soma-core 2.0.0

# SOMA-CORE API Specification v2.0

**Last Updated:** January 2025  
**Status:** Production Ready  
**Target Audience:** Integration Partners, Platform Developers, Researchers  

---

## 🎯 Overview

SOMA-CORE provides a comprehensive cognitive reasoning engine with 15 cognitive operators, self-aware capabilities, and production-ready reliability. This document serves as the definitive integration guide for external platforms.

## 🚀 Quick Start Integration

### Basic Setup

```rust
use soma_core::prelude::*;

// Initialize cognitive operators
let introspect = IntrospectOperator;
let visual_reasoning = VisualReasoningOperator;
let meta_reflective = MetaReflectiveOperator;

// Create symbolic context for analysis
let mut context = SymbolicContext::new();
context.set("analysis_target", "rust_function");
context.set("complexity_threshold", "medium");

// Execute cognitive analysis
let result = introspect.execute(&context)?;
```

### Advanced Integration Pattern

```rust
use soma_core::prelude::*;

pub struct CognitivePlatform {
    edit_system: EditClassificationSystem,
    git_integration: GitIntegrationSystem,
    cognitive_agents: Vec<Box<dyn SomaOperator>>,
}

impl CognitivePlatform {
    pub fn new() -> Self {
        let mut cognitive_agents: Vec<Box<dyn SomaOperator>> = vec![
            Box::new(IntrospectOperator),
            Box::new(MetaReflectiveOperator),
            Box::new(VisualReasoningOperator),
            Box::new(ConsensusOperator),
        ];
        
        Self {
            edit_system: EditClassificationSystem::new(),
            git_integration: GitIntegrationSystem::new(),
            cognitive_agents,
        }
    }
    
    pub fn analyze_code_intelligently(&self, code: &str) -> CognitiveSuggestions {
        // Use SOMA-CORE's cognitive capabilities
        // See examples/ directory for full implementation
    }
}
```

## 🧠 Core Cognitive Operators

### Meta-Cognitive Layer

#### **IntrospectOperator**
- **Purpose**: System state analysis and self-awareness
- **Cognitive Cost**: 2.0
- **Use Cases**: Performance monitoring, bottleneck detection
- **Input Context**: `analysis_depth`, `focus_areas`
- **Output**: System health metrics, performance insights

```rust
let introspect = IntrospectOperator;
let mut context = SymbolicContext::new();
context.set("analysis_depth", "comprehensive");
let result = introspect.execute(&context)?;
```

#### **MetaReflectiveOperator**
- **Purpose**: Meta-cognitive reflection and optimization
- **Cognitive Cost**: 3.0
- **Use Cases**: System optimization, strategic planning
- **Input Context**: `reflection_scope`, `optimization_targets`
- **Output**: Optimization recommendations, meta-analysis

#### **VisualReasoningOperator**
- **Purpose**: Code structure analysis and pattern recognition
- **Cognitive Cost**: 2.5
- **Use Cases**: Dependency mapping, architecture analysis
- **Input Context**: `code_text`, `analysis_mode`
- **Output**: Visual insights, dependency graphs, pattern detection

### Multi-Agent Reasoning

#### **ConsensusOperator**
- **Purpose**: Multi-agent decision making and conflict resolution
- **Cognitive Cost**: 2.8
- **Use Cases**: Team decision support, approval workflows
- **Input Context**: `agents`, `decision_context`, `voting_strategy`
- **Output**: Consensus results, confidence scores

#### **EmpathyOperator**
- **Purpose**: User context understanding and perspective modeling
- **Cognitive Cost**: 2.2
- **Use Cases**: User experience optimization, context adaptation
- **Input Context**: `user_profile`, `interaction_history`
- **Output**: Empathy insights, user modeling

#### **NegotiateOperator**
- **Purpose**: Conflict resolution and compromise finding
- **Cognitive Cost**: 2.6
- **Use Cases**: Merge conflict resolution, preference balancing
- **Input Context**: `stakeholders`, `conflict_context`, `constraints`
- **Output**: Negotiated solutions, compromise proposals

### Uncertainty Management

#### **UncertaintyPropagateOperator**
- **Purpose**: Confidence tracking and uncertainty propagation
- **Cognitive Cost**: 1.5
- **Use Cases**: Risk assessment, confidence evaluation
- **Input Context**: `initial_confidence`, `propagation_model`
- **Output**: Updated confidence metrics, uncertainty analysis

#### **DoubtOperator**
- **Purpose**: Critical analysis and doubt introduction
- **Cognitive Cost**: 1.8
- **Use Cases**: Quality assurance, critical review
- **Input Context**: `analysis_target`, `doubt_threshold`
- **Output**: Critical insights, doubt metrics

