knowledge-generation-framework-design.md•9.12 kB
# Knowledge Generation Framework Design
## Overview
The Knowledge Generation framework implements advanced prompting techniques to generate domain-specific architectural knowledge before making decisions. This enhances the accuracy and context-awareness of all MCP ADR Analysis Server tools while maintaining the 100% prompt-driven architecture.
## Core Concept
**Knowledge Generation Prompting** works by:
1. **Domain Analysis**: Identify the architectural domain and context
2. **Knowledge Generation**: Generate relevant domain-specific knowledge
3. **Knowledge Integration**: Combine generated knowledge with task prompts
4. **Decision Making**: Use enhanced prompts for better decision quality
## Architecture Integration
### Existing Components Integration
- **PromptObject Interface**: Knowledge generation returns PromptObject format
- **combinePrompts()**: Combines knowledge prompts with task prompts
- **Cache System**: Caches generated knowledge for performance
- **Security Validation**: Validates generated knowledge content
### Framework Components
```
Knowledge Generation Framework
├── Domain Templates (Web, Mobile, Microservices, etc.)
├── Knowledge Generator (Core generation logic)
├── Knowledge Cache (Persistent knowledge storage)
├── Integration Layer (Tool integration utilities)
└── Configuration (Settings and customization)
```
## Interface Definitions
### Core Knowledge Interfaces
```typescript
export interface DomainKnowledge {
domain: ArchitecturalDomain;
knowledge: KnowledgeItem[];
confidence: number;
timestamp: string;
sources: string[];
metadata: KnowledgeMetadata;
}
export interface KnowledgeItem {
category: KnowledgeCategory;
title: string;
content: string;
relevance: number;
evidence: string[];
tags: string[];
}
export interface KnowledgeGenerationConfig {
domains: ArchitecturalDomain[];
depth: 'basic' | 'intermediate' | 'advanced';
cacheEnabled: boolean;
cacheTTL: number;
securityValidation: boolean;
customTemplates?: DomainTemplate[];
}
export interface KnowledgeGenerationResult {
knowledgePrompt: PromptObject;
enhancedPrompt: PromptObject;
cacheKey: string;
metadata: GenerationMetadata;
}
```
### Domain Categories
```typescript
export type ArchitecturalDomain =
| 'web-applications'
| 'mobile-applications'
| 'microservices'
| 'database-design'
| 'cloud-infrastructure'
| 'devops-cicd'
| 'security-patterns'
| 'performance-optimization'
| 'api-design'
| 'data-architecture';
export type KnowledgeCategory =
| 'best-practices'
| 'design-patterns'
| 'anti-patterns'
| 'technology-specific'
| 'performance-considerations'
| 'security-guidelines'
| 'scalability-patterns'
| 'testing-strategies';
```
## Domain Knowledge Templates
### Web Applications Template
```typescript
const webApplicationsTemplate: DomainTemplate = {
domain: 'web-applications',
categories: [
{
category: 'best-practices',
items: [
'Component-based architecture principles',
'State management patterns (Redux, Zustand, Context)',
'Performance optimization techniques',
'Accessibility (a11y) guidelines',
'SEO optimization strategies'
]
},
{
category: 'design-patterns',
items: [
'Model-View-Controller (MVC)',
'Component composition patterns',
'Higher-Order Components (HOCs)',
'Render props pattern',
'Custom hooks pattern'
]
}
]
};
```
### Microservices Template
```typescript
const microservicesTemplate: DomainTemplate = {
domain: 'microservices',
categories: [
{
category: 'best-practices',
items: [
'Service decomposition strategies',
'API gateway patterns',
'Service mesh architecture',
'Event-driven communication',
'Distributed data management'
]
},
{
category: 'anti-patterns',
items: [
'Distributed monolith',
'Chatty interfaces',
'Shared databases',
'Synchronous communication overuse'
]
}
]
};
```
## Core Functions
### Primary Knowledge Generation Function
```typescript
export async function generateArchitecturalKnowledge(
context: ArchitecturalContext,
config: KnowledgeGenerationConfig = {}
): Promise<KnowledgeGenerationResult>
```
### Domain Detection Function
```typescript
export async function detectArchitecturalDomains(
projectContext: ProjectContext
): Promise<ArchitecturalDomain[]>
```
### Knowledge Enhancement Function
```typescript
export async function enhancePromptWithKnowledge(
originalPrompt: PromptObject,
domains: ArchitecturalDomain[],
