api_reference.md

Code Fractalization Protocol API Reference

Table of Contents

1. Core Components
2. Change Management
3. Resource Management
4. Verification System
5. AI Integration
6. [Novel Solution Management]{#novel-management}
7. Cross-Cutting Concerns
8. Error Handling
9. Common Types

Core Components

FractalManager

Primary interface for managing fractal structures.

class FractalManager:
    def create_fractal(self, specification: FractalSpecification) -> Fractal:
        """
        Create a new fractal based on the provided specification.
        
        Args:
            specification (FractalSpecification): Detailed specification for the fractal
                including implementation, knowledge, and contract layers
        
        Returns:
            Fractal: The newly created fractal instance
            
        Raises:
            InvalidSpecificationError: If the specification is invalid
            ResourceConflictError: If required resources are unavailable
        """
        pass

    def modify_fractal(self, fractal_id: str, changes: FractalChanges) -> Fractal:
        """
        Modify an existing fractal with the specified changes.
        
        Args:
            fractal_id (str): Unique identifier of the fractal
            changes (FractalChanges): Set of changes to apply
            
        Returns:
            Fractal: The updated fractal instance
            
        Raises:
            FractalNotFoundError: If the fractal doesn't exist
            InvalidChangeError: If the changes are invalid
        """
        pass

    def get_fractal(self, fractal_id: str) -> Fractal:
        """
        Retrieve a fractal by its ID.
        
        Args:
            fractal_id (str): Unique identifier of the fractal
            
        Returns:
            Fractal: The requested fractal instance
            
        Raises:
            FractalNotFoundError: If the fractal doesn't exist
        """
        pass

ContractManager

Manages contracts between fractals.

class ContractManager:
    def create_contract(self, specification: ContractSpecification) -> Contract:
        """
        Create a new contract between fractals.
        
        Args:
            specification (ContractSpecification): Detailed contract specification
                including interfaces, behaviors, and resources
        
        Returns:
            Contract: The newly created contract instance
            
        Raises:
            InvalidContractError: If the contract specification is invalid
            ConflictingContractError: If conflicting contracts exist
        """
        pass

    def verify_contract(self, contract: Contract) -> VerificationResult:
        """
        Verify a contract's consistency and completeness.
        
        Args:
            contract (Contract): The contract to verify
            
        Returns:
            VerificationResult: Detailed verification results
            
        Raises:
            ContractNotFoundError: If the contract doesn't exist
        """
        pass

    def evolve_contract(self, 
                       contract_id: str, 
                       changes: ContractChanges) -> ContractEvolution:
        """
        Evolve a contract while maintaining compatibility.
        
        Args:
            contract_id (str): Unique identifier of the contract
            changes (ContractChanges): Proposed changes to the contract
            
        Returns:
            ContractEvolution: Details of the contract evolution including
                compatibility layers and migration paths
            
        Raises:
            ContractNotFoundError: If the contract doesn't exist
            IncompatibleChangeError: If changes break compatibility
        """
        pass

Change Management

ProbabilisticImpactAnalyzer

Analyzes potential impacts of changes across the system.

class ProbabilisticImpactAnalyzer:
    def analyze_impact(self, 
                      change: Change, 
                      fractal_system: FractalSystem) -> ImpactAnalysisResult:
        """
        Analyze the potential impact of a change across the system.
        
        Args:
            change (Change): The proposed change
            fractal_system (FractalSystem): The system to analyze
            
        Returns:
            ImpactAnalysisResult: Detailed impact analysis including:
                - Impact probabilities
                - Affected components
                - Risk assessment
                - Suggested mitigations
            
        Raises:
            InvalidChangeError: If the change specification is invalid
            AnalysisTimeoutError: If analysis takes too long
        """
        pass

    def validate_change(self, 
                       change: Change, 
                       impact: ImpactAnalysisResult) -> ValidationResult:
        """
        Validate a change against its analyzed impact.
        
