31 KiB
31 KiB
System Architecture Document
Virtual Board Member AI System
Document Version: 1.0
Date: August 2025
Classification: Confidential
1. Executive Summary
This document defines the complete system architecture for the Virtual Board Member AI system, incorporating microservices architecture, event-driven design patterns, and enterprise-grade security controls. The architecture supports both local development and cloud-scale production deployment.
2. High-Level Architecture
2.1 System Overview
┌─────────────────────────────────────────────────────────────────┐
│ CLIENT LAYER │
├─────────────────┬───────────────────┬──────────────────────────┤
│ Web Portal │ Mobile Apps │ API Clients │
└────────┬────────┴────────┬──────────┴────────┬─────────────────┘
│ │ │
▼ ▼ ▼
┌─────────────────────────────────────────────────────────────────┐
│ API GATEWAY (Kong/AWS API GW) │
│ • Rate Limiting • Authentication • Request Routing │
└────────┬─────────────────────────────────────┬──────────────────┘
│ │
▼ ▼
┌──────────────────────────────┬─────────────────────────────────┐
│ SECURITY LAYER │ ORCHESTRATION LAYER │
├──────────────────────────────┼─────────────────────────────────┤
│ • OAuth 2.0/OIDC │ • LangChain Controller │
│ • JWT Validation │ • Workflow Engine (Airflow) │
│ • RBAC │ • Model Router │
└──────────────┬───────────────┴───────────┬─────────────────────┘
│ │
▼ ▼
┌──────────────────────────────────────────────────────────────┐
│ MICROSERVICES LAYER │
├────────────────┬────────────────┬───────────────┬─────────────┤
│ LLM Service │ RAG Service │ Doc Processor │ Analytics │
│ • OpenRouter │ • Qdrant │ • PDF/XLSX │ • Metrics │
│ • Fallback │ • Embedding │ • OCR │ • Insights │
└────────┬───────┴────────┬───────┴───────┬──────┴──────┬──────┘
│ │ │ │
▼ ▼ ▼ ▼
┌──────────────────────────────────────────────────────────────┐
│ DATA LAYER │
├─────────────┬──────────────┬──────────────┬─────────────────┤
│ Vector DB │ Document │ Cache │ Message Queue │
│ (Qdrant) │ Store (S3) │ (Redis) │ (Kafka/SQS) │
└─────────────┴──────────────┴──────────────┴─────────────────┘
2.2 Component Responsibilities
| Component | Primary Responsibility | Technology Stack |
|---|---|---|
| API Gateway | Request routing, rate limiting, authentication | Kong, AWS API Gateway |
| LLM Service | Model orchestration, prompt management | LangChain, OpenRouter |
| RAG Service | Document retrieval, context management | Qdrant, LangChain |
| Document Processor | File parsing, OCR, extraction | Python libs, Tesseract |
| Analytics Service | Usage tracking, insights generation | PostgreSQL, Grafana |
| Vector Database | Semantic search, document storage | Qdrant |
| Cache Layer | Response caching, session management | Redis |
| Message Queue | Async processing, event streaming | Kafka/AWS SQS |
3. Detailed Component Architecture
3.1 LLM Orchestration Service
class LLMOrchestrationArchitecture:
"""
Core orchestration service managing multi-model routing and execution
"""
components = {
"model_router": {
"responsibility": "Route requests to optimal models",
"implementation": "Strategy pattern with cost/quality optimization",
"models": {
"extraction": "gpt-4o-mini",
"analysis": "claude-3.5-sonnet",
"synthesis": "gpt-4-turbo",
"vision": "gpt-4-vision"
}
},
"prompt_manager": {
"responsibility": "Manage and version prompt templates",
"storage": "PostgreSQL with version control",
"caching": "Redis with 1-hour TTL"
},
"chain_executor": {
"responsibility": "Execute multi-step reasoning chains",
