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docs/DEVELOPMENT_ENVIRONMENT.md

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docs/contributing/CONTRIBUTING.md

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docs/models/compliance-patterns.md

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+# Compliance Patterns for LN1 Models
+
+This document outlines the compliance patterns and regulatory frameworks implemented in LN1 models to ensure adherence to legal and privacy standards while processing sensitive legal documents.
+
+## Core Compliance Patterns
+
+### Data Protection
+```python
+class DataProtectionPattern:
+    def __init__(self):
+        self.privacy_manager = PrivacyManager()
+        self.encryption_handler = EncryptionHandler()
+        self.access_controller = AccessController()
+
+    def apply_protection(self, data):
+        return {
+            'anonymization': self.privacy_manager.anonymize(),
+            'encryption': self.encryption_handler.encrypt(),
+            'access_control': self.access_controller.restrict()
+        }
+```
+
+### Regulatory Frameworks
+
+**Supported Standards**
+- GDPR Compliance
+- HIPAA Requirements
+- CCPA Regulations
+- Industry-specific standards
+
+## Implementation Guidelines
+
+### Privacy-Preserving Patterns
+```python
+class PrivacyPattern:
+    def implement_privacy(self):
+        return {
+            'data_minimization': self.minimize_data_collection(),
+            'purpose_limitation': self.restrict_usage(),
+            'storage_limitation': self.manage_retention(),
+            'transparency': self.provide_transparency()
+        }
+```
+
+### Audit Mechanisms
+1. Automated compliance checks
+2. Regular audit logging
+3. Violation detection
+4. Remediation tracking
+
+## Model-Specific Compliance
+
+### Training Data Compliance
+```python
+class TrainingCompliance:
+    def ensure_compliance(self, training_data):
+        return {
+            'data_source': self.verify_source(),
+            'consent_status': self.check_consent(),
+            'usage_rights': self.verify_rights(),
+            'retention_policy': self.check_retention()
+        }
+```
+
+### Inference Privacy
+- Data anonymization
+- Secure computation
+- Result filtering
+- Access controls
+
+## Documentation Requirements
+
+### Compliance Documentation
+```python
+class ComplianceDoc:
+    def generate_documentation(self):
+        return {
+            'privacy_impact': self.assess_privacy_impact(),
+            'data_flow': self.document_data_flow(),
+            'security_measures': self.list_security_measures(),
+            'compliance_checks': self.detail_compliance_checks()
+        }
+```
+
+## Monitoring and Reporting
+
+### Compliance Monitoring
+```python
+class ComplianceMonitor:
+    def monitor_compliance(self):
+        return {
+            'violation_detection': self.detect_violations(),
+            'audit_logs': self.collect_audit_logs(),
+            'performance_metrics': self.track_metrics(),
+            'incident_reports': self.generate_reports()
+        }
+```
+
+### Reporting Requirements
+- Regular compliance reports
+- Incident documentation
+- Audit trail maintenance
+- Stakeholder communications
+
+## Security Integration
+
+### Security Patterns
+```python
+class SecurityPattern:
+    def implement_security(self):
+        return {
+            'access_control': self.control_access(),
+            'data_encryption': self.encrypt_data(),
+            'secure_processing': self.secure_compute(),
+            'audit_logging': self.log_activities()
+        }
+```
+
+## Error Handling
+
+### Compliance Violations
+```python
+class ViolationHandler:
+    def handle_violation(self, violation):
+        return {
+            'detection': self.detect_violation(violation),
+            'assessment': self.assess_impact(violation),
+            'remediation': self.implement_fix(violation),
+            'reporting': self.report_incident(violation)
+        }
+```
+
+## Testing Framework
+
+### Compliance Testing
+```python
+class ComplianceTester:
+    def test_compliance(self):
+        return {
+            'privacy_tests': self.run_privacy_tests(),
+            'security_tests': self.run_security_tests(),
+            'regulatory_tests': self.verify_regulations(),
+            'documentation_tests': self.validate_documentation()
+        }
+```
+
+## Integration Guidelines
+
+### System Integration
+- API compliance
+- Data flow controls
+- Security protocols
+- Audit integration
+
+### Deployment Requirements
+```python
+class ComplianceDeployment:
+    def verify_deployment(self):
+        return {
+            'environment_check': self.check_environment(),
+            'security_verification': self.verify_security(),
+            'compliance_validation': self.validate_compliance(),
+            'documentation_review': self.review_documentation()
+        }
+```
+