        Args:
            change (Change): The proposed change
            impact (ImpactAnalysisResult): Previously analyzed impact
            
        Returns:
            ValidationResult: Validation results including:
                - Safety assessment
                - Contract compliance
                - Resource impacts
                
        Raises:
            InvalidChangeError: If the change is invalid
            ValidationError: If validation fails
        """
        pass

Resource Management

ResourceLifecycleManager

Manages resource lifecycles across fractals.

class ResourceLifecycleManager:
    def track_resource(self, 
                      resource: Resource, 
                      context: FractalContext) -> ResourceTracker:
        """
        Begin tracking a resource's lifecycle.
        
        Args:
            resource (Resource): The resource to track
            context (FractalContext): Context in which the resource exists
            
        Returns:
            ResourceTracker: Resource tracking interface
            
        Raises:
            ResourceExistsError: If resource is already tracked
            InvalidResourceError: If resource specification is invalid
        """
        pass

    def optimize_resource(self, 
                        resource_id: str, 
                        constraints: OptimizationConstraints) -> OptimizationPlan:
        """
        Generate a resource optimization plan.
        
        Args:
            resource_id (str): Unique identifier of the resource
            constraints (OptimizationConstraints): Optimization constraints
            
        Returns:
            OptimizationPlan: Detailed optimization plan
            
        Raises:
            ResourceNotFoundError: If resource doesn't exist
            OptimizationError: If optimization fails
        """
        pass

Verification System

PropertyTestManager

Manages property-based testing across the system.

class PropertyTestManager:
    def generate_tests(self, 
                      fractal: Fractal, 
                      coverage_requirements: CoverageRequirements) -> TestSuite:
        """
        Generate a comprehensive test suite for a fractal.
        
        Args:
            fractal (Fractal): The fractal to test
            coverage_requirements (CoverageRequirements): Required coverage levels
            
        Returns:
            TestSuite: Generated test suite
            
        Raises:
            InvalidFractalError: If fractal is invalid
            CoverageError: If coverage requirements cannot be met
        """
        pass

    def execute_tests(self, 
                     test_suite: TestSuite, 
                     context: TestContext) -> TestResults:
        """
        Execute a test suite.
        
        Args:
            test_suite (TestSuite): The test suite to execute
            context (TestContext): Test execution context
            
        Returns:
            TestResults: Detailed test results
            
        Raises:
            TestExecutionError: If test execution fails
        """
        pass

AI Integration

AIIntegrationManager

Manages AI integration across the system.

class AIIntegrationManager:
    def generate_prompts(self, 
                        task: Task, 
                        context: AIContext) -> list[Prompt]:
        """
        Generate AI prompts for a task.
        
        Args:
            task (Task): The task requiring AI assistance
            context (AIContext): Context for AI processing
            
        Returns:
            list[Prompt]: Generated prompts
            
        Raises:
            InvalidTaskError: If task specification is invalid
            ContextError: If context is insufficient
        """
        pass

    def process_response(self, 
                        response: AIResponse, 
                        validation_criteria: ValidationCriteria) -> ProcessedResult:
        """
        Process and validate AI responses.
        
        Args:
            response (AIResponse): The AI response to process
            validation_criteria (ValidationCriteria): Criteria for validation
            
        Returns:
            ProcessedResult: Processed and validated result
            
        Raises:
            ValidationError: If response fails validation
            ProcessingError: If processing fails
        """
        pass

Novel Solution Management

NovelSolutionManager

Primary interface for managing the discovery and validation of novel solutions.

class NovelSolutionManager:
    def handle_novel_solution(self, solution_context: SolutionContext) -> DiscoveryResponse:
        """
        Process and validate a newly discovered solution pattern.
        
        Args:
            solution_context (SolutionContext): Context object containing:
                - solution: The novel solution implementation
                - affected_fractals: List of fractals involved
                - performance_metrics: Initial performance data
                - resource_usage: Resource utilization data
        
        Returns:
            DiscoveryResponse: Processing results including:
                - validation_results: Solution validation status
                - behavioral_data: Monitored behavior metrics
                - extracted_patterns: Identified reusable patterns
                - suggested_contracts: Proposed contract updates
            
        Raises:
            ValidationError: If solution fails validation
            ResourceError: If solution exceeds resource constraints
            BoundaryError: If solution violates fractal boundaries
        """
        pass

    def validate_solution(self, solution: Solution) -> ValidationResult:
        """
        Validate a novel solution against system constraints.
        
