RAG Roadmap

RAG-Roadmap.md

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+# Forensic-Grade RAG Implementation Roadmap
+
+## Context & Current State Analysis
+
+You have access to a forensic tools recommendation system built with:
+- **Embeddings-based retrieval** (src/utils/embeddings.ts)
+- **Multi-stage AI pipeline** (src/utils/aiPipeline.ts) 
+- **Micro-task processing** for detailed analysis
+- **Rate limiting and queue management** (src/utils/rateLimitedQueue.ts)
+- **YAML-based tool database** (src/data/tools.yaml)
+
+**Current Architecture**: Basic RAG (Retrieve → AI Selection → Micro-task Generation)
+
+**Target Architecture**: Forensic-Grade RAG with transparency, objectivity, and reproducibility
+
+## Implementation Roadmap
+
+### PHASE 1: Configuration Externalization & AI Architecture Enhancement (Weeks 1-2)
+
+#### 1.1 Complete Configuration Externalization
+**Objective**: Remove all hard-coded values from codebase (except AI prompts)
+
+**Tasks**:
+1. **Create comprehensive configuration schema** in `src/config/`
+   - `forensic-scoring.yaml` - All scoring criteria, weights, thresholds
+   - `ai-models.yaml` - AI model configurations and routing
+   - `system-parameters.yaml` - Rate limits, queue settings, processing parameters
+   - `validation-criteria.yaml` - Expert validation rules, bias detection parameters
+
+2. **Implement configuration loader** (`src/utils/configLoader.ts`)
+   - Hot-reload capability for configuration changes
+   - Environment-specific overrides (dev/staging/prod)
+   - Configuration validation and schema enforcement
+   - Default fallbacks for missing values
+
+3. **Audit existing codebase** for hard-coded values:
+   - Search for literal numbers, strings, arrays in TypeScript files
+   - Extract to configuration files with meaningful names
+   - Ensure all thresholds (similarity scores, rate limits, token counts) are configurable
+
+#### 1.2 Dual AI Model Architecture Implementation
+**Objective**: Implement large + small model strategy for optimal cost/performance
+
+**Tasks**:
+1. **Extend environment configuration**:
+   ```
+   # Strategic Analysis Model (Large, Few Tokens)
+   AI_STRATEGIC_ENDPOINT=
+   AI_STRATEGIC_API_KEY=
+   AI_STRATEGIC_MODEL=mistral-large-latest
+   AI_STRATEGIC_MAX_TOKENS=500
+   AI_STRATEGIC_CONTEXT_WINDOW=32000
+   
+   # Content Generation Model (Small, Many Tokens)  
+   AI_CONTENT_ENDPOINT=
+   AI_CONTENT_API_KEY=
+   AI_CONTENT_MODEL=mistral-small-latest
+   AI_CONTENT_MAX_TOKENS=2000
+   AI_CONTENT_CONTEXT_WINDOW=8000
+   ```
+
+2. **Create AI router** (`src/utils/aiRouter.ts`):
+   - Route different task types to appropriate models
+   - **Strategic tasks** → Large model: tool selection, bias analysis, methodology decisions
+   - **Content tasks** → Small model: descriptions, explanations, micro-task outputs
+   - Automatic fallback logic if primary model fails
+   - Usage tracking and cost optimization
+
+3. **Update aiPipeline.ts**:
+   - Replace single `callAI()` method with task-specific methods
+   - Implement intelligent routing based on task complexity
+   - Add token estimation for optimal model selection
+
+### PHASE 2: Evidence-Based Scoring Framework (Weeks 3-5)
+
+#### 2.1 Forensic Scoring Engine Implementation
+**Objective**: Replace subjective AI selection with objective, measurable criteria
+
+**Tasks**:
+1. **Create scoring framework** (`src/scoring/ForensicScorer.ts`):
+   ```typescript
+   interface ScoringCriterion {
+     name: string;
+     weight: number;
+     methodology: string;
+     dataSources: string[];
+     calculator: (tool: Tool, scenario: Scenario) => Promise<CriterionScore>;
+   }
+   
+   interface CriterionScore {
+     value: number;           // 0-100
+     confidence: number;      // 0-100  
+     evidence: Evidence[];
+     lastUpdated: Date;
+   }
+   ```
+
+2. **Implement core scoring criteria**:
+   - **Court Admissibility Scorer**: Based on legal precedent database
+   - **Scientific Validity Scorer**: Based on peer-reviewed research citations
+   - **Methodology Alignment Scorer**: NIST SP 800-86 compliance assessment
+   - **Expert Consensus Scorer**: Practitioner survey data integration
+   - **Error Rate Scorer**: Known false positive/negative rates
+
+3. **Build evidence provenance system**:
+   - Track source of every score component
+   - Maintain citation database for all claims
+   - Version control for scoring methodologies
+   - Automatic staleness detection for outdated evidence
+
+#### 2.2 Deterministic Core Implementation  
+**Objective**: Ensure reproducible results for identical inputs
+
+**Tasks**:
+1. **Implement deterministic pipeline** (`src/analysis/DeterministicAnalyzer.ts`):
+   - Rule-based scenario classification (SCADA/Mobile/Network/etc.)
+   - Mathematical scoring combination (weighted averages, not AI decisions)
+   - Consistent tool ranking algorithms
+   - Reproducibility validation tests
+
+2. **Add AI enhancement layer**:
+   - AI provides explanations, NOT decisions
+   - AI generates workflow descriptions based on deterministic selections
+   - AI creates contextual advice around objective tool choices
+
+### PHASE 3: Transparency & Audit Trail System (Weeks 4-6)
+
+#### 3.1 Complete Audit Trail Implementation
+**Objective**: Track every decision with forensic-grade documentation
+
+**Tasks**:
+1. **Create audit framework** (`src/audit/AuditTrail.ts`):
+   ```typescript
+   interface ForensicAuditTrail {
+     queryId: string;
+     userQuery: string;
+     processingSteps: AuditStep[];
+     finalRecommendation: RecommendationWithEvidence;
+     reproducibilityHash: string;
+     validationStatus: ValidationStatus;
+   }
+   
+   interface AuditStep {
+     stepName: string;
+     input: any;
+     methodology: string;
+     output: any;
+     evidence: Evidence[];
+     confidence: number;
+     processingTime: number;
+     modelUsed?: string;
+   }
+   ```
+
+2. **Implement evidence citation system**:
+   - Automatic citation generation for all claims
+   - Link to source standards (NIST, ISO, RFC)
+   - Reference scientific papers for methodology choices
+   - Track expert validation contributors
+
+3. **Build explanation generator**:
+   - Human-readable reasoning for every recommendation
+   - "Why this tool" and "Why not alternatives" explanations
+   - Confidence level communication
+   - Uncertainty quantification
+
+#### 3.2 Bias Detection & Mitigation System
+**Objective**: Actively detect and correct recommendation biases
+
+**Tasks**:
+1. **Implement bias detection** (`src/bias/BiasDetector.ts`):
+   - **Popularity bias**: Over-recommendation of well-known tools
+   - **Availability bias**: Preference for easily accessible tools
+   - **Recency bias**: Over-weighting of newest tools
+   - **Cultural bias**: Platform or methodology preferences
+
+2. **Create mitigation strategies**:
+   - Automatic bias adjustment algorithms
+   - Diversity requirements for recommendations
+   - Fairness metrics across tool categories
+   - Bias reporting in audit trails
+
+### PHASE 4: Expert Validation & Learning System (Weeks 6-8)
+
+#### 4.1 Expert Review Integration
+**Objective**: Enable forensic experts to validate and improve recommendations
+
+**Tasks**:
+1. **Build expert validation interface** (`src/validation/ExpertReview.ts`):
+   - Structured feedback collection from forensic practitioners
+   - Agreement/disagreement tracking with detailed reasoning
+   - Expert consensus building over time
+   - Minority opinion preservation
+
+2. **Implement validation loop**:
+   - Flag recommendations requiring expert review
+   - Track expert validation rates and patterns
+   - Update scoring based on real-world feedback
+   - Methodology improvement based on expert input
+
+#### 4.2 Real-World Case Learning
+**Objective**: Learn from actual forensic investigations
+
+**Tasks**:
+1. **Create case study integration** (`src/learning/CaseStudyLearner.ts`):
+   - Anonymous case outcome tracking
+   - Tool effectiveness measurement in real scenarios
+   - Methodology success/failure analysis
+   - Continuous improvement based on field results
+
+2. **Implement feedback loops**:
+   - Post-case recommendation validation
+   - Tool performance tracking in actual investigations
+   - Methodology refinement based on outcomes
+   - Success rate improvement over time
+
+### PHASE 5: Advanced Features & Scientific Rigor (Weeks 7-10)
+
+#### 5.1 Confidence & Uncertainty Quantification
+**Objective**: Provide scientific confidence levels for all recommendations
+
+**Tasks**:
+1. **Implement uncertainty quantification** (`src/uncertainty/ConfidenceCalculator.ts`):
+   - Statistical confidence intervals for scores
+   - Uncertainty propagation through scoring pipeline
+   - Risk assessment for recommendation reliability
+   - Alternative recommendation ranking
+
+2. **Add fallback recommendation system**:
+   - Multiple ranked alternatives for each recommendation
+   - Contingency planning for tool failures
+   - Risk-based recommendation portfolios
+   - Sensitivity analysis for critical decisions
+
+#### 5.2 Reproducibility Testing Framework
