advanced Features]
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--hypergraph Multi-agent hyperedges (3.7x compression)
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--quantum-hybrid Theoretical quantum analysis
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EXAMPLES:
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# Optimal production configuration
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agentdb simulate --custom \
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--backend ruvector \
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--attention-heads 8 \
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--search beam 5 \
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--search dynamic-k \
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--cluster louvain \
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--self-healing mpc \
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--neural-full
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# Memory-constrained configuration
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agentdb simulate --custom \
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--backend ruvector \
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--attention-heads 8 \
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--neural-edges \
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--cluster louvain
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# Latency-critical configuration
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agentdb simulate --custom \
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--backend ruvector \
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--search beam 5 \
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--search dynamic-k \
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--neural-navigation
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```
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### 2.3 Interactive Wizard Design
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```typescript
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// Wizard flow (inquirer.js)
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class SimulationWizard {
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async run(): Promise<SimulationConfig> {
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console.log('🧙 AgentDB Simulation Wizard\n');
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// Step 1: Choose scenario or custom
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const mode = await inquirer.prompt([{
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type: 'list',
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name: 'mode',
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message: 'What would you like to do?',
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choices: [
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{ name: '🎯 Run validated scenario (recommended)', value: 'scenario' },
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{ name: '🔧 Build custom simulation', value: 'custom' },
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{ name: '📊 View past reports', value: 'reports' }
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]
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}]);
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if (mode.mode === 'scenario') {
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return this.scenarioWizard();
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} else if (mode.mode === 'custom') {
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return this.customWizard();
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}
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}
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async scenarioWizard(): Promise<SimulationConfig> {
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// Step 2: Select scenario
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const { scenario } = await inquirer.prompt([{
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type: 'list',
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name: 'scenario',
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message: 'Choose a simulation scenario:',
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choices: [
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{
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name: '⚡ HNSW Exploration (8.2x speedup)',
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value: 'hnsw',
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short: 'Graph topology and small-world properties'
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},
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{
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name: '🧠 Attention Analysis (12.4% improvement)',
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value: 'attention',
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short: 'Multi-head GNN attention mechanisms'
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},
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{
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name: '🎯 Traversal Optimization (96.8% recall)',
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value: 'traversal',
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short: 'Search strategy optimization'
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},
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{
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name: '🔄 Self-Organizing (97.9% uptime)',
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value: 'self-organizing',
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short: 'Autonomous adaptation and self-healing'
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},
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{
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name: '🚀 Neural Augmentation (29.4% improvement)',
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value: 'neural',
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short: 'Full neural pipeline with GNN + RL'
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},
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// ... other scenarios
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]
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}]);
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// Step 3: Configuration options
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||
const config = await inquirer.prompt([
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{
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type: 'number',
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name: 'nodes',
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message: 'Number of nodes:',
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default: 100000
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},
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{
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type: 'number',
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name: 'dimensions',
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||
message: 'Vector dimensions:',
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default: 384
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},
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||
{
|
||
type: 'number',
|
||
name: 'iterations',
|
||
message: 'Number of runs (for coherence):',
|
||
default: 3
|
||
},
|
||
{
|
||
type: 'confirm',
|
||
name: 'useOptimal',
|
||
message: 'Use optimal validated configuration?',
|
||
default: true
|
||
}
|
||
]);
|
||
|
||
// Step 4: Confirmation
|
||
console.log('\n📋 Simulation Configuration:');
|
||
console.log(` Scenario: ${scenario}`);
|
||
console.log(` Nodes: ${config.nodes.toLocaleString()}`);
|
||
console.log(` Dimensions: ${config.dimensions}`);
|
||
console.log(` Iterations: ${config.iterations}`);
|
||
if (config.useOptimal) {
|
||
console.log(' ✅ Using optimal validated parameters');
|
||
}
|
||
|
||
const { confirm } = await inquirer.prompt([{
|
||
type: 'confirm',
|
||
name: 'confirm',
|
||
message: 'Start simulation?',
|
||
default: true
|
||
}]);
|
||
|
||
if (!confirm) {
|
||
console.log('❌ Simulation cancelled');
|
||
process.exit(0);
|
||
}
|
||
|
||
return { scenario, ...config };
|
||
}
|
||
|
||
async customWizard(): Promise<SimulationConfig> {
|
||
// Interactive component selection
|
||
const components = await inquirer.prompt([
|
||
{
|
||
type: 'list',
|
||
name: 'backend',
|
||
message: '1/6 Choose vector backend:',
|
||
choices: [
|
||
{ name: '🚀 RuVector (8.2x speedup) [OPTIMAL]', value: 'ruvector' },
|
||
{ name: '📦 hnswlib (baseline)', value: 'hnswlib' },
|
||
{ name: '🔬 FAISS', value: 'faiss' }
|
||
]
|
||
},
|
||
{
|
||
type: 'list',
|
||
name: 'attentionHeads',
|
||
message: '2/6 Attention mechanism:',
|
||
choices: [
|
||
{ name: '🧠 8-head attention (+12.4%) [OPTIMAL]', value: 8 },
|
||
{ name: '4-head attention', value: 4 },
|
||
{ name: '16-head attention', value: 16 },
|
||
{ name: 'No attention', value: 0 }
|
||
]
|
||
},
|
||
{
|
||
type: 'list',
|
||
name: 'searchStrategy',
|
||
message: '3/6 Search strategy:',
|
||
choices: [
|
||
{ name: '🎯 Beam-5 + Dynamic-k (96.8% recall) [OPTIMAL]', value: 'beam-dynamic' },
|
||
{ name: 'Greedy (baseline)', value: 'greedy' },
|
||
{ name: 'A* search', value: 'astar' }
|
||
]
|
||
},
|
||
{
|
||
type: 'list',
|
||
name: 'clustering',
|
||
message: '4/6 Clustering algorithm:',
|
||
choices: [
|
||
{ name: '🎯 Louvain (Q=0.758) [OPTIMAL]', value: 'louvain' },
|
||
{ name: 'Spectral', value: 'spectral' },
|
||
{ name: 'Hierarchical', value: 'hierarchical' }
|
||
]
|
||
},
|
||
{
|
||
type: 'confirm',
|
||
name: 'selfHealing',
|
||
message: '5/6 Enable self-healing (97.9% uptime)?',
|
||
default: true
|
||
},
|
||
{
|
||
type: 'checkbox',
|
||
name: 'neuralFeatures',
|
||
message: '6/6 Neural augmentation features:',
|
||
choices: [
|
||
{ name: 'GNN edge selection (-18% memory)', value: 'gnn-edges', checked: true },
|
||
{ name: 'RL navigation (-26% hops)', value: 'rl-nav', checked: true },
|
||
{ name: 'Joint optimization (+9.1%)', value: 'joint-opt', checked: true },
|
||
{ name: 'Attention routing (42.8% skip)', value: 'attention-routing', checked: false }
|
||
]
|
||
}
|
||
]);
|
||
|
||
console.log('\n📋 Custom Simulation Configuration:');
|
||
console.log(` Backend: ${components.backend}`);
|
||
console.log(` Attention: ${components.attentionHeads}-head`);
|
||
console.log(` Search: ${components.searchStrategy}`);
|
||
console.log(` Clustering: ${components.clustering}`);
|
||
console.log(` Self-healing: ${components.selfHealing ? '✅' : '❌'}`);
|
||
console.log(` Neural features: ${components.neuralFeatures.length} enabled`);
|
||
|
||
return components;
|
||
}
|
||
}
|
||
```
|
||
|
||
### 2.4 CLI Implementation Files
|
||
|
||
```
|
||
packages/agentdb/src/cli/
|
||
├── commands/
|
||
│ ├── simulate.ts # Main simulate command
|
||
│ ├── simulate-wizard.ts # Interactive wizard
|
||
│ ├── simulate-custom.ts # Custom builder
|
||
│ └── simulate-report.ts # Report viewer
|
||
├── lib/
|
||
│ ├── simulation-runner.ts # Execute simulations
|
||
│ ├── config-validator.ts # Validate configurations
|
||
│ ├── report-generator.ts # Generate markdown/JSON/HTML
|
||
│ └── help-formatter.ts # Multi-level help system
|
||
└── index.ts # CLI entry point
|
||
|
||
# Integrate with existing AgentDB CLI
|
||
packages/agentdb/src/cli/index.ts:
|
||
import { simulateCommand } from './commands/simulate';
|
||
program.addCommand(simulateCommand);
|
||
```
|
||
|
||
---
|
||
|
||
## Part 3: Documentation Reorganization
|
||
|
||
### 3.1 Target Structure
|
||
|
||
```
|
||
packages/agentdb/simulation/
|
||
├── docs/
|
||
│ ├── architecture/
|
||
│ │ ├── CLI-INTEGRATION-PLAN.md (this file)
|
||
│ │ ├── SIMULATION-ARCHITECTURE.md
|
||
│ │ └── OPTIMIZATION-STRATEGY.md
|
||
│ ├── guides/
|
||
│ │ ├── README.md (move from scenarios/latent-space/)
|
||
│ │ ├── QUICK-START.md
|
||
│ │ ├── CUSTOM-SIMULATIONS.md
|
||
│ │ └── WIZARD-GUIDE.md
|
||
│ ├── reports/
|
||
│ │ └── latent-space/
|
||
│ │ ├── MASTER-SYNTHESIS.md (move from current location)
|
||
│ │ ├── README.md (move from current location)
|
||
│ │ └── [8 individual reports].md (move)
|
||
│ └── research/
|
||
│ └── latent-space/
|
||
│ └── [13 original research documents from RuVector]
|
||
├── scenarios/
|
||
│ └── latent-space/
|
||
│ ├── [8 TypeScript simulation files] (keep here)
|
||
│ ├── types.ts (keep here)
|
||
│ └── index.ts (keep here)
|
||
└── tests/
|
||
└── latent-space/
|
||
└── [test files for each simulation]
|
||
```
|
||
|
||
### 3.2 Migration Commands
|
||
|
||
```bash
|
||
# Move reports
|
||
mv packages/agentdb/simulation/reports/latent-space/* \
|
||
packages/agentdb/simulation/docs/reports/latent-space/
|
||
|
||
# Move README
|
||
mv packages/agentdb/simulation/scenarios/latent-space/README.md \
|
||
packages/agentdb/simulation/docs/guides/README.md
|
||
|
||
# Update all internal links in moved files
|
||
# (handled by swarm automation)
|
||
```
|
||
|
||
### 3.3 New Documentation Files to Create
|
||
|
||
1. **docs/guides/QUICK-START.md**
|
||
- 5-minute getting started
|
||
- Run your first simulation
|
||
- Understanding the output
|
||
|
||
2. **docs/guides/CUSTOM-SIMULATIONS.md**
|
||
- Building custom simulations
|
||
- Component reference
|
||
- Configuration examples
|
||
|
||
3. **docs/guides/WIZARD-GUIDE.md**
|
||
- Using the interactive wizard
|
||
- Wizard flow explanation
|
||
- Advanced wizard usage
|
||
|
||
4. **docs/architecture/SIMULATION-ARCHITECTURE.md**
|
||
- TypeScript architecture
|
||
- Component design
|
||
- Extension points
|
||
|
||
5. **docs/architecture/OPTIMIZATION-STRATEGY.md**
|
||
- Findings-based optimizations
|
||
- Performance tuning guide
|
||
- Production deployment
|
||
|
||
---
|
||
|
||
## Part 4: Swarm Coordination Strategy
|
||
|
||
### 4.1 Agent Assignment
|
||
|
||
**5 Concurrent Swarms** for parallel execution:
|
||
|
||
| Swarm | Agent Type | Responsibilities |
|
||
|-------|-----------|-----------------|
|
||
| **Swarm 1: TypeScript Optimizer** | `coder` | Revise all 8 .ts simulation files with optimizations |
|
||
| **Swarm 2: CLI Builder** | `backend-dev` | Build CLI commands, wizard, help system |
|
||
| **Swarm 3: Documentation** | `researcher` | Reorganize docs, create guides |
|
||
| **Swarm 4: Testing** | `tester` | Create comprehensive tests for CLI and simulations |
|
||
| **Swarm 5: Integration** | `system-architect` | Integrate simulations into AgentDB CLI |
|
||
|
||
### 4.2 Task Distribution
|
||
|
||
**Swarm 1: TypeScript Optimizer** (coder)
|
||
- [ ] Revise attention-analysis.ts (8-head optimal, real GNN)
|
||
- [ ] Revise hnsw-exploration.ts (M=32, small-world validation)
|
||
- [ ] Revise traversal-optimization.ts (Beam-5, dynamic-k)
|
||
- [ ] Revise clustering-analysis.ts (Louvain optimal)
|
||
- [ ] Revise self-organizing-hnsw.ts (MPC implementation)
|
||
- [ ] Revise neural-augmentation.ts (Full pipeline)
|
||
- [ ] Revise hypergraph-exploration.ts (3.7x compression)
|
||
- [ ] Update quantum-hybrid.ts (Theoretical analysis)
|
||
- [ ] Add shared optimizations to all files (dynamic-k, self-healing)
|
||
- [ ] Update types.ts with new interfaces
|
||
|
||
**Swarm 2: CLI Builder** (backend-dev)
|
||
- [ ] Create src/cli/commands/simulate.ts (main command)
|
||
- [ ] Create src/cli/commands/simulate-wizard.ts (interactive)
|
||
- [ ] Create src/cli/commands/simulate-custom.ts (builder)
|
||
- [ ] Create src/cli/commands/simulate-report.ts (viewer)
|
||
- [ ] Create src/cli/lib/simulation-runner.ts (execution)
|
||
- [ ] Create src/cli/lib/config-validator.ts (validation)
|
||
- [ ] Create src/cli/lib/report-generator.ts (markdown/JSON/HTML)
|
||
- [ ] Create src/cli/lib/help-formatter.ts (multi-level help)
|
||
- [ ] Integrate with existing AgentDB CLI (src/cli/index.ts)
|
||
- [ ] Add dependencies: inquirer, commander, chalk, ora
|
||
|
||
**Swarm 3: Documentation** (researcher)
|
||
- [ ] Move simulation/reports/ to simulation/docs/reports/
|
||
- [ ] Move scenarios/latent-space/README.md to docs/guides/
|
||
- [ ] Create docs/guides/QUICK-START.md
|
||
- [ ] Create docs/guides/CUSTOM-SIMULATIONS.md
|
||
- [ ] Create docs/guides/WIZARD-GUIDE.md
|
||
- [ ] Create docs/architecture/SIMULATION-ARCHITECTURE.md
|
||
- [ ] Create docs/architecture/OPTIMIZATION-STRATEGY.md
|
||
- [ ] Update all internal links after reorganization
|
||
- [ ] Create comprehensive CLI usage examples
|
||
|
||
**Swarm 4: Testing** (tester)
|
||
- [ ] Create tests/latent-space/attention-analysis.test.ts
|
||
- [ ] Create tests/latent-space/hnsw-exploration.test.ts
|
||
- [ ] Create tests/latent-space/traversal-optimization.test.ts
|
||
- [ ] Create tests/latent-space/clustering-analysis.test.ts
|
||
- [ ] Create tests/latent-space/self-organizing-hnsw.test.ts
|
||
- [ ] Create tests/latent-space/neural-augmentation.test.ts
|
||
- [ ] Create tests/latent-space/hypergraph-exploration.test.ts
|
||
- [ ] Create tests/cli/simulate.test.ts
|
||
- [ ] Create tests/cli/wizard.test.ts
|
||
- [ ] Create tests/cli/custom-builder.test.ts
|
||
|
||
**Swarm 5: Integration** (system-architect)
|
||
- [ ] Design CLI integration architecture
|
||
- [ ] Create simulation registry system
|
||
- [ ] Build configuration management
|
||
- [ ] Implement report persistence (SQLite/JSON)
|
||
- [ ] Add simulation history tracking
|
||
- [ ] Create migration guide for existing users
|
||
- [ ] Design extension API for custom scenarios
|
||
- [ ] Plan production deployment strategy
|
||
|
||
### 4.3 Coordination Protocol
|
||
|
||
Each swarm will use Claude Flow hooks:
|
||
|
||
```bash
|
||
# Before starting
|
||
npx claude-flow@alpha hooks pre-task --description "Swarm [N]: [Task]"
|
||
|
||
# Store intermediate results
|
||
npx claude-flow@alpha hooks post-edit \
|
||
--file "[file]" \
|
||
--memory-key "swarm/latent-space-cli/swarm-[N]/[step]"
|
||
|
||
# After completion
|
||
npx claude-flow@alpha hooks post-task --task-id "swarm-[N]"
|
||
```
|
||
|
||
**Memory Namespace**: `swarm/latent-space-cli/[swarm-id]/`
|
||
|
||
---
|
||
|
||
## Part 5: Implementation Timeline
|
||
|
||
### Phase 1: Foundation (Day 1)
|
||
- ✅ Create implementation plan (this document)
|
||
- ⏳ Reorganize documentation structure
|
||
- ⏳ Update types.ts with new interfaces
|
||
- ⏳ Set up CLI infrastructure
|
||
|
||
### Phase 2: Parallel Development (Days 2-3)
|
||
- ⏳ **Swarm 1**: Optimize all 8 TypeScript files
|
||
- ⏳ **Swarm 2**: Build CLI commands and wizard
|
||
- ⏳ **Swarm 3**: Create comprehensive documentation
|
||
- ⏳ **Swarm 4**: Write tests for all components
|
||
- ⏳ **Swarm 5**: Design integration architecture
|
||
|
||
### Phase 3: Integration & Testing (Day 3-4)
|
||
- ⏳ Integrate CLI into AgentDB
|
||
- ⏳ Run full test suite
|
||
- ⏳ Validate wizard flow
|
||
- ⏳ Test custom simulation builder
|
||
- ⏳ Generate sample reports
|
||
|
||
### Phase 4: Validation & Deployment (Day 4)
|
||
- ⏳ Run optimized simulations (validate improvements)
|
||
- ⏳ Compare results to original reports
|
||
- ⏳ Update MASTER-SYNTHESIS with new findings
|
||
- ⏳ Create deployment guide
|
||
- ⏳ Document API for extensions
|
||
|
||
---
|
||
|
||
## Part 6: Success Criteria
|
||
|
||
### 6.1 Functional Requirements
|
||
|
||
- ✅ All 8 simulations revised with optimal configurations
|
||
- ✅ CLI wizard provides interactive simulation creation
|
||
- ✅ Custom builder allows composing any component combination
|
||
- ✅ Multi-level --help system (3 levels minimum)
|
||
- ✅ Report generation in markdown, JSON, HTML formats
|
||
- ✅ Simulation history tracking and retrieval
|
||
- ✅ Documentation reorganized and comprehensive
|
||
|
||
### 6.2 Performance Requirements
|
||
|
||
- ✅ Simulations validate discovered optimizations:
|
||
- HNSW: 8.2x speedup vs baseline
|
||
- Attention: 12.4% improvement
|
||
- Traversal: 96.8% recall
|
||
- Self-healing: 97.9% degradation prevention
|
||
- Neural: 29.4% improvement
|
||
|
||
- ✅ CLI responsiveness:
|
||
- Wizard startup: <500ms
|
||
- Help display: <100ms
|
||
- Simulation execution: depends on config (document expected times)
|
||
|
||
### 6.3 Quality Requirements
|
||
|
||
- ✅ Test coverage: >90% for CLI commands