        Args:
            solution (Solution): The solution to validate
            
        Returns:
            ValidationResult: Validation results including:
                - contract_compliance: Contract validation results
                - resource_compliance: Resource usage validation
                - boundary_compliance: Boundary compliance check
                - stability_impact: System stability assessment
                
        Raises:
            ValidationError: If validation process fails
        """
        pass

    def extract_patterns(self, validated_solution: Solution) -> List[Pattern]:
        """
        Extract reusable patterns from a validated solution.
        
        Args:
            validated_solution (Solution): Previously validated solution
            
        Returns:
            List[Pattern]: Extracted reusable patterns
            
        Raises:
            ExtractionError: If pattern extraction fails
        """
        pass

PatternRegistry

Manages discovered patterns and their application across the system.

class PatternRegistry:
    def register_pattern(self, pattern: Pattern, context: PatternContext) -> str:
        """
        Register a new pattern for potential reuse.
        
        Args:
            pattern (Pattern): The pattern to register
            context (PatternContext): Pattern discovery context including:
                - origin_fractal: Source fractal
                - validation_results: Pattern validation data
                - performance_metrics: Performance characteristics
        
        Returns:
            str: Unique pattern identifier
            
        Raises:
            DuplicatePatternError: If pattern already exists
            ValidationError: If pattern fails validation
        """
        pass

    def query_patterns(self, search_context: SearchContext) -> List[Pattern]:
        """
        Query for applicable patterns.
        
        Args:
            search_context (SearchContext): Search parameters including:
                - target_fractal: Target application context
                - requirements: Required capabilities
                - constraints: Application constraints
        
        Returns:
            List[Pattern]: Matching patterns
            
        Raises:
            SearchError: If pattern search fails
        """
        pass

    def track_pattern_usage(self, pattern_id: str, usage_data: UsageData) -> UsageMetrics:
        """
        Track pattern usage and effectiveness.
        
        Args:
            pattern_id (str): Pattern identifier
            usage_data (UsageData): Pattern usage information
        
        Returns:
            UsageMetrics: Updated usage metrics
            
        Raises:
            PatternNotFoundError: If pattern doesn't exist
        """
        pass

CrossFractalOptimizer

Manages optimizations that span multiple fractals.

class CrossFractalOptimizer:
    def analyze_optimization_opportunity(self, fractals: List[Fractal]) -> OptimizationAnalysis:
        """
        Analyze potential cross-fractal optimizations.
        
        Args:
            fractals (List[Fractal]): Fractals to analyze
            
        Returns:
            OptimizationAnalysis: Analysis results including:
                - optimization_points: Identified optimization opportunities
                - impact_assessment: Potential impact analysis
                - resource_implications: Resource usage implications
                
        Raises:
            AnalysisError: If analysis fails
        """
        pass

    def apply_optimization(self, optimization_plan: OptimizationPlan) -> OptimizationResult:
        """
        Apply a cross-fractal optimization.
        
        Args:
            optimization_plan (OptimizationPlan): Plan details including:
                - target_fractals: Fractals to optimize
                - optimization_pattern: Pattern to apply
                - validation_criteria: Success criteria
        
        Returns:
            OptimizationResult: Optimization results
            
        Raises:
            OptimizationError: If optimization fails
            ValidationError: If validation fails
        """
        pass

    def monitor_optimization(self, optimization_id: str) -> OptimizationMetrics:
        """
        Monitor an applied optimization.
        
