# Enhanced Knowledge Graph Tools - AI Guidance Improvements
## Summary
The Knowledge Graph incarnation tools have been significantly enhanced to provide comprehensive guidance, validation, and error handling specifically designed for AI agents and human users. The improvements ensure that AI can easily understand how to use the tools correctly and receive actionable feedback when errors occur.
## Key Improvements Made
### 1. Enhanced `create_relations` Tool
**Better Documentation:**
- Comprehensive docstring with multiple example usage patterns
- Clear explanation of relationship directionality
- Categorized relationship types (Technical, Conceptual, Workflow, etc.)
- Error prevention tips and best practices
**Improved Validation:**
- Pre-validation to check if entities exist before creating relations
- Specific error messages for missing/invalid fields
- Helpful examples in error messages showing correct format
- Entity existence checking with actionable guidance
**Enhanced Error Messages:**
- Clear indication of which entities are missing
- Step-by-step guidance on how to create missing entities
- Examples of proper `create_entities` calls in error responses
### 2. Enhanced `create_entities` Tool
**Better Documentation:**
- Detailed examples organized by domain (Technology, Business, Academic, etc.)
- Clear explanation of required fields and formats
- Usage patterns for different scenarios
- Tips for creating good entity structures
**Improved Validation:**
- Comprehensive field validation with specific error messages
- Type checking for all required fields
- Non-empty string validation
- Better handling of observation arrays
**Enhanced Error Messages:**
- Position-specific error messages (Entity 1, Entity 2, etc.)
- Format examples in error messages
- Clear explanation of expected data types
### 3. Enhanced `read_graph` Tool
**Better Documentation:**
- Clear explanation of what the tool returns
- Use cases for AI agents
- Description of output format
- Guidance on when to use this tool
### 4. Added Safe Read Execution Method
**New `_safe_execute_read` Method:**
- Prevents transaction scope errors for read operations
- Consistent error handling pattern
- Returns processed results or None on error
### 5. Entity Existence Validation
**Pre-validation for Relations:**
- Checks all referenced entities exist before creating relations
- Provides specific feedback about missing entities
- Offers concrete examples of how to create missing entities
- Prevents silent failures
## Technical Improvements
### Error Handling
- All methods now use safe session execution patterns
- Comprehensive exception handling with logging
- User-friendly error messages that don't expose internal details
- Actionable guidance in error responses
### Validation
- Input type checking for all parameters
- Required field validation with specific error messages
- Data format validation (strings, arrays, objects)
- Business logic validation (entity existence)
### Documentation
- Extensive docstrings with multiple examples
- Clear parameter descriptions with expected formats
- Usage patterns organized by scenario
- Best practices and tips for AI agents
## AI-Friendly Features
### 1. Comprehensive Examples
Each tool now includes multiple real-world examples organized by:
- Technology stack relationships
- Conceptual knowledge connections
- Business process flows
- Academic/research relationships
### 2. Clear Format Specifications
- Required JSON structure clearly specified
- Field types and constraints documented
- Common relationship types categorized and listed
- Expected entity types by domain provided
### 3. Actionable Error Messages
- Specific field-level validation errors
- Format examples in error responses
- Step-by-step recovery instructions
- Concrete code examples for fixes
### 4. Progressive Guidance
- Tools guide users through proper workflow
- Entity creation before relationship creation
- Validation feedback suggests next steps
- Error messages include recovery procedures
## Usage Examples
### Creating Entities with Enhanced Validation
```python
# This will now provide detailed validation and guidance
entities = [
{
"name": "React",
"entityType": "Framework",
"observations": ["JavaScript UI library", "Component-based architecture"]
}
]
result = await incarnation.create_entities(entities)
```
### Creating Relations with Entity Validation
```python
# This will now check if entities exist and provide helpful errors
relations = [
{"from": "React", "to": "JavaScript", "relationType": "DEPENDS_ON"}
]
result = await incarnation.create_relations(relations)
```
### Reading Graph with Better Context
```python
# Enhanced documentation explains when and how to use this
result = await incarnation.read_graph()
```
## Benefits for AI Agents
1. **Reduced Trial and Error**: Clear format specifications prevent common mistakes
2. **Better Error Recovery**: Actionable error messages with concrete solutions
3. **Workflow Guidance**: Tools guide through proper sequence of operations
4. **Knowledge Discovery**: Enhanced read_graph helps understand existing data
5. **Validation Feedback**: Immediate feedback on data format and business rules
## Benefits for Human Users
1. **Learning Support**: Comprehensive examples teach proper usage
2. **Error Prevention**: Clear documentation prevents common mistakes
3. **Best Practices**: Built-in guidance on relationship naming and structure
4. **Debugging Help**: Detailed error messages speed up problem resolution
## Testing
A comprehensive test script (`test_enhanced_knowledge_graph.py`) demonstrates:
- Validation error handling
- Proper usage patterns
- Entity existence checking
- Error recovery workflows
- Complete knowledge graph operations
These enhancements make the Knowledge Graph tools robust, user-friendly, and particularly well-suited for AI agents that need clear guidance and reliable error handling.
