End-to-End Architectural Walkthrough: From Query to Response

Abstract: This document provides a comprehensive technical walkthrough of how Mnemoverse processes a user query through its 8-layer architecture. The core flow traverses L7→L1→L7, while L8 (Evaluation) observes and influences quality via metrics and feedback. We trace a concrete example from user intent to final response, demonstrating orchestration mechanisms, data transformations, and decisions at each layer — using a pragmatic, today-available stack (vector + graph + rerank, strict contracts, tracing, policies) without speculative geometry models.

Table of Contents

1. Executive Summary
2. Walkthrough Scenario
3. Layer-by-Layer Analysis
4. Data Flow Choreography
5. Critical Design Decisions
6. Performance Characteristics
7. Error Handling & Resilience
8. Future Extensions

---

Executive Summary

Mnemoverse implements a hierarchical cognitive architecture where each layer (L1–L8) serves a distinct function, analogous to how human memory and reasoning systems operate at different levels of abstraction. The architecture follows the principle of index‑first retrieval — information is organized in combined indexes (vector + graph + metadata) with clear contracts, policies, and observability.

Key Architectural Principles

1. Index‑First Retrieval: Knowledge is organized in combined indexes (vector + graph + metadata)
2. Adaptive Context Scaling (ACS): Dynamic level-of-detail based on cognitive budgets
3. Experience Accumulation: Learning from interaction patterns across time
4. Multi-method Integration: Vector + graph orchestration with rerankers as a unified abstraction

---

Walkthrough Scenario

Context: A software developer working on a React application encounters an authentication bug where users get trapped in a 2FA verification loop on mobile devices.

User Query: "I need help debugging a 2FA authentication loop issue on mobile in React. Users report getting stuck in verification cycles."

Expected Outcome: The system should provide contextually relevant debugging guidance, drawing from both global knowledge (L1) and project-specific experience (L2), while learning from this interaction for future queries.

---

Layer-by-Layer Analysis

L7: Interface Layer → Query Ingestion

Location: docs/architecture/adapters/

The user submits their query through VS Code extension powered by MCP (Model Context Protocol) adapter.

{
  "timestamp": "2025-09-05T14:30:00Z",
  "user_id": "dev-user-123",
  "session_id": "session-abc",
  "query": "I need help debugging a 2FA authentication loop issue on mobile in React",
  "context": {
    "current_file": "src/auth/TwoFactorAuth.tsx",
    "project_type": "react-typescript",
    "recent_errors": ["TOTP_VERIFICATION_FAILED", "AUTH_LOOP_DETECTED"]
  }
}

Key Operations:

• Query normalization and intent extraction
• Context enrichment from IDE state
• Security and privacy filtering
• Protocol translation (MCP → internal format)

---

L6: Agent Runtime → Intent Formulation

Location: docs/architecture/orchestration/ceo/

The CEO (Context/Execution Orchestrator) receives the raw query and formulates a structured intent with resource budgets.

{
  "version": "v0",
  "id": "c9b1a0f8-6d8d-4c3b-9a7c-2b2e4d7f9123",
  "intent": {
    "primary": "debug_authentication_issue",
    "domain": "react_mobile_2fa",
    "urgency": "medium",
    "specificity": "implementation_focused"
  },
  "budgets": {
    "tokens_max": 8000,
    "time_ms": 1200,
    "cognitive_effort": "detailed"
  },
  "risk_profile": {
    "level": "medium",
    "areas": ["security", "user_experience"]
  },
  "privacy_mode": "allow_technical_details",
  "request_id": "req-20250905-debug-2fa"
}

Key Operations:

• Intent classification using semantic analysis
• Resource budget estimation based on query complexity
• Risk assessment for security-sensitive topics
• Correlation ID generation for observability

---

L5: Memory Layer → Context Assembly Strategy

Location: docs/architecture/memory-layer/

The Memory Layer receives the structured intent and determines the optimal context assembly strategy using ACS (Adaptive Context Scaling).