        Args:
            optimization_id (str): Optimization identifier
            
        Returns:
            OptimizationMetrics: Current metrics
            
        Raises:
            MonitoringError: If monitoring fails
        """
        pass

Integration Interfaces

SolutionContext

@dataclass
class SolutionContext:
    """Context for a novel solution."""
    solution: Solution
    affected_fractals: List[Fractal]
    performance_metrics: PerformanceMetrics
    resource_usage: ResourceUsage
    discovery_metadata: DiscoveryMetadata

Pattern

@dataclass
class Pattern:
    """Reusable solution pattern."""
    pattern_type: PatternType
    implementation: Implementation
    applicability_criteria: List[Criterion]
    resource_requirements: ResourceRequirements
    validation_rules: List[ValidationRule]

OptimizationPlan

@dataclass
class OptimizationPlan:
    """Plan for cross-fractal optimization."""
    target_fractals: List[Fractal]
    optimization_pattern: Pattern
    validation_criteria: List[ValidationRule]
    rollback_procedure: RollbackProcedure
    monitoring_config: MonitoringConfig

Common Types

# Core types
SolutionId = str
PatternId = str
OptimizationId = str

# Metric types
ConfidenceScore = float
EffectivenessScore = float
StabilityScore = float

# Collection types
PatternMap = Dict[PatternId, Pattern]
OptimizationMap = Dict[OptimizationId, OptimizationResult]
ValidationMap = Dict[str, ValidationResult]

Cross-Cutting Concerns

SecurityManager

Manages security aspects across the system.

class SecurityManager:
    def define_boundaries(self, 
                         system: FractalSystem) -> SecurityBoundaries:
        """
        Define security boundaries for a system.
        
        Args:
            system (FractalSystem): The system to analyze
            
        Returns:
            SecurityBoundaries: Defined security boundaries
            
        Raises:
            AnalysisError: If boundary analysis fails
        """
        pass

    def verify_security(self, 
                       boundaries: SecurityBoundaries, 
                       requirements: SecurityRequirements) -> SecurityReport:
        """
        Verify security compliance.
        
        Args:
            boundaries (SecurityBoundaries): Defined security boundaries
            requirements (SecurityRequirements): Security requirements
            
        Returns:
            SecurityReport: Security verification report
            
        Raises:
            ComplianceError: If security requirements are not met
        """
        pass

Error Handling

Common Exceptions

class FractalError(Exception):
    """Base exception for all fractal-related errors."""
    pass

class InvalidSpecificationError(FractalError):
    """Raised when a specification is invalid."""
    pass

class ResourceError(FractalError):
    """Base exception for resource-related errors."""
    pass

class ContractError(FractalError):
    """Base exception for contract-related errors."""
    pass

class ValidationError(FractalError):
    """Base exception for validation failures."""
    pass

class SecurityError(FractalError):
    """Base exception for security-related errors."""
    pass

Common Types

Data Types

from dataclasses import dataclass
from typing import Optional, List, Dict, Any

@dataclass
class FractalSpecification:
    """Specification for creating a new fractal."""
    name: str
    implementation: Dict[str, Any]
    knowledge: Dict[str, Any]
    contracts: List[ContractSpecification]
    resources: Optional[List[ResourceSpecification]] = None

@dataclass
class ContractSpecification:
    """Specification for creating a new contract."""
    name: str
    interfaces: List[InterfaceSpecification]
    behaviors: List[BehaviorSpecification]
    resources: Optional[List[ResourceSpecification]] = None

@dataclass
class ValidationResult:
    """Results of a validation operation."""
    is_valid: bool
    issues: List[ValidationIssue]
    recommendations: Optional[List[str]] = None

Type Aliases

# Core types
FractalId = str
ContractId = str
ResourceId = str

# Result types
ImpactScore = float
RiskLevel = float
ConfidenceScore = float

# Collection types
FractalMap = Dict[FractalId, Fractal]
ContractMap = Dict[ContractId, Contract]
ResourceMap = Dict[ResourceId, Resource]

This API reference provides a comprehensive overview of the core interfaces and types in the Code Fractalization Protocol. Each section includes detailed documentation of parameters, return values, and possible exceptions. The reference focuses on the most commonly used components while maintaining extensibility for specific implementations.

For more detailed examples and usage patterns, refer to the Examples and Templates documentation. For implementation guidance, see the Implementation Guide.