docs/models/precedent-graphs.md

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+# Precedent Graph Models
+
+The Precedent Graph system models legal relationships and citations as a directed graph, enabling sophisticated legal analysis and precedent tracking within the LN1 node network.
+
+## Graph Structure
+
+### Core Components
+```python
+class PrecedentGraph:
+    def __init__(self):
+        self.nodes = {}  # Legal documents
+        self.edges = {}  # Citations and relationships
+        self.metadata = MetadataManager()
+        self.analyzer = GraphAnalyzer()
+```
+
+### Node Properties
+```typescript
+interface DocumentNode {
+    id: string;
+    metadata: {
+        title: string;
+        jurisdiction: string;
+        date: timestamp;
+        court: string;
+        status: string;
+    };
+    relationships: {
+        citations: string[];
+        references: string[];
+        overruled: string[];
+    }
+}
+```
+
+## Graph Operations
+
+### Citation Analysis
+```python
+class CitationAnalyzer:
+    def analyze_citations(self, document):
+        return {
+            'forward_citations': self.get_citing_documents(),
+            'backward_citations': self.get_cited_documents(),
+            'citation_strength': self.calculate_importance(),
+            'citation_context': self.extract_context()
+        }
+```
+
+### Relationship Mapping
+- Direct citations
+- Indirect references
+- Overruling relationships
+- Related cases
+
+## Graph Algorithms
+
+### Path Analysis
+```python
+class PathAnalyzer:
+    def analyze_precedent_path(self, start_node, end_node):
+        return {
+            'shortest_path': self.find_shortest_path(),
+            'all_paths': self.find_all_paths(),
+            'path_strength': self.calculate_path_strength(),
+            'key_intermediates': self.identify_key_nodes()
+        }
+```
+
+### Centrality Metrics
+- PageRank implementation
+- Citation authority
+- Legal influence scoring
+- Node importance
+
+## Visualization
+
+### Graph Rendering
+```python
+class GraphVisualizer:
+    def generate_visualization(self, graph_subset):
+        return {
+            'layout': self.calculate_layout(),
+            'node_styling': self.style_nodes(),
+            'edge_styling': self.style_edges(),
+            'interactive_elements': self.add_interactivity()
+        }
+```
+
+### Interactive Features
+- Zoom and pan
+- Node filtering
+- Path highlighting
+- Relationship exploration
+
+## Data Integration
+
+### Document Processing
+```python
+class DocumentProcessor:
+    def process_document(self, document):
+        return {
+            'extracted_citations': self.extract_citations(),
+            'relationship_mapping': self.map_relationships(),
+            'context_analysis': self.analyze_context(),
+            'metadata_enrichment': self.enrich_metadata()
+        }
+```
+
+### Update Mechanisms
+- Real-time updates
+- Batch processing
+- Incremental changes
+- Version control
+
+## Analysis Tools
+
+### Legal Research
+```python
+class ResearchTools:
+    def analyze_precedent(self, case_id):
+        return {
+            'authority_score': self.calculate_authority(),
+            'citation_network': self.map_network(),
+            'temporal_analysis': self.analyze_timeline(),
+            'jurisdiction_mapping': self.map_jurisdictions()
+        }
+```
+
+### Impact Analysis
+- Citation frequency
+- Authority metrics
+- Temporal relevance
+- Jurisdiction spread
+
+## Performance Optimization
+
+### Graph Operations
+```python
+class GraphOptimizer:
+    def optimize_operations(self):
+        return {
+            'index_optimization': self.optimize_indices(),
+            'query_caching': self.implement_cache(),
+            'batch_processing': self.optimize_batches(),
+            'memory_management': self.manage_memory()
+        }
+```
+
+### Scaling Considerations
+- Distributed processing
+- Partition strategies
+- Cache management
+- Query optimization
+
+## Integration APIs
+
+### Query Interface
+```python
+class GraphAPI:
+    def query_endpoints(self):
+        return {
+            '/graph/search': self.handle_search,
+            '/graph/analyze': self.handle_analysis,
+            '/graph/visualize': self.handle_visualization,
+            '/graph/update': self.handle_updates
+        }
+```
+
+## Monitoring and Metrics
+
+### Performance Tracking
+```python
+class GraphMonitor:
+    def track_metrics(self):
+        return {
+            'query_performance': self.measure_query_speed(),
+            'graph_size': self.measure_graph_size(),
+            'update_frequency': self.track_updates(),
+            'usage_patterns': self.analyze_usage()
+        }
+```
+
+Remember to update this documentation as new features and improvements are implemented in the precedent graph system.