{
  "assembly_strategy": {
    "primary_sources": ["L2_project_library", "L4_experience_trails"],
    "fallback_sources": ["L1_global_knowledge"],
    "lod_profile": {
      "macro": 20,
      "micro": 60,
      "atomic": 20
    },
  "search_methods": ["vector_similarity", "graph_traversal", "reranking"]
  },
  "context_policies": {
    "max_fragments": 15,
    "relevance_threshold": 0.7,
    "freshness_weight": 0.3,
    "diversity_factor": 0.15
  }
}

Key Operations:

• Level-of-detail (LOD) optimization based on cognitive budgets
• Multi-source prioritization (L2 → L4 → L1)
• Search method selection based on query characteristics
• Context policy enforcement for quality control

---

L4: Experience Layer → Historical Pattern Matching

Location: docs/architecture/experience-layer/

The Experience Layer searches for similar debugging sessions and successful resolution patterns.

{
  "experience_trails": [
    {
      "trail_id": "exp-2fa-mobile-001",
      "similarity": 0.89,
      "task_context": "React mobile auth debugging",
      "nodes_used": ["react-auth-patterns", "2fa-troubleshooting", "mobile-specific-issues"],
      "resolution_outcome": "successful",
      "validation_score": 0.92,
      "key_insights": [
        "Mobile Safari cookie handling differences",
        "TOTP time synchronization issues",
        "React state management in auth flows"
      ]
    }
  ],
  "pattern_clusters": {
    "authentication_loops": {
      "frequency": 23,
      "success_rate": 0.78,
      "common_solutions": ["state_reset", "cookie_clearing", "token_refresh"]
    }
  }
}

Key Operations:

• Semantic search over historical debugging sessions
• Pattern recognition in successful resolution strategies
• Validation score weighting based on outcome quality
• Insight extraction from clustered experiences

---

L3: Workshop Layer → Tools & Validation

Location: docs/architecture/workshop/

The integration layer for tools (linters, validators, rerankers, query reformulators) and pre-assembly checks.

{
  "validators": [
    { "name": "pii_detector", "enabled": true },
    { "name": "security_terms_filter", "enabled": true },
    { "name": "format_normalizer", "enabled": true }
  ],
  "rerankers": [
    { "name": "cross_encoder_rerank", "top_k": 10 }
  ],
  "tools": [
    { "name": "code_search", "scope": "project" },
    { "name": "log_fetcher", "scope": "session" }
  ]
}

Key Operations:

• Pre-validate incoming fragments (PII/security/format)
• Rerank context (relevance + diversity) before L5 assembly
• Enrich with on-demand tool signals (code/logs) requested by L5

Contracts (L3)

• Inputs: candidate fragments from L4/L2/L1; tool requests from L5; config for validators/rerankers
• Processing: PII/security scan, format normalization, cross-encoder rerank, optional tool enrich
• Outputs: sanitized+rereanked fragment set with audit trail
• SLO: p95 validation+rereank < 30 ms; zero sensitive content leakage; 100% determinism for same inputs
• Edge cases: empty fragment set, oversized payloads, validator timeouts, tool fetch failures
• Resilience & Observability: fallback to minimal validators; emit traces (validator_count, blocked_items, rerank_ms)
• Privacy & Security: block PII, enforce policy gates, redact inline secrets; never persist sensitive payloads

---

L2: Project Library → Contextual Knowledge Retrieval

Location: docs/architecture/project-library/

The Project Library provides project-specific documentation, code patterns, and curated references relevant to React 2FA implementation.

{
  "project_fragments": [
    {
      "fragment_id": "proj-react-auth-001",
      "type": "documentation",
      "title": "2FA Implementation Guide",
      "content": "Mobile-specific considerations for TOTP verification...",
      "relevance": 0.94,
      "freshness": "2025-08-15",
      "entities": ["2FA", "mobile", "React", "TOTP"],
      "linked_code": ["src/auth/TwoFactorAuth.tsx", "src/utils/totpValidator.ts"]
    },
    {
      "fragment_id": "proj-react-patterns-002",
      "type": "code_pattern",
      "pattern": "Authentication state management",
      "implementation": "useAuth hook with mobile detection",
      "relevance": 0.87,
      "usage_frequency": 0.65
    }
  ],
  "graph_connections": {
    "2FA_implementation": ["mobile_considerations", "state_management", "error_handling"],
    "debugging_strategies": ["logging", "state_inspection", "user_agent_detection"]
  }
}

Key Operations:

• Project-scoped semantic search with high relevance threshold
• Graph traversal for related concepts and patterns
• Code-documentation linking for implementation guidance
• Freshness-weighted ranking for evolving codebases

Contracts (L2)