+**Objective**: Ensure consistent results across time and implementations
+
+**Tasks**:
+1. **Build reproducibility testing** (`src/testing/ReproducibilityTester.ts`):
+   - Automated consistency validation
+   - Inter-rater reliability testing
+   - Cross-temporal stability analysis
+   - Version control for methodology changes
+
+2. **Implement quality assurance**:
+   - Continuous integration for reproducibility
+   - Regression testing for methodology changes
+   - Performance monitoring for consistency
+   - Alert system for unexpected variations
+
+### PHASE 6: Integration & Production Readiness (Weeks 9-12)
+
+#### 6.1 System Integration
+**Objective**: Integrate all forensic-grade components seamlessly
+
+**Tasks**:
+1. **Update existing components**:
+   - Modify `aiPipeline.ts` to use new scoring framework
+   - Update `embeddings.ts` with evidence tracking
+   - Enhance `rateLimitedQueue.ts` with audit capabilities
+   - Refactor `query.ts` API to return audit trails
+
+2. **Performance optimization**:
+   - Caching strategies for expensive evidence lookups
+   - Parallel processing for scoring criteria
+   - Efficient storage for audit trails
+   - Load balancing for dual AI models
+
+#### 6.2 Production Features
+**Objective**: Make system ready for professional forensic use
+
+**Tasks**:
+1. **Add professional features**:
+   - Export recommendations to forensic report formats
+   - Integration with existing forensic workflows
+   - Batch processing for multiple scenarios
+   - API endpoints for external tool integration
+
+2. **Implement monitoring & maintenance**:
+   - Health checks for all system components
+   - Performance monitoring for response times
+   - Error tracking and alerting
+   - Automatic system updates for new evidence
+
+## Technical Implementation Guidelines
+
+### Configuration Management
+- Use YAML files for human-readable configuration
+- Implement JSON Schema validation for all config files
+- Support environment variable overrides
+- Hot-reload for development, restart for production changes
+
+### AI Model Routing Strategy
+```typescript
+// Task Classification for Model Selection
+const AI_TASK_ROUTING = {
+  strategic: ['tool-selection', 'bias-analysis', 'methodology-decisions'],
+  content: ['descriptions', 'explanations', 'micro-tasks', 'workflows']
+};
+
+// Cost Optimization Logic
+if (taskComplexity === 'high' && responseTokens < 500) {
+  useModel = 'large';
+} else if (taskComplexity === 'low' && responseTokens > 1000) {
+  useModel = 'small';
+} else {
+  useModel = config.defaultModel;
+}
+```
+
+### Evidence Database Structure
+```typescript
+interface EvidenceSource {
+  type: 'standard' | 'paper' | 'case-law' | 'expert-survey';
+  citation: string;
+  reliability: number;
+  lastValidated: Date;
+  content: string;
+  metadata: Record<string, any>;
+}
+```
+
+### Quality Assurance Requirements
+- All scoring criteria must have documented methodologies
+- Every recommendation must include confidence levels
+- All AI-generated content must be marked as such
+- Reproducibility tests must pass with >95% consistency
+- Expert validation rate must exceed 80% for production use
+
+## Success Metrics
+
+### Forensic Quality Metrics
+- **Transparency**: 100% of decisions traceable to evidence
+- **Objectivity**: <5% variance in scoring between runs
+- **Reproducibility**: >95% identical results for identical inputs
+- **Expert Agreement**: >80% expert validation rate
+- **Bias Reduction**: <10% bias score across all categories
+
+### Performance Metrics  
+- **Response Time**: <30 seconds for workflow recommendations
+- **Accuracy**: >90% real-world case validation success
+- **Coverage**: Support for >95% of common forensic scenarios
+- **Reliability**: <1% system error rate
+- **Cost Efficiency**: <50% cost reduction vs. single large model
+
+## Risk Mitigation
+
+### Technical Risks
+- **AI Model Failures**: Implement robust fallback mechanisms
+- **Configuration Errors**: Comprehensive validation and testing
+- **Performance Issues**: Load testing and optimization
+- **Data Corruption**: Backup and recovery procedures
+
+### Forensic Risks
+- **Bias Introduction**: Continuous monitoring and expert validation
+- **Methodology Errors**: Peer review and scientific validation
+- **Legal Challenges**: Ensure compliance with admissibility standards
+- **Expert Disagreement**: Transparent uncertainty communication