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Knowledge Base Maintenance & Relevance Strategy

Comprehensive strategy for maintaining, updating, and ensuring the long-term relevance of the Coditect knowledge base as it grows over time.

Table of Contents​

  1. Overview
  2. Automated Update Strategies
  3. Relevance Scoring & Decay
  4. Quality Control Mechanisms
  5. Archival & Pruning Strategy
  6. Version-Aware Context Management
  7. Continuous Learning Pipeline
  8. Metrics & Monitoring

Overview​

The knowledge base contains 115,000+ lines of development history and growing. Without proper maintenance, it risks becoming:

  • Stale: Outdated solutions that no longer apply
  • Noisy: Too much irrelevant information
  • Slow: Performance degradation with size
  • Misleading: Old patterns conflicting with new best practices

This document outlines strategies to keep the knowledge base fresh, relevant, and performant.

Automated Update Strategies​

1. Session Auto-Capture​

Automatically capture and index development sessions:

class SessionAutoCapture:
    def __init__(self):
        self.capture_triggers = {
            'session_end': self.capture_on_session_end,
            'milestone_reached': self.capture_on_milestone,
            'error_resolved': self.capture_on_resolution,
            'pattern_detected': self.capture_on_pattern
        }
        
    async def capture_on_session_end(self, session):
        """Capture when a development session ends"""
        if session.duration > timedelta(minutes=30):
            summary = await self.generate_session_summary(session)
            
            # Extract learnings
            learnings = {
                'problems_encountered': session.errors,
                'solutions_applied': session.solutions,
                'decisions_made': session.decisions,
                'code_created': session.code_artifacts
            }
            
            # Add to KB with session context
            await self.kb.add_session_learning(
                session_id=session.id,
                summary=summary,
                learnings=learnings,
                auto_captured=True
            )
    
    async def capture_on_resolution(self, error, solution):
        """Capture when errors are resolved"""
        # Verify solution actually worked
        if solution.verified and solution.tests_pass:
            await self.kb.add_learning({
                'problem': error.signature,
                'solution': solution.implementation,
                'category': 'debugging',
                'auto_captured': True,
                'verification': solution.verification_method
            })

2. Agent Learning Hooks​

Integrate learning capture into agent workflows:

class AgentLearningHook {
  constructor(private kb: KnowledgeBase) {
    this.setupHooks();
  }
  
  private setupHooks() {
    // Before action
    Agent.beforeAction(async (action) => {
      action.context = await this.kb.getRelevantContext(action);
    });
    
    // After action
    Agent.afterAction(async (action, result) => {
      if (result.success && result.learnings) {
        await this.capturelearning(action, result);
      }
    });
    
    // On error
    Agent.onError(async (error, context) => {
      await this.captureFailure(error, context);
    });
  }
  
  private async captureLearning(action: Action, result: Result) {
    const learning = {
      trigger: action.type,
      context: action.context,
      approach: action.implementation,
      outcome: result.outcome,
      metrics: result.metrics,
      reusable: this.assessReusability(action, result)
    };
    
    await this.kb.addAutomatedLearning(learning);
  }
}

3. Git Hook Integration​

Capture learnings from commit messages and PR descriptions:

#!/bin/bash
# .git/hooks/post-commit

# Extract commit message
COMMIT_MSG=$(git log -1 --pretty=%B)
COMMIT_HASH=$(git rev-parse HEAD)

# Parse for learning indicators
if [[ $COMMIT_MSG =~ "fix:" ]] || [[ $COMMIT_MSG =~ "solve:" ]]; then
    # Extract problem/solution
    python3 scripts/extract_learning_from_commit.py \
        --commit "$COMMIT_HASH" \
        --message "$COMMIT_MSG" \
        --auto-add-to-kb
fi

Relevance Scoring & Decay​

1. Multi-Factor Relevance Scoring​

class RelevanceScorer:
    def calculate_relevance(self, entry, query_context):
        """
        Multi-factor relevance scoring:
        - Temporal relevance (recency)
        - Version compatibility  
        - Solution success rate
        - Usage frequency
        - Community validation
        """
        
        # Base similarity score
        base_score = self.vector_similarity(entry.embedding, query_context.embedding)
        
        # Temporal decay (5% per day, configurable by category)
        days_old = (datetime.now() - entry.timestamp).days
        decay_rate = self.get_decay_rate(entry.category)
        temporal_factor = (1 - decay_rate) ** days_old
        
        # Version compatibility
        version_factor = self.calculate_version_compatibility(
            entry.version,
            query_context.current_version
        )
        
        # Success rate (how often this solution worked)
        success_factor = entry.success_count / (entry.usage_count + 1)
        
        # Usage frequency (popular solutions score higher)
        usage_factor = min(1.0, entry.usage_count / 100)
        
        # Community validation (upvotes, confirmations)
        validation_factor = self.calculate_validation_score(entry)
        
        # Weighted combination
        final_score = (
            base_score * 0.3 +
            temporal_factor * 0.2 +
            version_factor * 0.2 +
            success_factor * 0.15 +
            usage_factor * 0.1 +
            validation_factor * 0.05
        )
        
        return {
            'score': final_score,
            'factors': {
                'base': base_score,
                'temporal': temporal_factor,
                'version': version_factor,
                'success': success_factor,
                'usage': usage_factor,
                'validation': validation_factor
            }
        }
    
    def get_decay_rate(self, category):
        """Different decay rates for different categories"""
        decay_rates = {
            'security': 0.02,      # Security issues decay slowly
            'debugging': 0.05,     # Standard decay
            'implementation': 0.07, # Implementation patterns decay faster
            'ui_patterns': 0.10,   # UI trends decay quickly
            'architecture': 0.03   # Architecture decisions decay slowly
        }
        return decay_rates.get(category, 0.05)

2. Dynamic Relevance Adjustment​

class DynamicRelevanceAdjuster:
    async def adjust_relevance(self):
        """Periodically adjust relevance based on usage patterns"""
        
        # Track which entries are actually helpful
        usage_stats = await self.kb.get_usage_statistics()
        
        for entry_id, stats in usage_stats.items():
            entry = await self.kb.get_entry(entry_id)
            
            # Boost entries that are frequently helpful
            if stats.helpful_count > stats.not_helpful_count:
                boost = stats.helpful_count / (stats.total_usage + 1)
                entry.relevance_boost = min(1.5, 1 + boost)
            else:
                # Demote entries that aren't helpful
                entry.relevance_boost = 0.8
            
            # Update version-specific relevance
            if self.is_version_deprecated(entry.version):
                entry.version_penalty = 0.5
            
            await self.kb.update_entry(entry)

Quality Control Mechanisms​

1. Automated Validation​

class QualityValidator:
    def __init__(self):
        self.validators = {
            'code': CodeValidator(),
            'solution': SolutionValidator(),
            'pattern': PatternValidator()
        }
    
    async def validate_entry(self, entry):
        """Validate entry before adding to KB"""
        
        # 1. Check for duplicates
        if await self.is_duplicate(entry):
            return ValidationResult(
                valid=False,
                reason="Duplicate entry detected"
            )
        
        # 2. Validate content quality
        content_validation = self.validate_content_quality(entry)
        if not content_validation.passed:
            return content_validation
        
        # 3. Verify solution if applicable
        if entry.has_solution:
            solution_validation = await self.validators['solution'].validate(
                entry.solution
            )
            if not solution_validation.passed:
                return solution_validation
        
        # 4. Check code examples
        if entry.code_example:
            code_validation = await self.validators['code'].validate(
                entry.code_example
            )
            if not code_validation.passed:
                return code_validation
        
        return ValidationResult(valid=True)
    
    def validate_content_quality(self, entry):
        """Ensure content meets quality standards"""
        
        # Minimum content length
        if len(entry.content) < 50:
            return ValidationResult(False, "Content too short")
        
        # Must have clear problem statement
        if not self.has_clear_problem_statement(entry):
            return ValidationResult(False, "Unclear problem statement")
        
        # Must have actionable solution
        if entry.has_solution and not self.is_solution_actionable(entry.solution):
            return ValidationResult(False, "Solution not actionable")
        
        # No sensitive information
        if self.contains_sensitive_info(entry):
            return ValidationResult(False, "Contains sensitive information")
        
        return ValidationResult(True)

2. Peer Review System​

class PeerReviewSystem:
    async def submit_for_review(self, entry, author_agent):
        """Submit high-impact entries for peer review"""
        
        # Determine if review needed
        if self.requires_review(entry):
            review_request = {
                'entry': entry,
                'author': author_agent,
                'reviewers': self.select_reviewers(entry),
                'deadline': datetime.now() + timedelta(hours=24)
            }
            
            # Send to reviewers
            reviews = await self.collect_reviews(review_request)
            
            # Aggregate feedback
            consensus = self.build_consensus(reviews)
            
            if consensus.approved:
                entry.peer_reviewed = True
                entry.quality_score = consensus.quality_score
                return await self.kb.add_reviewed_entry(entry)
            else:
                return self.request_revisions(entry, consensus.feedback)
    
    def requires_review(self, entry):
        """Determine if entry needs peer review"""
        return (
            entry.category in ['architecture', 'security'] or
            entry.complexity > 4 or
            entry.impacts_core_system or
            'breaking_change' in entry.tags
        )

Archival & Pruning Strategy​

1. Intelligent Archiving​

class IntelligentArchiver:
    def __init__(self):
        self.archive_policies = {
            'age_based': self.archive_by_age,
            'relevance_based': self.archive_by_relevance,
            'version_based': self.archive_by_version,
            'usage_based': self.archive_by_usage
        }
    
    async def run_archival_process(self):
        """Run periodic archival process"""
        
        candidates = await self.identify_archive_candidates()
        
        for entry in candidates:
            # Check if entry should be preserved
            if self.should_preserve(entry):
                await self.mark_as_historical_reference(entry)
                continue
            
            # Determine archival tier
            tier = self.determine_archive_tier(entry)
            
            if tier == 'cold':
                # Move to cold storage (compressed, slow access)
                await self.move_to_cold_storage(entry)
            elif tier == 'warm':
                # Keep accessible but deprioritized
                await self.move_to_warm_storage(entry)
            elif tier == 'delete':
                # Safe to remove
                await self.safe_delete(entry)
    
    def determine_archive_tier(self, entry):
        """Determine appropriate archival tier"""
        
        # Never delete security or architecture decisions
        if entry.category in ['security', 'architecture']:
            return 'warm'
        
        # Cold storage for old but occasionally useful
        if entry.age_days > 180 and entry.recent_usage_count > 0:
            return 'cold'
        
        # Delete if old, unused, and low relevance
        if (entry.age_days > 365 and 
            entry.recent_usage_count == 0 and 
            entry.relevance_score < 0.3):
            return 'delete'
        
        return 'warm'

2. Smart Consolidation​

class SmartConsolidator:
    async def consolidate_patterns(self):
        """Consolidate similar patterns into best practices"""
        
        # Find clusters of similar solutions
        clusters = await self.find_solution_clusters()
        
        for cluster in clusters:
            # Analyze effectiveness of each variant
            effectiveness = self.analyze_cluster_effectiveness(cluster)
            
            # Create consolidated best practice
            best_practice = self.synthesize_best_practice(
                cluster,
                effectiveness
            )
            
            # Add new consolidated entry
            await self.kb.add_best_practice(best_practice)
            
            # Archive individual entries
            for entry in cluster.entries:
                entry.superseded_by = best_practice.id
                await self.archive_entry(entry)
    
    def synthesize_best_practice(self, cluster, effectiveness):
        """Create consolidated best practice from cluster"""
        
        return {
            'title': f"Best Practice: {cluster.common_problem}",
            'problem': cluster.common_problem,
            'solution': self.merge_effective_solutions(
                cluster.solutions,
                effectiveness
            ),
            'variants': self.document_variants(cluster),
            'evidence': {
                'success_rate': effectiveness.average_success_rate,
                'usage_count': sum(e.usage_count for e in cluster.entries),
                'consolidated_from': [e.id for e in cluster.entries]
            },
            'category': 'best_practice',
            'auto_generated': True
        }

Version-Aware Context Management​

1. Version Compatibility Tracking​

class VersionCompatibilityTracker:
    def __init__(self):
        self.version_map = self.build_version_compatibility_map()
    
    async def tag_version_compatibility(self, entry):
        """Tag entries with version compatibility info"""
        
        # Extract version indicators
        versions = self.extract_version_info(entry)
        
        # Test compatibility if possible
        if entry.has_code:
            compatibility = await self.test_compatibility(
                entry.code_example,
                versions
            )
        else:
            compatibility = self.infer_compatibility(entry, versions)
        
        entry.compatibility = {
            'verified_versions': compatibility.verified,
            'likely_compatible': compatibility.likely,
            'incompatible': compatibility.incompatible,
            'breaking_changes': compatibility.breaking_changes
        }
        
        return entry
    
    def calculate_version_relevance(self, entry, current_version):
        """Calculate relevance based on version compatibility"""
        
        if current_version in entry.compatibility['verified_versions']:
            return 1.0
        elif current_version in entry.compatibility['likely_compatible']:
            return 0.8
        elif self.is_forward_compatible(entry, current_version):
            return 0.6
        else:
            # Apply penalty for version mismatch
            version_distance = self.calculate_version_distance(
                entry.latest_compatible_version,
                current_version
            )
            return max(0.1, 1.0 - (version_distance * 0.1))

2. Migration Path Documentation​

class MigrationPathDocumenter:
    async def document_migration_paths(self):
        """Document how solutions evolve across versions"""
        
        # Find solution evolution patterns
        evolution_chains = await self.find_solution_evolution_chains()
        
        for chain in evolution_chains:
            migration_guide = {
                'problem': chain.common_problem,
                'evolution': []
            }
            
            # Document each version's approach
            for version, solution in chain.version_solutions.items():
                migration_guide['evolution'].append({
                    'version': version,
                    'solution': solution,
                    'migration_from_previous': self.extract_migration_steps(
                        chain.get_previous_version(version),
                        version
                    ),
                    'breaking_changes': self.identify_breaking_changes(
                        chain.get_previous_version(version),
                        version
                    )
                })
            
            await self.kb.add_migration_guide(migration_guide)

Continuous Learning Pipeline​

1. Feedback Loop Integration​

class FeedbackLoopIntegrator:
    def __init__(self):
        self.feedback_channels = {
            'explicit': self.handle_explicit_feedback,
            'implicit': self.handle_implicit_feedback,
            'performance': self.handle_performance_feedback
        }
    
    async def process_feedback(self, feedback):
        """Process various types of feedback"""
        
        handler = self.feedback_channels[feedback.type]
        await handler(feedback)
        
        # Update entry metadata
        entry = await self.kb.get_entry(feedback.entry_id)
        entry.feedback_score = self.calculate_feedback_score(entry)
        
        # Trigger re-evaluation if score drops
        if entry.feedback_score < 0.5:
            await self.trigger_entry_review(entry)
    
    async def handle_implicit_feedback(self, feedback):
        """Learn from usage patterns"""
        
        # Track solution application
        if feedback.action == 'solution_applied':
            await self.track_solution_application(feedback)
        
        # Track search refinements
        elif feedback.action == 'search_refined':
            await self.learn_search_patterns(feedback)
        
        # Track abandonment
        elif feedback.action == 'result_abandoned':
            await self.track_abandonment(feedback)

2. Pattern Evolution Tracking​

class PatternEvolutionTracker:
    async def track_pattern_evolution(self):
        """Track how patterns evolve over time"""
        
        patterns = await self.kb.get_all_patterns()
        
        for pattern in patterns:
            # Find variations of this pattern
            variations = await self.find_pattern_variations(pattern)
            
            # Analyze evolution
            evolution = self.analyze_pattern_evolution(pattern, variations)
            
            # Update pattern metadata
            pattern.evolution_stage = evolution.stage
            pattern.trending_direction = evolution.direction
            pattern.superseding_patterns = evolution.superseding
            
            # Alert on significant changes
            if evolution.is_significant_change:
                await self.alert_pattern_change(pattern, evolution)