"framework": "LangChain with custom extensions",
"patterns": ["MapReduce", "Sequential", "Parallel"]
},
"memory_manager": {
"responsibility": "Maintain conversation context",
"types": {
"short_term": "Redis (24-hour TTL)",
"long_term": "PostgreSQL",
"semantic": "Qdrant vectors"
}
}
}
3.2 Document Processing Pipeline
pipeline:
stages:
- ingestion:
supported_formats: [pdf, xlsx, csv, pptx, txt]
max_file_size: 100MB
concurrent_processing: 10
- extraction:
pdf:
primary: pdfplumber
fallback: PyPDF2
ocr: tesseract-ocr
excel:
library: openpyxl
preserve: [formulas, formatting, charts]
powerpoint:
library: python-pptx
image_extraction: gpt-4-vision
- transformation:
chunking:
strategy: semantic
size: 1000-1500 tokens
overlap: 200 tokens
metadata:
extraction: automatic
enrichment: business_context
- indexing:
embedding_model: voyage-3-large
batch_size: 100
parallel_workers: 4
3.3 Vector Database Architecture
class VectorDatabaseSchema:
"""
Qdrant collection schema for board documents
"""
collection_config = {
"name": "board_documents",
"vector_size": 1024,
"distance": "Cosine",
"optimizers_config": {
"indexing_threshold": 20000,
"memmap_threshold": 50000,
"default_segment_number": 4
},
"payload_schema": {
"document_id": "keyword",
"document_type": "keyword", # report|presentation|minutes
"department": "keyword", # finance|hr|legal|operations
"date_created": "datetime",
"reporting_period": "keyword",
"confidentiality": "keyword", # public|internal|confidential
"stakeholders": "keyword[]",
"key_topics": "text[]",
"content": "text",
"chunk_index": "integer",
"total_chunks": "integer"
}
}
4. Data Flow Architecture
4.1 Document Ingestion Flow
User Upload → API Gateway → Document Processor
↓
Validation & Security Scan
↓
Format-Specific Parser
↓
Content Extraction
↓
┌──────────┴──────────┐
↓ ↓
Raw Storage (S3) Text Processing
↓
Chunking Strategy
↓
Embedding Generation
↓
Vector Database
↓
Indexing Complete
4.2 Query Processing Flow
User Query → API Gateway → Authentication
↓
Query Processor
↓
Intent Classification
↓
┌─────────────┼─────────────┐
↓ ↓ ↓
RAG Pipeline Direct LLM Analytics
↓ ↓ ↓
Vector Search Model Router SQL Query
↓ ↓ ↓
Context Build Prompt Build Data Fetch
↓ ↓ ↓
└─────────────┼─────────────┘
↓
Response Synthesis
↓
Output Validation
↓
Client Response
5. Security Architecture
5.1 Security Layers
security_architecture:
perimeter_security:
- waf: AWS WAF / Cloudflare
- ddos_protection: Cloudflare / AWS Shield
- api_gateway: Rate limiting, API key validation
authentication:
- protocol: OAuth 2.0 / OIDC
- provider: Auth0 / AWS Cognito
- mfa: Required for admin access
authorization:
- model: RBAC with attribute-based extensions
- roles:
- board_member: Full access to all features
- executive: Department-specific access
- analyst: Read-only access
- admin: System configuration
data_protection:
encryption_at_rest:
- algorithm: AES-256-GCM
- key_management: AWS KMS / HashiCorp Vault
encryption_in_transit:
- protocol: TLS 1.3
- certificate: EV SSL
llm_security:
- prompt_injection_prevention: Input validation
- output_filtering: PII detection and masking
- audit_logging: All queries and responses
- rate_limiting: Per-user and per-endpoint
5.2 Zero-Trust Architecture
class ZeroTrustImplementation:
"""
Zero-trust security model implementation
"""
principles = {
"never_trust": "All requests validated regardless of source",
"always_verify": "Continuous authentication and authorization",
"least_privilege": "Minimal access rights by default",
"assume_breach": "Design assumes compromise has occurred"
}
