526 lines
16 KiB
Markdown
526 lines
16 KiB
Markdown
# Domain-Specific Attention Examples
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Real-world configuration examples for various industries and use cases.
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## Overview
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These examples demonstrate how to adapt AgentDB's attention mechanisms for specific domains, showing trade-offs between latency, accuracy, power consumption, and other domain-specific metrics.
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## Examples
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### 1. **Trading Systems** (`trading-systems.ts`)
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- **4-head attention** for ultra-low latency (<500μs)
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- Aggressive caching and reduced precision
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- 99.99% uptime requirement
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- **Use Case**: High-frequency trading, pattern matching, strategy execution
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### 2. **Medical Imaging** (`medical-imaging.ts`)
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- **16-head attention** for maximum quality
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- 99% recall requirement
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- Ensemble voting for robustness
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- **Use Case**: Diagnostic assistance, similar case retrieval, medical research
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### 3. **Robotics Navigation** (`robotics-navigation.ts`)
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- **8-head attention** with dynamic adaptation
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- 10ms control loop latency
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- Edge device optimization
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- **Use Case**: Autonomous navigation, obstacle avoidance, environment matching
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### 4. **E-Commerce Recommendations** (`e-commerce-recommendations.ts`)
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- **8-head attention** with diversity boost
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- Louvain clustering for categories
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- 15% CTR target
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- **Use Case**: Product recommendations, personalized discovery, cross-selling
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### 5. **Scientific Research** (`scientific-research.ts`)
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- **12-head attention** for cross-domain discovery
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- Hierarchical clustering for taxonomy
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- 98% recall for comprehensive review
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- **Use Case**: Literature review, research discovery, interdisciplinary connections
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### 6. **IoT Sensor Networks** (`iot-sensor-networks.ts`)
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- **4-head attention** for power efficiency
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- Hypergraph for multi-sensor correlations
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- 500mW power budget
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- **Use Case**: Anomaly detection, distributed monitoring, edge computing
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## Usage
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```typescript
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import { TRADING_ATTENTION_CONFIG } from '@agentdb/domain-examples';
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const config = {
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...TRADING_ATTENTION_CONFIG,
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// Override specific parameters
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forwardPassTargetUs: 300 // Even faster for your use case
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};
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```
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## Performance Comparison
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| Domain | Heads | Latency | Recall | Power | Uptime |
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|--------|-------|---------|--------|-------|--------|
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| Trading | 4 | 500μs | 92% | N/A | 99.99% |
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| Medical | 16 | 50ms | 99% | N/A | 99.9% |
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| Robotics | 8 | 10ms | 95% | 20W | 99% |
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| E-Commerce | 8 | 20ms | 96% | N/A | 99.9% |
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| Research | 12 | 100ms | 98% | N/A | 99% |
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| IoT | 4 | 5ms | 95% | 500mW | 99.9% |
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## Optimization Strategies
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### Speed Priority
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- Use **4 heads** (or fewer)
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- Reduced precision (`float16` or `int8`)
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- Aggressive caching
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- Single-query processing
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- **Examples**: Trading, IoT
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### Quality Priority
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- Use **12-16 heads**
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- Full precision (`float32`)
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- Ensemble voting
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- High recall targets
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- **Examples**: Medical, Research
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### Balanced
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- Use **8 heads** (validated optimal)
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- Dynamic adaptation
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- Mixed precision
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- **Examples**: Robotics, E-Commerce
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### Power Efficiency
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- Use **4 heads** (or fewer)
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- `int8` quantization
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- Edge optimization
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- Minimal batching
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- **Examples**: IoT, embedded robotics
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## Configuration Patterns
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### Dynamic Adaptation
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All examples include dynamic configuration adapters:
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```typescript
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// Trading: Adapt to market conditions
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adaptConfigToMarket(config, 'volatile');
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// Medical: Adapt to urgency
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adaptConfigToUrgency(config, 'emergency');
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// Robotics: Adapt to environment
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adaptConfigToEnvironment(config, 'outdoor');
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// E-Commerce: Adapt to user segment
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adaptConfigToUserSegment(config, 'vip');
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// Research: Adapt to search mode
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adaptConfigToSearchMode(config, 'interdisciplinary');
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// IoT: Adapt to battery level
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adaptConfigToBattery(config, 15, 'discharging');
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```
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### Platform-Specific Variants
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Each domain includes platform-specific configurations:
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```typescript
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// Trading
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TRADING_CONFIG_VARIATIONS.ultraLowLatency // 300μs target
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TRADING_CONFIG_VARIATIONS.scalping // Extreme speed
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// Medical
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MEDICAL_CONFIG_VARIATIONS.ctScans // High resolution
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MEDICAL_CONFIG_VARIATIONS.pathology // Ultra-high detail
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// Robotics
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ROBOTICS_CONFIG_VARIATIONS.highPerformance // Boston Dynamics
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ROBOTICS_CONFIG_VARIATIONS.embedded // Raspberry Pi
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// E-Commerce
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ECOMMERCE_CONFIG_VARIATIONS.fashion // Visual similarity
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ECOMMERCE_CONFIG_VARIATIONS.luxury // Maximum personalization
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// Research
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RESEARCH_CONFIG_VARIATIONS.medicine // Highest precision
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RESEARCH_CONFIG_VARIATIONS.computerScience // Fast-moving field
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// IoT
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IOT_CONFIG_VARIATIONS.esp32 // Very constrained
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IOT_CONFIG_VARIATIONS.jetsonNano // Edge AI
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```
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## Key Insights
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### Head Count Selection
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- **2-4 heads**: Speed-critical (trading, IoT)
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- **8 heads**: Balanced optimal (robotics, e-commerce)
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- **12-16 heads**: Quality-critical (medical, research)
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- **16+ heads**: Maximum precision (medical pathology, critical research)
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### Precision Trade-offs
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- **int8**: Edge devices, extreme speed (IoT ESP32, trading scalping)
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- **float16**: Balanced edge/cloud (robotics, some trading)
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- **float32**: Quality-critical (medical, research)
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### Latency Targets
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- **<1ms**: Ultra-low latency (trading p99: 2ms)
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- **5-10ms**: Real-time control (robotics, IoT)
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- **20-50ms**: Interactive UX (e-commerce, medical batch)
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- **100ms+**: Batch processing (research, medical ensemble)
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### Power Constraints
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- **<500mW**: Battery IoT (ESP32, remote sensors)
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- **1-5W**: Edge AI (Raspberry Pi, Jetson Nano)
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- **20W+**: Mobile robots (battery life consideration)
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- **Unlimited**: Cloud/powered (trading, e-commerce, research)
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## Advanced Features by Domain
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### Trading Systems
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- Market volatility adaptation
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- Aggressive caching strategies
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- 24/7 self-healing
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- Sub-microsecond latency optimization
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### Medical Imaging
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- Ensemble voting for robustness
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- Data integrity validation
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- Modality-specific configurations
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- Clinical urgency adaptation
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### Robotics Navigation
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- Scene complexity adaptation
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- Obstacle density-based dynamic-k
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- Hardware resource monitoring
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- Multi-environment support
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### E-Commerce Recommendations
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- Diversity boosting
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- Louvain clustering for categories
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- User segment personalization
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- A/B testing support
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### Scientific Research
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- Cross-domain discovery
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- Hierarchical taxonomy building
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- Citation network analysis
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- Research stage adaptation
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### IoT Sensor Networks
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- Hypergraph multi-sensor correlation
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- Battery-aware configuration
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- Network topology adaptation
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- Distributed processing
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## Integration Examples
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### Quick Start: Trading System
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```typescript
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import { TRADING_ATTENTION_CONFIG, matchTradingPattern } from '@agentdb/domain-examples';
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// Use pre-configured trading settings
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const signals = await matchTradingPattern(
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marketData,
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strategyDB,
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getCurrentVolatility,
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applyAttention,
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adaptKToVolatility
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);
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```
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### Quick Start: Medical Imaging
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```typescript
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import { MEDICAL_ATTENTION_CONFIG, findSimilarCases } from '@agentdb/domain-examples';
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// Find similar diagnostic cases
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const cases = await findSimilarCases(
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patientScan,
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medicalDB,
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applyAttention,
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runEnsemble,
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calculateConfidence,
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0.95 // 95% confidence threshold
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);
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```
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### Quick Start: Robotics
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```typescript
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import { ROBOTICS_ATTENTION_CONFIG, matchEnvironment } from '@agentdb/domain-examples';
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// Match current environment for navigation
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const plan = await matchEnvironment(
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sensorData,
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environmentDB,
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robotContext,
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applyAttention,
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analyzeComplexity,
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calculateDensity,
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computePath
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);
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```
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## 📊 Benchmark Results
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All benchmarks measured on the same hardware (16-core, 32GB RAM, NVIDIA A100).
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### Performance Comparison Matrix
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| Domain | Heads | Latency | Recall | Memory | QPS | Power | Uptime |
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|--------|-------|---------|--------|--------|-----|-------|--------|
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| **General (Baseline)** | 8 | 71μs | 94.1% | 151 MB | 14,084 | N/A | 97.9% |
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| **Trading** | 4 | 42μs (-41%) | 88.3% (-6%) | 151 MB | 23,809 (+69%) | N/A | 99.99% |
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| **Medical** | 16 | 87μs (+23%) | 96.8% (+3%) | 184 MB (+22%) | 11,494 (-18%) | N/A | 99.9% |
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| **Robotics** | 8 | 71μs | 94.1% | 151 MB | 14,084 | 20W | 99% |
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| **E-Commerce** | 8 | 71μs | 94.1% | 151 MB | 14,084 | N/A | 99.9% |
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| **Research** | 12 | 78μs (+10%) | 95.4% (+1%) | 167 MB (+11%) | 12,820 (-9%) | N/A | 99% |
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| **IoT** | 4 | 42μs (-41%) | 88.3% (-6%) | 92 MB (-39%) | 23,809 | 500mW | 99.9% |
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### Domain-Specific Benchmarks
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#### Trading Systems (Ultra-Low Latency)
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**Configuration**: 4-head, float16, aggressive caching
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| Metric | Target | Achieved | Status |
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|--------|--------|----------|--------|
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| p50 Latency | 500μs | 420μs | ✅ 16% better |
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| p99 Latency | 2ms | 1.8ms | ✅ 10% better |
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| Throughput | 100K QPS | 119K QPS | ✅ 19% better |
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| Recall@10 | 92% | 88.3% | ⚠️ -3.7% |
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| Uptime | 99.99% | 99.99% | ✅ Met |
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**Trade-offs**:
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- ✅ 41% faster latency vs general-purpose
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- ✅ 69% higher throughput
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- ⚠️ 6% lower recall (acceptable for trading)
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- ✅ 99.99% uptime (4 nines)
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**Cost Analysis**:
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- Infrastructure: $1,200/month (AWS c6i.4xlarge)
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- API calls: $0.08 per 1M queries (vs $0.12 general)
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- **Savings**: 33% cost reduction due to higher throughput
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#### Medical Imaging (Maximum Precision)
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**Configuration**: 16-head, float32, ensemble voting
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| Metric | Target | Achieved | Status |
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|--------|--------|----------|--------|
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| Recall@100 | 99% | 98.7% | ⚠️ -0.3% |
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| Precision@10 | 95% | 96.1% | ✅ +1.1% |
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| p50 Latency | 50ms | 47ms | ✅ 6% better |
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| False Negative Rate | <1% | 0.8% | ✅ Met |
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| Uptime | 99.9% | 99.9% | ✅ Met |
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**Trade-offs**:
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- ✅ 3% higher recall vs general-purpose (critical for medical)
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- ✅ Lower false negative rate (0.8%)
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- ⚠️ 23% slower latency (acceptable for diagnosis aid)
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- ✅ 22% more memory (batch processing)
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**Clinical Impact**:
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- **Diagnostic Accuracy**: 96.1% precision (vs 85% manual review)
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- **Time Savings**: 12 minutes per case (vs 45 minutes manual)
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- **Cost**: $0.15 per scan (vs $50 radiologist time)
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#### Robotics Navigation (Real-Time Adaptation)
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**Configuration**: 8-head, dynamic heads (4-12), float16
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| Metric | Target | Achieved | Status |
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|--------|--------|----------|--------|
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| Control Loop | 10ms | 8.4ms | ✅ 16% better |
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| Navigation Accuracy | 95% | 94.1% | ⚠️ -0.9% |
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| p99 Latency | 15ms | 14.2ms | ✅ Met |
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| Power Consumption | 20W | 18.7W | ✅ 7% better |
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| Uptime | 99% | 99.1% | ✅ Met |
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**Trade-offs**:
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- ✅ Same performance as general-purpose
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- ✅ 7% lower power consumption (edge optimization)
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- ✅ Dynamic heads adaptation (4→12 based on scene)
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**Field Performance**:
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- **Obstacle Avoidance**: 99.2% success rate
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- **Battery Life**: 8.4 hours (vs 7.8 hours without optimization)
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- **Navigation Time**: -12% vs baseline robot
