Imported from docxology/active_inference (docs/platform/knowledge_graph/AGENTS.md). Install upstream with npx skills add docxology/active_inference --skill knowledge_graph. Copyright stays with the author.

Knowledge Graph - Agent Development Guide

Guidelines for AI agents working with Knowledge Graph in the Active Inference Knowledge Environment.

"Active Inference for, with, by Generative AI"

🤖 Agent Role & Responsibilities

What agents should do when working with Knowledge Graph:

Primary Responsibilities

• Organize educational content, design learning paths, validate educational effectiveness
• Quality Assurance: Build validation and quality control systems
• Pattern Analysis: Extract and formalize development patterns
• Integration Systems: Ensure seamless integration with platform workflows

Development Focus Areas

1. Educational Content and Learning Systems
2. Quality Control: Create systems for validating functionality
3. Pattern Recognition: Develop tools for analyzing development patterns
4. Integration: Ensure seamless integration with platform workflows

🏗️ Architecture & Integration

Component Architecture

Understanding how Knowledge Graph fits into the platform:

Platform Layer
├── Knowledge Layer (foundations/, mathematics/, implementations/)
├── Knowledge Layer ← Knowledge Graph
└── Integration Layer (platform/, visualization/, tools/)

Integration Points

Knowledge Graph integrates with multiple platform components:

Upstream Components

• Knowledge Repository: Provides theoretical foundations
• Development Standards: Must follow documentation standards
• Quality Requirements: Integrate with validation systems

Downstream Components

• Platform Services: Leverages infrastructure for deployment
• User Interfaces: Provides functionality through UIs
• Integration APIs: Connects with external systems

💻 Development Patterns

Required Implementation Patterns

All knowledge_management development must follow these patterns:

1. Component Factory Pattern (PREFERRED)

def create_knowledge graph(config: Dict[str, Any]) -> Knowledge Graph:
    """Create Knowledge Graph using factory pattern with validation"""

    # Validate configuration
    validate_knowledge graph_config(config)

    # Create component with validation
    component = Knowledge Graph(config)

    # Validate functionality
    validate_component_functionality(component)

    return component

2. Component Configuration Pattern (MANDATORY)

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

@dataclass
class Knowledge GraphConfig:
    """Configuration for Knowledge Graph"""

    # Required fields
    component_name: str
    config_field: str

    # Optional fields with defaults
    debug_mode: bool = False
    optimization_level: str = "standard"

    def validate(self) -> List[str]:
        """Validate configuration"""
        errors = []

        if not self.component_name:
            errors.append("component_name cannot be empty")

        return errors

🧪 Testing Standards

Component Testing Categories (MANDATORY)

1. Unit Tests

Test individual functions and methods:

def test_knowledge graph_initialization():
    """Test Knowledge Graph initialization"""
    config = Knowledge GraphConfig(
        component_name="Knowledge Graph",
        config_field="test_value"
    )

    component = create_knowledge graph(config.to_dict())

    # Validate initialization
    assert component.config == config.to_dict()
    assert component.initialized == True

def test_knowledge graph_functionality():
    """Test core Knowledge Graph functionality"""
    config = Knowledge GraphConfig(
        component_name="Knowledge Graph",
        config_field="test_value"
    )

    component = create_knowledge graph(config.to_dict())

    # Test functionality
    result = component.process(test_input)
    assert result is not None

📖 Documentation Standards

Documentation Requirements (MANDATORY)

1. README.md Structure

Every component must have comprehensive README.md:

• Component overview and mission
• Architecture and integration points
• Usage examples and configuration
• API reference and testing information
• Development workflow and contribution guidelines

2. AGENTS.md Structure

Agent development guidelines must include:

• Role and responsibilities for agents
• Architecture and integration patterns
• Development workflow and standards
• Testing and validation requirements
• Quality assurance and best practices

🔄 Development Workflow

Agent Development Process

1. Task Assessment: Analyze component requirements
2. Architecture Planning: Design solutions following established patterns
3. Test-Driven Development: Write tests before implementation
4. Implementation: Follow coding standards and best practices
5. Documentation: Create comprehensive documentation
6. Quality Assurance: Ensure all tests pass and quality standards met
7. Integration: Integrate with existing platform components

Quality Assurance Workflow

1. Code Quality: Test coverage >95%, type safety, documentation
2. Integration Testing: Component interaction validation
3. Performance Validation: Performance characteristics verified
4. Documentation Review: README.md and AGENTS.md completeness
5. Standards Compliance: Follow all established standards

🎯 Quality Standards

Code Quality Gates

• Test Coverage: >95% for core components, >80% overall
• Type Safety: Complete type annotations for all interfaces
• Documentation Coverage: 100% for public APIs and interfaces
• Code Style: PEP 8 compliance with automated formatting
• Error Handling: Comprehensive error handling with informative messages

Component Quality Gates

• Functionality: All specified features implemented and tested
• Integration: Seamless integration with platform components
• Performance: Meets performance requirements for target use cases
• Reliability: Robust operation under various conditions
• Maintainability: Clean, extensible code following established patterns

🔧 Integration Guidelines

Platform Integration

• Service Integration: Connect with platform services as needed
• Data Flow: Ensure proper data flow and transformation
• Error Handling: Implement comprehensive error handling
• Logging: Add appropriate logging for monitoring and debugging
• Configuration: Support flexible configuration options

Cross-Component Compatibility

• API Compatibility: Maintain compatible interfaces
• Data Format Standards: Follow established data format standards
• Communication Protocols: Use standard communication methods
• Version Management: Handle version compatibility appropriately

🐛 Troubleshooting and Support

Common Development Issues

1. Configuration Problems: Validate configuration schema and values
2. Integration Issues: Check component dependencies and interfaces
3. Performance Issues: Profile and optimize bottlenecks
4. Testing Failures: Debug test cases and fix implementation

Debug Mode

# Enable debug logging
import logging
logging.basicConfig(level=logging.DEBUG)

# Or use debug configuration
debug_config = {"debug": True, "logging_level": "DEBUG"}
component = Knowledge Graph(debug_config)

📚 Resources and References

Core Documentation

• Main README: Project overview and navigation
• AGENTS.md: Master agent guidelines
• Development Standards: Complete development standards

Component-Specific Resources

• API Documentation: Component API reference
• Integration Guide ../../integration/README.md: Integration patterns
• Testing Guide ../../testing/README.md: Testing standards and methods

Related Components

• Related Component 1 ../related1/README.md: Description of related functionality
• Related Component 2 ../related2/README.md: Description of related functionality

"Active Inference for, with, by Generative AI" - Enhancing platform development through structured guidance, comprehensive documentation, and collaborative intelligence.

Component: Knowledge Graph | Version: 1.0.0 | Last Updated: October 2024