implementation = {
"micro_segmentation": {
"network": "Service mesh with Istio",
"services": "Individual service authentication",
"data": "Field-level encryption where needed"
},
"continuous_validation": {
"token_refresh": "15-minute intervals",
"behavior_analysis": "Anomaly detection on usage patterns",
"device_trust": "Device fingerprinting and validation"
}
}
6. Scalability Architecture
6.1 Horizontal Scaling Strategy
scaling_configuration:
kubernetes:
autoscaling:
- type: HorizontalPodAutoscaler
metrics:
- cpu: 70%
- memory: 80%
- custom: requests_per_second > 100
services:
llm_service:
min_replicas: 2
max_replicas: 20
target_cpu: 70%
rag_service:
min_replicas: 3
max_replicas: 15
target_cpu: 60%
document_processor:
min_replicas: 2
max_replicas: 10
scaling_policy: job_queue_length
database:
qdrant:
sharding: 4 shards
replication: 3 replicas per shard
distribution: Consistent hashing
redis:
clustering: Redis Cluster mode
nodes: 6 (3 masters, 3 replicas)
6.2 Performance Optimization
class PerformanceOptimization:
"""
System-wide performance optimization strategies
"""
caching_strategy = {
"l1_cache": {
"type": "Application memory",
"ttl": "5 minutes",
"size": "1GB per instance"
},
"l2_cache": {
"type": "Redis",
"ttl": "1 hour",
"size": "10GB cluster"
},
"l3_cache": {
"type": "CDN (CloudFront)",
"ttl": "24 hours",
"content": "Static assets, common reports"
}
}
database_optimization = {
"connection_pooling": {
"min_connections": 10,
"max_connections": 100,
"timeout": 30
},
"query_optimization": {
"indexes": "Automated index recommendation",
"partitioning": "Time-based for logs",
"materialized_views": "Common aggregations"
}
}
llm_optimization = {
"batching": "Group similar requests",
"caching": "Semantic similarity matching",
"model_routing": "Cost-optimized selection",
"token_optimization": "Prompt compression"
}
7. Deployment Architecture
7.1 Environment Strategy
environments:
development:
infrastructure: Docker Compose
database: Chroma (local)
llm: OpenRouter sandbox
data: Synthetic test data
staging:
infrastructure: Kubernetes (single node)
database: Qdrant Cloud (dev tier)
llm: OpenRouter with rate limits
data: Anonymized production sample
production:
infrastructure: EKS/GKE/AKS
database: Qdrant Cloud (production)
llm: OpenRouter production
data: Full production access
backup: Real-time replication
7.2 CI/CD Pipeline
pipeline:
source_control:
platform: GitHub/GitLab
branching: GitFlow
protection: Main branch protected
continuous_integration:
- trigger: Pull request
- steps:
- lint: Black, isort, mypy
- test: pytest with 80% coverage
- security: Bandit, safety
- build: Docker multi-stage
continuous_deployment:
- staging:
trigger: Merge to develop
approval: Automatic
rollback: Automatic on failure
- production:
trigger: Merge to main
approval: Manual (2 approvers)
strategy: Blue-green deployment
rollback: One-click rollback
8. Monitoring & Observability
8.1 Monitoring Stack
monitoring:
metrics:
collection: Prometheus
storage: VictoriaMetrics
visualization: Grafana
logging:
aggregation: Fluentd
storage: Elasticsearch
analysis: Kibana
tracing:
instrumentation: OpenTelemetry
backend: Jaeger
sampling: 1% in production
alerting:
manager: AlertManager
channels: [email, slack, pagerduty]
escalation: 3-tier support model
8.2 Key Performance Indicators
class SystemKPIs:
"""
Critical metrics for system health monitoring
"""
availability = {
"uptime_target": "99.9%",
"measurement": "Synthetic monitoring",
"alert_threshold": "99.5%"
}
performance = {
"response_time_p50": "< 2 seconds",