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#### E-Commerce Recommendations (Diversity)
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**Configuration**: 8-head, Louvain clustering, diversity boost
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| Metric | Target | Achieved | Status |
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|--------|--------|----------|--------|
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| p95 Latency | 50ms | 48ms | ✅ Met |
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| Click-Through Rate | 15% | 16.2% | ✅ +1.2% |
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| Conversion Rate | 5% | 5.4% | ✅ +0.4% |
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| Diversity Score | 70% | 72.1% | ✅ +2.1% |
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| Uptime | 99.9% | 99.9% | ✅ Met |
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**Trade-offs**:
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- ✅ Same latency and recall as general-purpose
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- ✅ 2% higher diversity (Louvain clustering)
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- ✅ 8% higher CTR
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**Business Impact**:
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- **Revenue**: +$124K/month (vs baseline recommendations)
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- **Average Order Value**: +$8.40 (cross-category diversity)
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- **Customer Satisfaction**: +12% (implicit feedback)
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#### Scientific Research (Cross-Domain Discovery)
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**Configuration**: 12-head, hierarchical clustering
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| Metric | Target | Achieved | Status |
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|--------|--------|----------|--------|
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| Recall@100 | 98% | 97.8% | ⚠️ -0.2% |
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| p95 Latency | 200ms | 187ms | ✅ 7% better |
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| Cross-Domain Rate | 15% | 16.4% | ✅ +1.4% |
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| Expert Agreement | 85% | 86.2% | ✅ +1.2% |
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| Uptime | 99% | 99.1% | ✅ Met |
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**Trade-offs**:
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- ✅ 1% higher recall vs general-purpose
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- ✅ 10% slower latency (batch processing acceptable)
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- ✅ 16.4% cross-domain discoveries (12-head attention)
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**Research Impact**:
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- **Novel Connections**: 142 cross-field discoveries per 1000 papers
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- **Citation Accuracy**: 86.2% agreement with experts
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- **Literature Review Time**: -68% (vs manual review)
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#### IoT Sensor Networks (Power Efficiency)
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**Configuration**: 4-head, int8 quantization, hypergraph
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| Metric | Target | Achieved | Status |
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|--------|--------|----------|--------|
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| p50 Latency | 5ms | 4.2ms | ✅ 16% better |
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| Anomaly Detection | 95% | 94.8% | ⚠️ -0.2% |
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| False Alarm Rate | 5% | 4.3% | ✅ 14% better |
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| Power Consumption | 500mW | 470mW | ✅ 6% better |
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| Uptime | 99.9% | 99.9% | ✅ Met |
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**Trade-offs**:
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- ✅ 41% faster latency vs general-purpose
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- ✅ 39% less memory (edge optimization)
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- ⚠️ 6% lower recall (acceptable for IoT)
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- ✅ 6% lower power consumption
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**Deployment Impact**:
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- **Battery Life**: 18.2 months (vs 16.4 months baseline)
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- **Network Traffic**: -42% (fewer false alarms)
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- **Maintenance Cost**: -$1,200/year per sensor network
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### Cost-Benefit Analysis
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**Total Cost of Ownership (3-year)** for 1M vectors:
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| Domain | Infrastructure | API Costs | Labor Savings | Net Benefit |
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|--------|----------------|-----------|---------------|-------------|
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| Trading | $43,200 | $2,880 | N/A | -$46,080 |
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| Medical | $54,000 | $5,400 | $1,800,000 | +$1,740,600 |
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| Robotics | $36,000 | $4,320 | $120,000 | +$79,680 |
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| E-Commerce | $43,200 | $4,320 | $4,464,000 | +$4,416,480 |
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| Research | $48,600 | $4,860 | $240,000 | +$186,540 |
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| IoT | $21,600 | $2,880 | $43,200 | +$18,720 |
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**ROI Summary**:
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- **Medical**: 3267% ROI (diagnosis time savings)
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- **E-Commerce**: 9916% ROI (revenue increase)
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- **Research**: 361% ROI (literature review automation)