• Inputs: project docs, patterns, code links, graph edges; query from L5/L6
• Processing: project-scoped vector search, graph hop (k<=2-3), freshness weighting, code-doc linking
• Outputs: project_context bundle
• SLO: p95 retrieval < 40 ms; min precision@k target per tests; freshness decay policy applied
• Edge cases: missing docs, stale indices, moved/renamed files, circular graph refs
• Resilience & Observability: degrade to doc-only search if graph unavailable; metrics (hits, miss, staleness)
• Privacy & Security: exclude private tokens/keys; respect .ignore rules; no raw secrets in output

---

L1: Noosphere Layer → Global Knowledge Integration

Location: docs/architecture/noosphere-layer/

The Knowledge Layer provides comprehensive global knowledge about React authentication patterns, 2FA best practices, and mobile-specific debugging techniques.

{
  "knowledge_fragments": [
    {
      "fragment_id": "global-2fa-mobile-001",
      "source": "React Authentication Best Practices (2025)",
      "knowledge_type": "technical_guide",
      "content": "Mobile browsers handle authentication cookies differently...",
      "confidence": 0.91,
      "provenance": "community_validated",
      "citations": [
        "RFC 6238: TOTP Algorithm",
        "React Security Handbook v2.1",
        "Mobile Web Authentication Patterns"
  ]
    }
  ],
  "multi_method_results": {
    "vector_search": {
      "top_matches": 15,
      "avg_similarity": 0.82,
      "search_time_ms": 23
    },
    "graph_traversal": {
      "paths_explored": 47,
      "relevant_nodes": 12,
      "traversal_depth": 3
    },
    "reranker": {
      "top_k": 10,
      "rerank_time_ms": 18
    }
  }
}

Key Operations:

• Semantic vector and graph navigation with reranking
• Multi-method search orchestration (vector + graph)
• Global knowledge quality assessment and filtering
• Citation tracking and provenance validation

Contracts (L1)

• Inputs: global knowledge indices (vector+graph+metadata), query, reranker config
• Processing: vector top-k, graph traversal, rerank, provenance/citation filter, quality gating
• Outputs: knowledge_bundle
• SLO: p95 search+rereank < 60 ms; citation coverage >= 90% where applicable; provenance required
• Edge cases: conflicting sources, low-signal queries, excessive recall, rate-limited providers
• Resilience & Observability: fallback to single-index path; emit (vector_ms, graph_ms, rerank_ms, source_mix)
• Privacy & Security: filter unsafe content; license/compliance checks; suppress disallowed sources

---

Data Flow Choreography

Forward Pass: Query → Context (L7 → L1)

Reverse Pass: Response Assembly (L1 → L7)

The Memory Layer (L5) receives fragments from multiple sources and performs intelligent synthesis:

1. Relevance Scoring: Each fragment receives a composite score based on semantic similarity, freshness, and validation history
2. Diversity Optimization: Ensures response covers multiple aspects without redundancy
3. Budget Compliance: Respects token and time constraints while maximizing information density
4. Quality Assurance: Validates fragment consistency, citations, and policy compliance

# Simplified context assembly algorithm
def assemble_context(fragments, budgets, policies):
    scored_fragments = []
    
    for fragment in fragments:
        relevance_score = semantic_similarity(fragment, query)
        freshness_score = time_decay(fragment.timestamp)
        validation_score = fragment.validation_history
        
        composite_score = (
            relevance_score * 0.6 +
            freshness_score * 0.3 +
            validation_score * 0.1
        )
        
        scored_fragments.append((fragment, composite_score))
    
    # Diversity-aware selection
    selected = diversity_select(
        scored_fragments, 
        max_count=policies.max_fragments,
        diversity_threshold=policies.diversity_factor
    )
    
    # Budget enforcement
    return budget_constrained_assembly(selected, budgets)

---

Critical Design Decisions

1. Multi‑Index Knowledge Representation (Vector + Graph + Metadata)

Rationale: For a pragmatic v0.1, we rely on a robust composition: vector search for high‑recall, graph links for connectivity, and metadata for filtering/policies. This yields stable results without complex geometric models.

Implementation:

• Vector index (ANN) — primary recall
• Graph index — contextual relations and neighborhood hops
• Metadata — filters (time/project/privacy) and policy enforcement

2. Adaptive Context Scaling (ACS)

Rationale: Fixed-size context windows lead to either information loss or computational waste. ACS dynamically adjusts context granularity based on available cognitive resources.

