Enhanced Financial Advisor: Comprehensive Agent Memory Integration #79
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## Summary
Completely redesigned the financial advisor example to showcase the full potential of ProllyTree's agent memory system. This implementation demonstrates sophisticated AI-powered financial analysis with deep personalization, compliance automation, and multi-step workflow orchestration.
## Key Features
### 🧠 Complete Agent Memory Integration
- **Short-term Memory**: Working memory for active analysis contexts
- **Semantic Memory**: Client profiles, market entities, and compliance rules
- **Episodic Memory**: Recommendation history, client interactions, and outcomes
- **Procedural Memory**: Analysis workflows, risk procedures, and compliance automation
### 🔄 Multi-Step Workflow Processing
- Orchestrated analysis pipeline with memory-driven intelligence
- Market research → Risk analysis → Compliance validation → Personalized recommendations
- Each step leverages appropriate memory types for enhanced decision-making
- Comprehensive execution metadata and performance tracking
### 🎯 Deep Personalization Engine
- Client behavior modeling from interaction history
- Adaptive communication styles (Professional, Conversational, Educational, Direct)
- Risk tolerance evolution tracking with trigger event analysis
- Decision pattern recognition (follows advice, research-focused, impulsive, etc.)
- Personalized follow-up actions based on client preferences
### 🤖 AI-Powered Analysis
- Rig framework integration for enhanced market insights
- Context-aware prompts using client history and market conditions
- Fallback reasoning for scenarios without AI access
- Confidence adjustments based on AI analysis quality
### 🛡️ Enhanced Compliance System
- Automated compliance rule checking from procedural memory
- Risk-based violation detection with severity levels
- Client suitability assessments using historical data
- Required disclosure management and audit trails
### 📊 Advanced Market Analysis
- Fundamental analysis with valuation metrics
- Technical analysis with support/resistance levels
- Sector analysis and rotation outlook
- Sentiment analysis from multiple sources
- Historical pattern recognition from episodic memory
## Technical Architecture
### Memory-Driven Design
```
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Short-term │ │ Semantic │ │ Episodic │
│ Working Memory │ │ Knowledge Base │ │ Experience │
│ │ │ │ │ │
│ • Analysis │◄──►│ • Client Profiles│◄──►│ • Interactions │
│ Context │ │ • Market Data │ │ • Outcomes │
│ • Step Results │ │ • Compliance │ │ • Decisions │
└─────────────────┘ └──────────────────┘ └─────────────────┘
│
▼
┌──────────────────┐
│ Procedural │
│ Workflows │
│ │
│ • Analysis Steps │
│ • Risk Procedures│
│ • Compliance │
└──────────────────┘
```
### Core Components
- **EnhancedFinancialAdvisor**: Main coordinator with memory system integration
- **WorkflowProcessor**: Multi-step analysis orchestration
- **AnalysisModuleRegistry**: Specialized analysis components
- **PersonalizationEngine**: Client behavior modeling and adaptation
- **Enhanced Memory Types**: Financial-specific semantic structures
## New Files Added
- `src/advisor/enhanced_advisor.rs` - Main enhanced advisor implementation
- `src/advisor/workflow.rs` - Multi-step workflow processing
- `src/advisor/analysis_modules.rs` - Specialized analysis modules
- `src/advisor/personalization.rs` - Client behavior modeling
- `src/memory/enhanced_types.rs` - Financial-specific memory structures
- `examples/enhanced_demo.rs` - Comprehensive demonstration
- `ENHANCED_README.md` - Detailed documentation
## CLI Integration
```bash
# New enhanced command
cargo run -- enhanced --verbose
# Interactive session with memory-driven advice
advisor> client john_doe
advisor> recommend AAPL
advisor> research MSFT
advisor> stats
```
## Memory System Usage Examples
### Client Profile (Semantic Memory)
```rust
ClientEntity {
risk_tolerance: RiskTolerance::Moderate,
investment_goals: ["growth", "income"],
time_horizon: "Long-term",
behavioral_patterns: tracked_automatically
}
```
### Recommendation Episode (Episodic Memory)
```rust
RecommendationEpisode {
action: RecommendationType::Buy,
confidence: 0.85,
market_conditions: snapshot,
outcome: Some(actual_performance),
workflow_steps: detailed_execution_log
}
```
### Analysis Workflow (Procedural Memory)
```rust
AnalysisWorkflow {
steps: [GatherMarketData, AnalyzeRisk, CheckCompliance],
success_rate: 0.92,
applicable_conditions: ["bull_market", "moderate_volatility"]
}
```
## Performance & Quality
- ✅ Compiles cleanly with `cargo build --all`
- ✅ Zero unused variable warnings
- ✅ Comprehensive error handling
- ✅ Memory-efficient Arc<T> usage for shared state
- ✅ Async/await patterns throughout
- ✅ Extensive documentation and examples
## Learning & Adaptation
- Recommendation outcome tracking for continuous improvement
- Client behavior pattern evolution
- Procedural knowledge updates based on success rates
- Confidence adjustments from historical performance
This implementation transforms the basic financial advisor into a sophisticated AI agent that learns, adapts, and provides highly personalized financial advice using the full power of ProllyTree's agent memory system.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
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Summary
Completely redesigned the financial advisor example to showcase the full potential of ProllyTree's agent memory system. This implementation demonstrates sophisticated AI-powered financial analysis with deep personalization, compliance automation, and multi-step workflow orchestration.
Key Features
🧠 Complete Agent Memory Integration
🔄 Multi-Step Workflow Processing
🎯 Deep Personalization Engine
🤖 AI-Powered Analysis
🛡️ Enhanced Compliance System
📊 Advanced Market Analysis
Technical Architecture
Memory-Driven Design
The diagram shows the interconnected memory system where:
Core Components
CLI Integration
This implementation transforms the basic financial advisor into a sophisticated AI agent that learns, adapts, and provides highly personalized financial advice using the full power of ProllyTree's agent memory system.