### Cognitive Load Management

#### **CognitiveLoadOperator**
- **Purpose**: Complexity analysis and cognitive load assessment
- **Cognitive Cost**: 2.0
- **Use Cases**: Complexity reduction, cognitive optimization
- **Input Context**: `complexity_target`, `assessment_criteria`
- **Output**: Cognitive load metrics, optimization suggestions

#### **AttentionFocusOperator**
- **Purpose**: Attention management and priority focusing
- **Cognitive Cost**: 1.7
- **Use Cases**: Priority management, attention optimization
- **Input Context**: `attention_targets`, `priority_weights`
- **Output**: Focus recommendations, attention allocation

### Core Operators

#### **AddOperator, ComposeOperator, IfThenOperator, ReflectOperator, DelayOperator**
- **Purpose**: Basic cognitive operations and symbolic manipulation
- **Cognitive Cost**: 0.4 - 0.8
- **Use Cases**: Data processing, logical operations, context management

## 🎯 Advanced Edit Control System

### EditClassificationSystem

```rust
use soma_core::prelude::*;

let classifier = EditClassificationSystem::new();

// Classify edit with risk assessment
let edit = ProposedEdit::new(/* parameters */);
let classification = classifier.classify_edit(&edit)?;

match classification.category {
    EditCategory::Critical => {
        // Handle critical edits with special approval
    },
    EditCategory::Safe => {
        // Auto-approve safe edits
    },
    // ... handle other categories
}
```

### StagedApplicationSystem

```rust
let staged_system = StagedApplicationSystem::new();

// Create staging workflow
staged_system.add_stage("validation", vec![edit1, edit2])?;
staged_system.add_stage("testing", vec![edit3])?;
staged_system.add_stage("deployment", vec![edit4, edit5])?;

// Execute with rollback capabilities
let results = staged_system.execute_all_stages()?;
if results.has_failures() {
    staged_system.rollback_to_stage("validation")?;
}
```

## 🔌 Integration Patterns

### For Development Platforms (like AetherWeaver)

```rust
pub struct AetherWeaverIntegration {
    cognitive_engine: Vec<Box<dyn SomaOperator>>,
    edit_system: EditClassificationSystem,
}

impl AetherWeaverIntegration {
    pub fn analyze_flow_node(&self, node: &FlowNode) -> CognitiveInsights {
        let mut context = SymbolicContext::new();
        context.set("node_type", &node.node_type);
        context.set("connections", &node.connections.len().to_string());
        
        // Use SOMA-CORE's visual reasoning
        let visual_op = VisualReasoningOperator;
        let insights = visual_op.execute(&context).unwrap();
        
        // Convert to AetherWeaver-specific insights
        CognitiveInsights::from_soma_context(insights)
    }
    
    pub fn suggest_flow_optimizations(&self, flow: &VisualFlow) -> Vec<OptimizationSuggestion> {
        // Use meta-reflective operator for optimization
        let meta_op = MetaReflectiveOperator;
        // Implementation details...
    }
}
```

### For Enterprise Platforms

```rust
pub struct EnterpriseCodeReview {
    cognitive_reviewers: HashMap<String, Box<dyn SomaOperator>>,
    approval_system: StagedApplicationSystem,
}

impl EnterpriseCodeReview {
    pub fn cognitive_code_review(&self, pull_request: &PullRequest) -> ReviewResults {
        let mut review_context = SymbolicContext::new();
        review_context.set("pr_size", &pull_request.changes.len().to_string());
        review_context.set("security_scope", &pull_request.security_impact);
        
        // Multi-agent review process
        let consensus_op = ConsensusOperator;
        let security_insights = consensus_op.execute(&review_context)?;
        
        ReviewResults::from_cognitive_analysis(security_insights)
    }
}
```

### For Research Projects

```rust
pub struct CognitiveResearchPlatform {
    meta_cognitive_layer: MetaReflectiveOperator,
    uncertainty_tracker: UncertaintyPropagateOperator,
    experiment_design: Vec<Box<dyn SomaOperator>>,
}

impl CognitiveResearchPlatform {
    pub fn study_emergent_behavior(&self, system_state: &SystemState) -> EmergentBehaviorAnalysis {
        // Use SOMA-CORE's meta-cognitive capabilities for research
        let meta_analysis = self.meta_cognitive_layer.execute(&system_state.to_context())?;
        
        EmergentBehaviorAnalysis {
            emergence_indicators: meta_analysis.get("emergence_level"),
            cognitive_patterns: meta_analysis.get("pattern_analysis"),
            optimization_opportunities: meta_analysis.get("optimization_recommendations"),
        }
    }
}
```

## 🔒 Security & Best Practices

### API Key Management

```rust
// ✅ Secure API key handling
use std::env;

fn setup_llm_integration() -> Result<LLMOperator> {
    let api_key = env::var("OPENAI_API_KEY")
        .map_err(|_| "Missing OPENAI_API_KEY environment variable")?;
    
    LLMOperator {
        id: "gpt-analysis".to_string(),
        model: "gpt-4".to_string(),
        provider: "gpt".to_string(),
        api_key,
    }
}
```

### Error Handling Patterns

```rust
use anyhow::Result;

pub fn robust_cognitive_analysis(input: &str) -> Result<CognitiveInsights> {
    let mut context = SymbolicContext::new();
    context.set("input", input);
    
    // Chain cognitive operators with error handling
    let introspect = IntrospectOperator;
    let introspect_result = introspect.execute(&context)
        .map_err(|e| anyhow!("Introspection failed: {}", e))?;
    
    let visual_reasoning = VisualReasoningOperator;
    let visual_result = visual_reasoning.execute(&introspect_result)
        .map_err(|e| anyhow!("Visual reasoning failed: {}", e))?;
    
    Ok(CognitiveInsights::from_context(visual_result))
}
```

## 📊 Performance Considerations

### Cognitive Cost Management

```rust
pub struct CognitiveBudget {
    max_cognitive_cost: f64,
    current_cost: f64,
}

impl CognitiveBudget {
    pub fn can_execute(&self, operator: &dyn SomaOperator) -> bool {
        self.current_cost + operator.cognitive_cost() <= self.max_cognitive_cost
    }
    
    pub fn execute_with_budget(&mut self, operator: &dyn SomaOperator, context: &SymbolicContext) -> Result<SymbolicContext> {
        if !self.can_execute(operator) {
            return Err(anyhow!("Cognitive budget exceeded"));
        }
        
        let result = operator.execute(context)?;
        self.current_cost += operator.cognitive_cost();
        Ok(result)
    }
}
```

### Async Integration

```rust
use tokio;

pub async fn async_cognitive_processing(inputs: Vec<SymbolicContext>) -> Result<Vec<SymbolicContext>> {
    let introspect = IntrospectOperator;
    
    let tasks: Vec<_> = inputs.into_iter().map(|context| {
        let op = &introspect;
        tokio::spawn(async move {
            op.execute(&context)
        })
    }).collect();
    
    let mut results = Vec::new();
    for task in tasks {
        results.push(task.await??);
    }
    
    Ok(results)
}
```

## 🎛️ Configuration Options

### Environment Variables

```bash
# LLM Integration
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GOOGLE_API_KEY=AIza...

# Performance Tuning
MAX_TOKENS=2000
TEMPERATURE=0.7
API_TIMEOUT=30

# Security
USE_MOCK_RESPONSES=false
LOG_API_REQUESTS=false
DEBUG_MODE=false
ENVIRONMENT=production
```

### Runtime Configuration

```rust
use soma_core::prelude::*;

// Configure cognitive operators
let mut config = CognitiveConfig::new();
config.set_max_cognitive_cost(10.0);
config.set_uncertainty_threshold(0.8);
config.enable_meta_reflection(true);

// Apply configuration
let configured_introspect = IntrospectOperator::with_config(config);
```

## 📈 Monitoring & Observability

### Cognitive Metrics

```rust
pub struct CognitiveMetrics {
    pub total_operations: u64,
    pub average_cognitive_cost: f64,
    pub uncertainty_levels: Vec<f64>,
    pub performance_scores: HashMap<String, f64>,
}

impl CognitiveMetrics {
    pub fn track_operation(&mut self, operator: &dyn SomaOperator, result: &SymbolicContext) {
        self.total_operations += 1;
        self.average_cognitive_cost = 
            (self.average_cognitive_cost * (self.total_operations - 1) as f64 + operator.cognitive_cost()) 
            / self.total_operations as f64;
        
        // Extract uncertainty from result
        if let Some(uncertainty) = result.get("uncertainty_score") {
            if let Ok(score) = uncertainty.parse::<f64>() {
                self.uncertainty_levels.push(score);
            }
        }
    }
}
```

## 🔗 External Resources

### Documentation Links
- **Examples Repository**: See `examples/` directory for comprehensive usage patterns
- **GitHub Repository**: https://github.com/soma-core/soma-core
- **API Documentation**: https://docs.rs/soma-core
- **Integration Guides**: Coming soon - detailed platform-specific guides

### Community & Support
- **Discussions**: GitHub Discussions for integration questions
- **Issues**: GitHub Issues for bug reports and feature requests
- **Research Collaboration**: Contact for academic partnerships

---

**This API specification serves as the foundation for integrating SOMA-CORE's cognitive capabilities into any development platform, enabling intelligent, self-aware features with production-ready reliability.**