|
||
- ✅ Test coverage: >80% for simulation logic
|
||
- ✅ TypeScript: Zero compilation errors
|
||
- ✅ Documentation: Complete for all features
|
||
- ✅ Examples: 10+ working examples in docs
|
||
|
||
### 6.4 User Experience Requirements
|
||
|
||
- ✅ Wizard flow: <5 minutes to configure and run simulation
|
||
- ✅ Help system: 3-level hierarchy with clear navigation
|
||
- ✅ Error messages: Actionable and informative
|
||
- ✅ Reports: Beautiful, readable, shareable
|
||
|
||
---
|
||
|
||
## Part 7: Extension Points
|
||
|
||
### 7.1 Adding New Simulations
|
||
|
||
```typescript
|
||
// 1. Create simulation file
|
||
// packages/agentdb/simulation/scenarios/my-category/my-simulation.ts
|
||
export class MySimulation implements SimulationScenario {
|
||
id = 'my-simulation';
|
||
name = 'My Custom Simulation';
|
||
category = 'my-category';
|
||
|
||
async run(config: any): Promise<SimulationReport> {
|
||
// Implementation
|
||
}
|
||
}
|
||
|
||
// 2. Register in index.ts
|
||
export { MySimulation } from './my-category/my-simulation';
|
||
|
||
// 3. Add to CLI registry
|
||
// src/cli/lib/simulation-registry.ts
|
||
import { MySimulation } from '../../simulation/scenarios';
|
||
registry.register(new MySimulation());
|
||
```
|
||
|
||
### 7.2 Adding New Components
|
||
|
||
```typescript
|
||
// Custom search strategy
|
||
export class MySearchStrategy implements SearchStrategy {
|
||
name = 'my-strategy';
|
||
|
||
async search(query: Float32Array, graph: HNSWGraph): Promise<Neighbor[]> {
|
||
// Implementation
|
||
}
|
||
}
|
||
|
||
// Register for custom builder
|
||
componentRegistry.registerSearchStrategy(new MySearchStrategy());
|
||
```
|
||
|
||
### 7.3 Custom Report Formats
|
||
|
||
```typescript
|
||
// Add PDF export
|
||
export class PDFReportGenerator implements ReportGenerator {
|
||
format = 'pdf';
|
||
|
||
async generate(report: SimulationReport): Promise<Buffer> {
|
||
// Use pdfkit or similar
|
||
}
|
||
}
|
||
|
||
reportGeneratorRegistry.register(new PDFReportGenerator());
|
||
```
|
||
|
||
---
|
||
|
||
## Part 8: Risk Assessment
|
||
|
||
| Risk | Impact | Mitigation |
|
||
|------|--------|-----------|
|
||
| TypeScript compilation errors | HIGH | Incremental compilation, comprehensive types.ts |
|
||
| CLI integration breaks existing | MEDIUM | Feature flags, backward compatibility |
|
||
| Simulation optimizations don't match reports | HIGH | Validation runs, coherence checks |
|
||
| Documentation reorganization breaks links | LOW | Automated link checking, redirects |
|
||
| Test coverage inadequate | MEDIUM | TDD approach, coverage gates |
|
||
| Wizard UX confusing | MEDIUM | User testing, iteration |
|
||
|
||
---
|
||
|
||
## Part 9: Next Steps
|
||
|
||
**IMMEDIATE (Today)**:
|
||
1. Spawn 5 concurrent swarms (Task tool)
|
||
2. Reorganize documentation structure
|
||
3. Update types.ts with new interfaces
|
||
4. Begin TypeScript file optimizations
|
||
|
||
**SHORT-TERM (Tomorrow)**:
|
||
5. Complete all 8 simulation file revisions
|
||
6. Build CLI infrastructure (commands, wizard, help)
|
||
7. Create comprehensive documentation
|
||
8. Write tests for all components
|
||
|
||
**COMPLETION (Day 3-4)**:
|
||
9. Integrate CLI into AgentDB
|
||
10. Run validation simulations
|
||
11. Compare results to original reports
|
||
12. Finalize documentation and examples
|
||
|
||
---
|
||
|
||
## Conclusion
|
||
|
||
This plan provides a comprehensive roadmap for:
|
||
- ✅ Optimizing simulations based on empirical findings
|
||
- ✅ Building production-ready CLI with wizard interface
|
||
- ✅ Reorganizing documentation for clarity
|
||
- ✅ Creating extensible architecture for future enhancements
|
||
|
||
**Estimated Completion**: 3-4 days with concurrent swarm execution
|
||
**Complexity**: High (TypeScript + CLI + Docs)
|
||
**Risk**: Medium (mitigated by comprehensive testing)
|
||
**Impact**: HIGH - Transforms research into production-ready tool
|
||
|
||
---
|
||
|
||
**Document Status**: ✅ IMPLEMENTATION READY
|
||
**Generated**: 2025-11-30
|
||
**Version**: 1.0.0
|