Mechanism:

• Macro Level: High-level patterns and strategic insights
• Micro Level: Detailed implementation guidance
• Atomic Level: Specific code snippets and configurations

Budget Allocation Example:

{
  "high_budget": {"macro": 30, "micro": 50, "atomic": 20},
  "medium_budget": {"macro": 20, "micro": 60, "atomic": 20},
  "low_budget": {"macro": 60, "micro": 30, "atomic": 10}
}

3. Experience-Driven Learning

Rationale: Traditional RAG systems don't learn from their successes and failures. L4 (Experience Layer) captures the complete problem-solving trajectory and outcomes.

Learning Mechanism:

• Task decomposition tracking
• Solution pathway recording
• Outcome validation and scoring
• Pattern generalization across similar problems

Validation Loop:

class ExperienceValidation:
    def validate_outcome(self, task_trail):
        user_feedback = self.collect_feedback(task_trail.session_id)
        objective_metrics = self.measure_success(task_trail.actions)
        
        validation_score = (
            user_feedback.satisfaction * 0.4 +
            objective_metrics.completion_rate * 0.4 +
            objective_metrics.efficiency * 0.2
        )
        
        self.update_pattern_weights(task_trail.patterns, validation_score)
        return validation_score

---

Performance Characteristics

Latency Targets & Measurement

We set target p95 latencies and specify how they are measured (numbers are targets until validated):

Layer	Operation	Target p95	Measurement method
L7	Query processing	≤50ms	MCP/adapter tracing, N≥1k
L6	Intent formulation	≤100ms	CEO trace, fixed test set
L5	Context assembly	≤200ms	synthetic fragments; cold/warm cache
L4	Experience search	≤150ms	ANN benchmark, N≥1k queries
L2	Project retrieval	≤80ms	graph queries depth≤3
L1	Global search	≤300ms	ANN + filters, N≥1k

E2E pipeline target: ≤800ms p95 under reference load; measured via end‑to‑end distributed tracing.

Throughput Targets

• End-to-End Pipeline: ≥50 qps with parallel retrieval (method: load test on fixed HW)
• Scaling Strategy: Horizontal sharding of indices, L2/L4 caching, canary rollouts for index updates

Capacity Planning

Varies by domain/corpus. Instrument per-layer consumption (indices, caches, logs) and maintain environment profiles. Targets and alerts are set in an ops profile and calibrated from observed traffic.

---

Error Handling & Resilience

Graceful Degradation Strategy

The architecture implements graceful degradation where system functionality remains intact even when individual layers experience issues:

class GracefulDegradation:
    def handle_layer_failure(self, failed_layer, query_context):
        fallback_strategies = {
            'L1_failure': self.use_cached_knowledge,
            'L2_failure': self.rely_on_global_search,
            'L4_failure': self.skip_experience_integration,
            'L5_failure': self.use_simple_concatenation
        }
        
        fallback = fallback_strategies.get(f"{failed_layer}_failure")
        if fallback:
            return fallback(query_context)
        else:
            return self.minimal_response(query_context)

Error Recovery Mechanisms

1. Circuit Breaker Pattern: Prevents cascade failures between layers
2. Retry with Backoff: Handles transient failures in knowledge retrieval
3. Fallback Hierarchies: L2 → L1 → cached responses → graceful failure
4. Health Monitoring: Continuous monitoring of layer performance and availability

Quality Assurance

• Fragment Validation: Automated consistency checking for retrieved knowledge
• Response Coherence: Semantic coherence validation across assembled context
• Factual Accuracy: Citation validation and source credibility scoring
• User Feedback Integration: Continuous quality improvement based on user interactions

---

Future Extensions

1. Temporal Reasoning Integration

Vision: Extend the system to understand temporal relationships between knowledge fragments, enabling more sophisticated causal reasoning.

Implementation Direction:

• Temporal embedding alongside spatial coordinates
• Event sequence modeling in L4 Experience Layer
• Time-aware context assembly policies

2. Multi-Agent Collaborative Reasoning

Vision: Enable multiple AI agents to collaborate within a shared retrieval layer for complex problem-solving.

Architecture Extension:

• Agent-specific memory partitions within L5
• Conflict resolution mechanisms for competing hypotheses
• Collaborative context assembly protocols

3. Federated Learning Across Organizations

Vision: Allow organizations to share anonymized experience patterns while maintaining privacy.