config: KnowledgeGenerationConfig = {}
): Promise<PromptObject>
```
## Caching Strategy
### Cache Key Generation
```typescript
function generateKnowledgeCacheKey(
domains: ArchitecturalDomain[],
context: ArchitecturalContext,
config: KnowledgeGenerationConfig
): string {
return `knowledge:${domains.join('+')}-${hashContext(context)}-${hashConfig(config)}`;
}
```
### Cache TTL Strategy
- **Basic Knowledge**: 24 hours (stable architectural principles)
- **Technology-Specific**: 6 hours (evolving best practices)
- **Project-Specific**: 1 hour (context-dependent knowledge)
## Security Validation
### Knowledge Content Validation
1. **Source Verification**: Ensure knowledge comes from trusted sources
2. **Content Sanitization**: Remove potentially harmful content
3. **Relevance Validation**: Verify knowledge relevance to context
4. **Quality Assessment**: Evaluate knowledge quality and accuracy
### Security Checks
```typescript
export interface KnowledgeSecurityCheck {
contentSafety: boolean;
sourceReliability: number;
relevanceScore: number;
qualityScore: number;
warnings: string[];
}
```
## Integration Patterns
### Tool Integration Example
```typescript
// In suggest_adrs tool
const knowledgeResult = await generateArchitecturalKnowledge(
{ domains: ['web-applications', 'api-design'], projectContext },
{ depth: 'intermediate', cacheEnabled: true }
);
const enhancedPrompt = combinePrompts(
knowledgeResult.knowledgePrompt,
originalAdrPrompt
);
```
### Configuration Integration
```typescript
export interface ToolKnowledgeConfig {
enableKnowledgeGeneration: boolean;
domains: ArchitecturalDomain[];
knowledgeDepth: 'basic' | 'intermediate' | 'advanced';
cacheStrategy: 'aggressive' | 'moderate' | 'minimal';
}
```
## Usage Patterns
### Pattern 1: Domain-Aware ADR Suggestions
1. Detect project domains from codebase analysis
2. Generate domain-specific architectural knowledge
3. Enhance ADR suggestion prompts with knowledge
4. Cache knowledge for subsequent suggestions
### Pattern 2: Context-Enhanced Analysis
1. Analyze project structure and technologies
2. Generate relevant architectural knowledge
3. Combine knowledge with analysis prompts
4. Provide context-aware recommendations
### Pattern 3: Knowledge-Driven Research Questions
1. Identify knowledge gaps in project context
2. Generate domain expertise knowledge
3. Create research questions based on knowledge
4. Enhance research planning with domain insights
## Performance Considerations
### Optimization Strategies
1. **Lazy Loading**: Load domain templates only when needed
2. **Incremental Generation**: Generate knowledge incrementally
3. **Parallel Processing**: Generate knowledge for multiple domains in parallel
4. **Smart Caching**: Cache at multiple levels (domain, context, config)
### Resource Management
- **Memory Usage**: Limit concurrent knowledge generation
- **Cache Size**: Implement cache size limits and cleanup
- **Generation Time**: Set timeouts for knowledge generation
- **Quality vs Speed**: Balance knowledge depth with generation time
## Future Enhancements
### Planned Features
1. **Custom Domain Templates**: User-defined domain knowledge
2. **Knowledge Learning**: Learn from successful knowledge applications
3. **Knowledge Validation**: Validate knowledge against project outcomes
4. **Knowledge Sharing**: Share knowledge across projects and teams
### Integration Opportunities
1. **APE Integration**: Optimize knowledge generation prompts
2. **Reflexion Integration**: Learn from knowledge effectiveness
3. **External Sources**: Integrate with external knowledge bases
4. **Team Knowledge**: Incorporate team-specific knowledge
## Implementation Roadmap
### Phase 1: Core Framework (Week 1-2)
- Implement basic knowledge generation
- Create domain templates
- Integrate with existing prompt system
- Add basic caching
### Phase 2: Advanced Features (Week 3-4)
- Add security validation
- Implement configuration system
- Create tool integration utilities
- Add performance optimizations
### Phase 3: Enhancement & Testing (Week 5-6)
- Comprehensive testing
- Performance tuning
- Documentation completion
- Integration with priority tools
This framework design provides a solid foundation for implementing Knowledge Generation across the MCP ADR Analysis Server while maintaining architectural consistency and security requirements.