"response_time_p95": "< 5 seconds",
"response_time_p99": "< 10 seconds",
"throughput": "> 100 requests/second"
}
business_metrics = {
"daily_active_users": "Track unique users",
"query_success_rate": "> 95%",
"document_processing_rate": "> 500/hour",
"cost_per_query": "< $0.10"
}
ai_metrics = {
"model_accuracy": "> 90%",
"hallucination_rate": "< 2%",
"context_relevance": "> 85%",
"user_satisfaction": "> 4.5/5"
}
9. Disaster Recovery
9.1 Backup Strategy
backup_strategy:
data_classification:
critical:
- vector_database
- document_store
- configuration
important:
- logs
- metrics
- cache
backup_schedule:
critical:
frequency: Real-time replication
retention: 90 days
location: Multi-region
important:
frequency: Daily
retention: 30 days
location: Single region
recovery_objectives:
rto: 4 hours # Recovery Time Objective
rpo: 1 hour # Recovery Point Objective
9.2 Failure Scenarios
class FailureScenarios:
"""
Documented failure scenarios and recovery procedures
"""
scenarios = {
"llm_service_failure": {
"detection": "Health check failure",
"immediate_action": "Fallback to secondary model",
"recovery": "Auto-restart with exponential backoff",
"escalation": "Page on-call after 3 failures"
},
"database_failure": {
"detection": "Connection timeout",
"immediate_action": "Serve from cache",
"recovery": "Automatic failover to replica",
"escalation": "Immediate page to DBA"
},
"data_corruption": {
"detection": "Checksum validation",
"immediate_action": "Isolate affected data",
"recovery": "Restore from last known good backup",
"escalation": "Executive notification"
}
}
10. Integration Architecture
10.1 External System Integrations
integrations:
document_sources:
sharepoint:
protocol: REST API
auth: OAuth 2.0
sync: Incremental every 15 minutes
google_drive:
protocol: REST API
auth: OAuth 2.0
sync: Real-time via webhooks
email:
protocol: IMAP/Exchange
auth: OAuth 2.0
sync: Every 5 minutes
identity_providers:
primary: Active Directory
protocol: SAML 2.0
attributes: [email, department, role]
notification_systems:
email: SMTP with TLS
slack: Webhook API
teams: Graph API
10.2 API Specifications
class APISpecification:
"""
RESTful API design following OpenAPI 3.0
"""
endpoints = {
"/api/v1/documents": {
"POST": "Upload document",
"GET": "List documents",
"DELETE": "Remove document"
},
"/api/v1/query": {
"POST": "Submit query",
"GET": "Retrieve query history"
},
"/api/v1/analysis": {
"POST": "Generate analysis",
"GET": "Retrieve past analyses"
},
"/api/v1/commitments": {
"GET": "List commitments",
"PUT": "Update commitment status",
"POST": "Create manual commitment"
}
}
authentication = {
"type": "Bearer token (JWT)",
"header": "Authorization: Bearer <token>",
"expiry": "1 hour",
"refresh": "Available via /api/v1/auth/refresh"
}
rate_limiting = {
"default": "100 requests per minute",
"burst": "200 requests allowed",
"headers": {
"X-RateLimit-Limit": "Current limit",
"X-RateLimit-Remaining": "Requests remaining",
"X-RateLimit-Reset": "Reset timestamp"
}
}
11. Development Architecture
11.1 Local Development Setup
local_development:
prerequisites:
- Docker Desktop 4.0+
- Python 3.11+
- Node.js 18+ (for frontend)
- 16GB RAM minimum
- 50GB free disk space
setup_script: |
# Clone repository
git clone https://github.com/company/vbm-ai
cd vbm-ai
# Environment setup
cp .env.example .env.local
# Start services
docker-compose -f docker-compose.dev.yml up -d
# Install dependencies
poetry install
# Run migrations
poetry run alembic upgrade head
# Seed test data
poetry run python scripts/seed_data.py
# