bzzz/slurp/intelligence

SLURP Contextual Intelligence Engine

The Contextual Intelligence Engine implements advanced analysis and processing capabilities for extracting meaningful insights from code, architecture, and project structure within the BZZZ ecosystem.

Purpose

This module provides the "Intelligence" layer of SLURP by:

• File Purpose Analysis: Intelligent analysis of file roles and purposes
• Architectural Decision Extraction: Identifying and documenting architectural decisions
• Cross-Component Relationship Mapping: Understanding how components interact
• Role-Specific Insight Generation: Tailoring insights to specific AI agent roles
• Project-Goal Alignment: Ensuring context aligns with project objectives

Architecture

The Intelligence Engine operates as a multi-layered analysis system:

┌─────────────────────────────────────┐
│       Role-Specific Insights        │
├─────────────────────────────────────┤
│     Project Goal Alignment         │  
├─────────────────────────────────────┤
│   Cross-Component Relationships    │
├─────────────────────────────────────┤
│   Architectural Decision Analysis   │
├─────────────────────────────────────┤
│      File Purpose Analysis         │
├─────────────────────────────────────┤
│       Context Data Sources          │
└─────────────────────────────────────┘

Key Components

File Purpose Analysis Engine

Analyzes code files to determine their purpose and role within the system:

Code Pattern Recognition

• Entry Points: Identifies main functions, application bootstraps
• API Endpoints: Recognizes REST endpoints, GraphQL resolvers
• Data Models: Detects entity definitions, database schemas
• Utilities: Identifies helper functions, common utilities
• Configuration: Finds configuration files, environment settings

Language-Specific Analysis

• Rust: Module structure, trait implementations, macro usage
• Go: Package organization, interface definitions, concurrency patterns
• JavaScript/TypeScript: Component structure, async patterns, module exports
• Python: Class hierarchies, decorators, async/await patterns

Architectural Pattern Detection

• MVC Patterns: Model-View-Controller structure identification
• Microservices: Service boundary detection and communication patterns
• P2P Systems: Peer discovery, network protocols, consensus mechanisms
• Event-Driven: Event producers, consumers, message flows

Architectural Decision Extraction

Identifies and documents architectural decisions embedded in code:

Decision Indicators

• Design Patterns: Observer, Factory, Strategy pattern implementations
• Technology Choices: Framework selections, library dependencies
• Performance Optimizations: Caching strategies, algorithm choices
• Security Measures: Authentication, encryption, access control
• Scalability Decisions: Load balancing, data partitioning, caching

Decision Documentation

@dataclass
class ArchitecturalDecision:
    decision_id: str
    title: str
    status: DecisionStatus  # PROPOSED, ACCEPTED, DEPRECATED, SUPERSEDED
    context: str           # What is the issue?
    decision: str          # What is the change we're proposing/doing?
    rationale: str         # Why are we doing this?
    consequences: List[str] # What becomes easier/harder?
    alternatives: List[str] # What other options were considered?
    related_decisions: List[str] # Related decision IDs
    extracted_from: List[str]    # UCXL addresses where evidence found

Cross-Component Relationship Mapping

Maps relationships and dependencies between system components:

Relationship Types

• Import Dependencies: Direct code imports and includes
• API Dependencies: Service calls, endpoint usage
• Data Dependencies: Shared data structures, database schemas
• Configuration Dependencies: Shared configuration, environment variables
• Deployment Dependencies: Infrastructure, container dependencies

Relationship Analysis

@dataclass
class ComponentRelationship:
    from_component: str    # Source UCXL address
    to_component: str      # Target UCXL address
    relationship_type: RelationshipType
    strength: float        # How strong is this relationship?
    confidence: float      # How confident are we in this relationship?
    evidence: List[str]    # What code/config indicates this relationship?
    impact_level: str      # How much does from_component depend on to_component?

Role-Specific Insight Generation

Tailors contextual insights to specific AI agent roles:

Role-Based Analysis Focus

Senior Architect

• System-wide architectural patterns
• Technology stack coherence
• Scalability and performance implications
• Technical debt identification
• Cross-system integration points

Backend Developer

• API design patterns
• Data flow architecture
• Service boundaries
• Performance bottlenecks
• Security implementation

Frontend Developer

• Component architecture
• State management patterns
• User experience flow
• Performance optimization
• Accessibility considerations

DevOps Engineer

• Deployment configurations
• Infrastructure dependencies
• Monitoring and logging
• Security hardening
• Scalability requirements

Insight Personalization

def generate_role_specific_insights(context: ContextNode, role: AgentRole) -> List[str]:
    insights = []
    
    if role == AgentRole.SENIOR_ARCHITECT:
        insights.extend(analyze_architectural_patterns(context))
        insights.extend(identify_system_boundaries(context))
        insights.extend(assess_scalability_implications(context))
    
    elif role == AgentRole.BACKEND_DEVELOPER:
        insights.extend(analyze_api_design(context))
        insights.extend(identify_data_flows(context))
        insights.extend(assess_performance_characteristics(context))
    
    # ... role-specific analysis continues
    
    return insights

Project Goal Alignment

Ensures contextual intelligence aligns with current project goals and objectives:

Goal-Context Mapping

• Feature Development: Context relevance to current feature development
• Performance Optimization: Performance-related context for optimization goals
• Security Hardening: Security-relevant context for hardening initiatives
• Technical Debt: Technical debt context for refactoring goals
• Documentation: Documentation needs for knowledge sharing goals

Alignment Scoring

@dataclass
class GoalAlignment:
    goal_id: str
    goal_description: str
    context_relevance: float    # How relevant is this context to the goal?
    contribution_score: float   # How much does this component contribute?
    priority_weight: float      # How important is this goal currently?
    alignment_confidence: float # How confident are we in this alignment?