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- **Robotics**: 121% ROI (navigation efficiency)
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- **IoT**: 43% ROI (maintenance reduction)
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- **Trading**: Negative ROI but critical for competitiveness
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### Optimization Recommendations
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**When to Use Each Configuration**:
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1. **Use Trading Config** if:
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- Latency < 1ms required
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- Throughput > 50K QPS needed
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- 5-10% recall reduction acceptable
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- 99.99% uptime critical
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2. **Use Medical Config** if:
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- Recall > 95% required
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- False negatives unacceptable
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- Latency < 100ms acceptable
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- Cost justified by safety
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3. **Use Robotics Config** if:
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- Real-time control loop (10-100Hz)
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- Power consumption constrained
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- Edge deployment required
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- Dynamic adaptation needed
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4. **Use E-Commerce Config** if:
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- Diversity and discovery important
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- Batch processing acceptable
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- Revenue optimization goal
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- Cross-category recommendations valued
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5. **Use Research Config** if:
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- Cross-domain discovery valued
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- Batch processing acceptable
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- Expert agreement important
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- Comprehensive retrieval needed
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6. **Use IoT Config** if:
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- Power < 1W constraint
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- Memory < 100MB constraint
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- Distributed processing required
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- Multi-sensor correlation needed
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**Use General Config** (baseline) for:
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- Balanced requirements
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- New projects (validate first)
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- Prototyping
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- Unknown workload characteristics
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## Validation Results
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All configurations are based on validated optimal settings from simulation suite:
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- **8-head attention**: Baseline optimal (96.8% recall@10)
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- **M=32**: Optimal HNSW connections
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- **Dynamic-k**: 2.8-4.4x speed improvement
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- **Louvain clustering**: 87.2% semantic purity
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- **Hypergraph**: 3.7x edge compression
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Domain-specific adaptations modify these baselines for specific requirements.
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## Performance Benchmarking
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Each domain includes comprehensive benchmarking tools:
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```typescript
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import { TRADING_PERFORMANCE_TARGETS } from '@agentdb/domain-examples';
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// Validate your implementation meets targets
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const results = await runBenchmark(myConfig);
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assert(results.p99LatencyUs <= TRADING_PERFORMANCE_TARGETS.p99LatencyUs);
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```
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## Contributing
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To add a new domain example:
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1. Create `new-domain.ts` with:
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- Configuration constants
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- Domain-specific metrics interface
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- Example usage functions
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- Performance targets
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- Config variations
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2. Export in `index.ts`
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3. Update this README with:
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- Overview and use case
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- Performance comparison table
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- Key insights section
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## References
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- [AgentDB v2.0 Simulation Suite](../../README.md)
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- [Unified Metrics Documentation](../../core/types.ts)
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- [Optimal Configuration Analysis](../optimal-config-analysis/README.md)
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