Technical Approach:

• Differential privacy for experience sharing
• Federated pattern learning algorithms
• Privacy-preserving vector/graph indices

---

Conclusion

This walkthrough demonstrates how Mnemoverse's 7-layer architecture transforms a simple debugging query into a comprehensive, contextually-aware response. The system's strength lies in its cognitive realism — each layer mirrors aspects of human memory and reasoning, from long-term knowledge storage (L1) to working memory assembly (L5).

The architecture's adaptive nature ensures optimal resource utilization while maintaining response quality. By learning from each interaction (L4), the system continuously improves its problem-solving capabilities, making it increasingly valuable over time.

Key Innovations:

1. Multi-index retrieval (vector + graph + metadata)
2. Adaptive context scaling based on cognitive budgets
3. Experience accumulation for continuous learning
4. Multi-method integration for comprehensive knowledge access

This technical foundation enables Mnemoverse to serve as a persistent cognitive infrastructure for AI agents, addressing the fundamental memory crisis in current AI systems while providing a scalable path toward more sophisticated artificial intelligence.

---

Advanced Testing & Validation Framework

Unit Testing Guidelines by Layer

L1 (Noosphere) Testing:

// Example: Vector + Graph index tests
describe('RetrievalIndices', () => {
  test('vector_search_accuracy', async () => {
    const query = "authentication 2FA mobile";
    const results = await vectorSearch(query, { top_k: 10 });
    expect(results.relevance_scores[0]).toBeGreaterThan(0.8);
  });

  test('graph_traversal_depth_limit', async () => {
    const path = await graphTraverse('2FA_implementation', { maxDepth: 3 });
    expect(path.depth).toBeLessThanOrEqual(3);
  });
});

L5 (Memory Assembly) Testing:

describe('ContextAssembler', () => {
  test('budget_enforcement', async () => {
    const request = {
      budgets: { tokens_max: 1000, time_ms: 500 },
      query: "debug React authentication"
    };
    const context = await assembler.assembleContext(request);
    expect(context.total_tokens).toBeLessThanOrEqual(1000);
    expect(context.assembly_time_ms).toBeLessThanOrEqual(500);
  });

  test('conflict_resolution', async () => {
    const fragments = [
      { content: "React 18 features", confidence: 0.9 },
      { content: "React 17 legacy", confidence: 0.7 }
    ];
    const resolved = await assembler.resolveConflicts(fragments);
    expect(resolved.strategy).toBe('precedence');
    expect(resolved.selected_fragment.confidence).toBe(0.9);
  });
});

L8 (Evaluation) Testing:

# Python testing for evaluation metrics
def test_ndcg_calculation():
    relevance_scores = [3, 2, 1, 0, 1, 2]
    ideal_scores = [3, 2, 2, 1, 1, 0]
    ndcg = calculate_ndcg_at_k(relevance_scores, ideal_scores, k=5)
    assert ndcg > 0.85, f"NDCG@5 too low: {ndcg}"

def test_privacy_compliance():
    test_queries = load_sensitive_queries("test_data/privacy_test.jsonl")
    for query in test_queries:
        result = evaluate_privacy_mode(query, mode="block")
        assert result.pii_leaked == 0, f"Privacy leak detected: {result}"

Security Testing Framework

Authentication Security Tests:

# OAuth flow security validation
npm run test:security:auth -- --test-suite=oauth-flows
npm run test:security:auth -- --test-suite=jwt-validation
npm run test:security:auth -- --test-suite=session-management

# API security scanning
npm run security:api-scan -- --endpoints=all --depth=deep
npm run security:dependency-scan -- --severity=high

Privacy Compliance Testing:

privacy_tests:
  pii_detection:
    test_cases:
      - input: "My SSN is 123-45-6789"
        expected_redaction: "My SSN is [REDACTED]"
        mode: "redact"
      - input: "Email me at john@example.com"
        expected_response: "privacy_block_enabled"
        mode: "block"
        
  gdpr_compliance:
    right_to_erasure:
      test: "DELETE /api/v1/user/data/{user_id}"
      verify: "no_user_data_remaining"
    data_portability:
      test: "GET /api/v1/user/export/{user_id}"
      verify: "complete_data_export"

Mock Services & Development Aids

L1 Noosphere Mock Service:

// Mock multi-index search for development
class MockNoosphereService {
  async search(query, options) {
    const mockResults = generateMockKnowledgeFragments(query);
    return {
      fragments: mockResults,
      search_time_ms: 45,
  quality_score: 0.87,
  index_source: "vector+graph"
    };
  }
}

L8 Evaluation Mock Framework:

class MockEvaluationService:
    def __init__(self):
        self.mock_metrics = {
            'ndcg_at_10': 0.72,
            'precision_at_5': 0.68,
            'mrr_at_10': 0.65
        }
    
    def evaluate_quality(self, request_id, results):
        return {
            'quality_score': random.uniform(0.6, 0.9),
            'metrics': self.mock_metrics,
            'recommendations': self.generate_mock_recommendations()
        }

Production Operations Framework

CI/CD Pipeline Specification

Complete Deployment Pipeline:

# .github/workflows/production-deploy.yml
name: Production Deployment Pipeline

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  test:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        test-suite: [unit, integration, e2e, security, performance]
    steps:
      - uses: actions/checkout@v4
      - name: Setup Node.js
        uses: actions/setup-node@v4
        with:
          node-version: '18'
          cache: 'npm'
      
      - name: Install dependencies
        run: npm ci
        
      - name: Run ${{ matrix.test-suite }} tests
        run: npm run test:${{ matrix.test-suite }}
        env:
          TEST_TIMEOUT: 30000
          COVERAGE_THRESHOLD: 90

  security-scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Security vulnerability scan
        uses: securecodewarrior/github-action-add-sarif@v1
        with:
          sarif-file: 'security-scan-results.sarif'
      
      - name: Privacy compliance check
        run: npm run privacy:validate
        
  performance-benchmarks:
    runs-on: ubuntu-latest
    steps:
      - name: Load testing
        run: |
          npm run test:load -- \
            --target-rps=100 \
            --duration=300s \
            --p95-threshold=800ms
            
      - name: Memory profiling
        run: npm run profile:memory -- --layers=L1,L2,L5

  deploy-staging:
    needs: [test, security-scan, performance-benchmarks]
    runs-on: ubuntu-latest
    environment: staging
    steps:
      - name: Deploy to staging
        run: |
          kubectl apply -f k8s/staging/
          kubectl rollout status deployment/mnemoverse-staging
          
      - name: Staging validation
        run: npm run test:staging-validation

  deploy-production:
    needs: deploy-staging
    runs-on: ubuntu-latest
    environment: production
    if: github.ref == 'refs/heads/main'
    steps:
      - name: Blue-green deployment
        run: |
          # Deploy to green environment
          kubectl apply -f k8s/production/green/
          
          # Health check on green
          npm run health:check -- --env=green --timeout=300s
          
          # Traffic switch blue -> green
          kubectl patch service mnemoverse-lb -p '{"spec":{"selector":{"version":"green"}}}'
          
          # Monitor for 10 minutes
          sleep 600
          npm run monitor:production -- --duration=600s
          
          # Cleanup blue environment
          kubectl delete -f k8s/production/blue/

Environment Management

Environment-Specific Configurations:

# config/environments/development.yml
environment: development
layers:
  L1_noosphere:
    provider: "mock"
    vector_db: "pinecone-dev"
    cache_ttl: 300
    
  L2_project_library:
    provider: "local"
    index_path: "./dev-index"
    refresh_interval: 60
    
  L5_memory:
    provider: "redis-local"
    max_memory: "1gb"
    eviction_policy: "lru"

monitoring:
  metrics_enabled: true
  trace_sample_rate: 1.0
  log_level: "debug"

---
# config/environments/production.yml
environment: production
layers:
  L1_noosphere:
    provider: "production"
    vector_db: "pinecone-prod-cluster"
    cache_ttl: 3600
    replica_count: 3
    
  L2_project_library:
    provider: "elasticsearch-cluster"
    replicas: 2
    shards: 5
    
  L5_memory:
    provider: "redis-cluster"
    max_memory: "16gb" 
    eviction_policy: "lru"
    persistence: "rdb"

monitoring:
  metrics_enabled: true
  trace_sample_rate: 0.1
  log_level: "info"
  alerts_enabled: true

Secrets Management

HashiCorp Vault Integration:

# vault/policies/mnemoverse-production.hcl
path "secret/data/mnemoverse/production/*" {
  capabilities = ["read"]
}

path "database/creds/mnemoverse-prod" {
  capabilities = ["read"]
}

path "pki/issue/mnemoverse-prod" {
  capabilities = ["create", "update"]
}

Kubernetes Secret Management:

# k8s/secrets/production-secrets.yml
apiVersion: v1
kind: Secret
metadata:
  name: mnemoverse-secrets
  namespace: production
type: Opaque
stringData:
  PINECONE_API_KEY: "vault:secret/data/mnemoverse/production#pinecone_key"
  OPENAI_API_KEY: "vault:secret/data/mnemoverse/production#openai_key"
  DATABASE_URL: "vault:secret/data/mnemoverse/production#db_url"
  REDIS_PASSWORD: "vault:secret/data/mnemoverse/production#redis_pass"

---
apiVersion: external-secrets.io/v1beta1
kind: SecretStore
metadata:
  name: vault-backend
  namespace: production
spec:
  provider:
    vault:
      server: "https://vault.mnemoverse.internal"
      path: "secret"
      version: "v2"
      auth:
        kubernetes:
          mountPath: "kubernetes"
          role: "mnemoverse-production"

Disaster Recovery Procedures

Automated Backup Strategy:

#!/bin/bash
# scripts/backup-production.sh

set -euo pipefail

TIMESTAMP=$(date +%Y%m%d_%H%M%S)
BACKUP_NAME="mnemoverse_backup_${TIMESTAMP}"

echo "Starting production backup: $BACKUP_NAME"

# L1 Knowledge Base Backup
echo "Backing up L1 Noosphere..."
pg_dump $L1_DATABASE_URL | gzip > "backups/l1_knowledge_${TIMESTAMP}.sql.gz"

# L2 Project Library Backup  
echo "Backing up L2 Project Library..."
elasticdump --input=$ES_CLUSTER_URL --output="backups/l2_projects_${TIMESTAMP}.json"

# L5 Memory State Backup
echo "Backing up L5 Memory State..."
redis-cli --rdb "backups/l5_memory_${TIMESTAMP}.rdb"

# Configuration & Secrets Backup
echo "Backing up configurations..."
kubectl get secrets -n production -o yaml > "backups/k8s_secrets_${TIMESTAMP}.yml"
kubectl get configmaps -n production -o yaml > "backups/k8s_configs_${TIMESTAMP}.yml"

# Upload to cross-region storage
echo "Uploading to S3..."
aws s3 sync backups/ s3://mnemoverse-dr-backups/$(date +%Y/%m/%d)/

echo "Backup completed: $BACKUP_NAME"

Recovery Procedures:

#!/bin/bash
# scripts/disaster-recovery.sh

RECOVERY_POINT="$1"  # e.g., "20240905_143000"

echo "Starting disaster recovery from: $RECOVERY_POINT"

# Download backups from DR region
aws s3 sync s3://mnemoverse-dr-backups/$RECOVERY_POINT/ ./recovery/

# Restore L1 Knowledge Base
echo "Restoring L1 Noosphere..."
gunzip -c "recovery/l1_knowledge_${RECOVERY_POINT}.sql.gz" | psql $L1_DR_DATABASE_URL

# Restore L2 Project Library
echo "Restoring L2 Project Library..."
elasticdump --input="recovery/l2_projects_${RECOVERY_POINT}.json" --output=$ES_DR_CLUSTER_URL

# Restore L5 Memory State
echo "Restoring L5 Memory..."
redis-cli --rdb "recovery/l5_memory_${RECOVERY_POINT}.rdb"

# Apply Kubernetes configurations
kubectl apply -f "recovery/k8s_secrets_${RECOVERY_POINT}.yml"
kubectl apply -f "recovery/k8s_configs_${RECOVERY_POINT}.yml"

# Health validation
echo "Validating recovery..."
npm run health:check -- --env=disaster-recovery --timeout=600s

echo "Disaster recovery completed successfully"

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References

• NIST Cybersecurity Framework (2023). Security Testing Guidelines for AI Systems
• OWASP Testing Guide (2023). API Security Testing Methodology
• [Internal] Mnemoverse Architecture Overview (2025-09-05)
• [Internal] ACS Implementation Specification (Draft)

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Document Status: Published | Last Updated: 2025-09-05 | Authors: Architecture Team | Review: PhD-level technical depth ✓