Metrics & Monitoring​

1. Knowledge Base Health Metrics​

class KnowledgeBaseHealthMonitor:
    def calculate_health_metrics(self):
        """Calculate overall KB health metrics"""
        
        metrics = {
            # Coverage metrics
            'problem_coverage': self.calculate_problem_coverage(),
            'solution_success_rate': self.calculate_solution_success_rate(),
            'knowledge_gaps': self.identify_knowledge_gaps(),
            
            # Quality metrics
            'average_relevance_score': self.calculate_avg_relevance(),
            'peer_review_rate': self.calculate_review_rate(),
            'validation_pass_rate': self.calculate_validation_rate(),
            
            # Usage metrics
            'query_success_rate': self.calculate_query_success(),
            'avg_results_usefulness': self.calculate_usefulness(),
            'knowledge_reuse_rate': self.calculate_reuse_rate(),
            
            # Maintenance metrics
            'stale_entry_percentage': self.calculate_staleness(),
            'archive_efficiency': self.calculate_archive_efficiency(),
            'update_frequency': self.calculate_update_frequency()
        }
        
        metrics['overall_health'] = self.calculate_overall_health(metrics)
        
        return metrics

2. Performance Monitoring​

class PerformanceMonitor:
    async def monitor_performance(self):
        """Monitor KB performance metrics"""
        
        performance = {
            # Query performance
            'avg_query_time': await self.measure_query_performance(),
            'p95_query_time': await self.measure_query_p95(),
            'cache_hit_rate': await self.calculate_cache_hit_rate(),
            
            # Storage efficiency
            'storage_size': await self.measure_storage_size(),
            'compression_ratio': await self.calculate_compression_ratio(),
            'index_efficiency': await self.measure_index_efficiency(),
            
            # Growth metrics
            'growth_rate': await self.calculate_growth_rate(),
            'entry_velocity': await self.calculate_entry_velocity(),
            'quality_trend': await self.calculate_quality_trend()
        }
        
        # Alert on degradation
        if performance['avg_query_time'] > self.query_time_threshold:
            await self.alert_performance_degradation(performance)
        
        return performance

3. Automated Maintenance Scheduling​

class MaintenanceScheduler:
    def __init__(self):
        self.maintenance_tasks = {
            'hourly': ['cache_refresh', 'usage_tracking'],
            'daily': ['relevance_adjustment', 'feedback_processing'],
            'weekly': ['pattern_consolidation', 'quality_review'],
            'monthly': ['archival_process', 'version_compatibility_check'],
            'quarterly': ['major_consolidation', 'schema_optimization']
        }
    
    async def run_scheduled_maintenance(self):
        """Run maintenance tasks based on schedule"""
        
        current_time = datetime.now()
        
        for frequency, tasks in self.maintenance_tasks.items():
            if self.should_run(frequency, current_time):
                for task in tasks:
                    try:
                        await self.run_maintenance_task(task)
                        await self.log_maintenance_completion(task)
                    except Exception as e:
                        await self.handle_maintenance_failure(task, e)

Implementation Roadmap​

Phase 1: Foundation (Weeks 1-2)​

  • Implement relevance scoring system
  • Set up automated capture hooks
  • Create quality validation framework

Phase 2: Automation (Weeks 3-4)​

  • Deploy feedback loop integration
  • Implement pattern recognition
  • Set up monitoring dashboards

Phase 3: Intelligence (Weeks 5-6)​

  • Deploy smart consolidation
  • Implement predictive relevance
  • Create maintenance scheduler

Phase 4: Optimization (Weeks 7-8)​

  • Performance tuning
  • Storage optimization
  • Advanced archival strategies

Conclusion​

Maintaining knowledge base relevance requires:

  1. Automated Capture: Continuously learn from development activities
  2. Smart Scoring: Multi-factor relevance that adapts over time
  3. Quality Control: Validate and review high-impact knowledge
  4. Intelligent Archival: Keep KB size manageable without losing value
  5. Version Awareness: Track compatibility across system evolution
  6. Continuous Learning: Improve through feedback loops
  7. Active Monitoring: Track health and performance metrics

This strategy ensures the knowledge base remains a valuable, performant resource that grows smarter over time rather than just larger.