Analysis Algorithms

Contextual Similarity Analysis

Identifies similar components based on contextual features:

def calculate_context_similarity(context1: ContextNode, context2: ContextNode) -> float:
    # Technology stack similarity
    tech_similarity = jaccard_similarity(context1.technologies, context2.technologies)
    
    # Purpose similarity (semantic analysis)
    purpose_similarity = semantic_similarity(context1.purpose, context2.purpose)
    
    # Tag overlap
    tag_similarity = jaccard_similarity(context1.tags, context2.tags)
    
    # Architectural pattern similarity
    pattern_similarity = analyze_pattern_similarity(context1, context2)
    
    return weighted_average([
        (tech_similarity, 0.3),
        (purpose_similarity, 0.4),
        (tag_similarity, 0.2),
        (pattern_similarity, 0.1)
    ])

Impact Analysis Engine

Predicts the impact of changes based on contextual relationships:

def analyze_change_impact(target_address: str, change_type: ChangeType) -> ImpactAnalysis:
    # Find all components related to target
    relationships = find_all_relationships(target_address)
    
    # Calculate impact scores based on relationship strength and change type
    impacts = []
    for rel in relationships:
        impact_score = calculate_impact_score(rel, change_type)
        impacts.append(ComponentImpact(
            component=rel.to_component,
            impact_type=determine_impact_type(rel, change_type),
            severity=impact_score,
            confidence=rel.confidence
        ))
    
    return ImpactAnalysis(
        target_component=target_address,
        change_type=change_type,
        direct_impacts=impacts,
        indirect_impacts=calculate_indirect_impacts(impacts),
        risk_assessment=assess_change_risk(impacts)
    )

Integration with BZZZ Leader System

Leader-Coordinated Intelligence Generation

• Centralized Analysis: Only Leader generates contextual intelligence
• Quality Control: Consistent analysis quality across all contexts
• Role-Based Distribution: Intelligence tailored and encrypted per role
• Priority Scheduling: Leader schedules analysis based on project priorities

Intelligence Update Propagation

When Leader generates new intelligence:

1. Analysis Triggering: Code changes, decision updates trigger re-analysis
2. Intelligence Generation: Deep analysis of affected components
3. Role-Based Packaging: Package insights for each agent role
4. Encrypted Distribution: Distribute encrypted intelligence via DHT
5. Update Notifications: Notify agents of available intelligence updates

Performance Optimization

Analysis Caching

• Result Caching: Cache analysis results for unchanged components
• Incremental Analysis: Only re-analyze changed components
• Batch Processing: Process multiple components efficiently
• Lazy Loading: Load detailed analysis only when requested

Parallel Processing

• Component-Level Parallelism: Analyze multiple components concurrently
• Pipeline Parallelism: Pipeline different analysis stages
• Resource Management: Respect CPU/memory limits during analysis
• Priority Queuing: Prioritize analysis based on component importance

Quality Assurance

Analysis Confidence Scoring

@dataclass
class AnalysisConfidence:
    overall_confidence: float
    code_analysis_confidence: float    # How well we understand the code
    pattern_recognition_confidence: float # How confident in pattern detection
    relationship_confidence: float    # How confident in relationships
    goal_alignment_confidence: float  # How confident in goal alignment
    factors_considered: List[str]     # What factors influenced confidence

Validation Mechanisms

• Cross-Validation: Multiple analysis approaches for critical insights
• Human Validation: Mechanisms for team members to validate/correct insights
• Consistency Checking: Ensure insights are consistent across related components
• Temporal Validation: Validate insights remain accurate over time

Future Enhancements

Advanced AI Integration

• Large Language Models: Integration with LLMs for enhanced code understanding
• Machine Learning: ML models for pattern recognition and similarity analysis
• Natural Language Processing: Better extraction of intent from comments/docs
• Computer Vision: Analysis of architectural diagrams and documentation

Enhanced Analysis Capabilities

• Behavioral Analysis: Understanding runtime behavior from code patterns
• Performance Prediction: Predict performance characteristics from code structure
• Security Analysis: Automated security vulnerability detection
• Maintainability Scoring: Assess code maintainability and technical debt

Real-Time Intelligence

• Live Analysis: Real-time analysis of code changes
• Predictive Insights: Predict likely next steps in development
• Proactive Recommendations: Suggest improvements before problems arise
• Continuous Learning: System learns and improves analysis over time