From 37edc1549e0acfc18863320beb9b92dc7989b1fd Mon Sep 17 00:00:00 2001 From: overcuriousity Date: Sat, 2 Aug 2025 11:54:07 +0200 Subject: [PATCH] RAG Roadmap --- RAG-Roadmap.md | 358 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 358 insertions(+) create mode 100644 RAG-Roadmap.md diff --git a/RAG-Roadmap.md b/RAG-Roadmap.md new file mode 100644 index 0000000..787f7b0 --- /dev/null +++ b/RAG-Roadmap.md @@ -0,0 +1,358 @@ +# 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; + } + + 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; +} +``` + +### 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 \ No newline at end of file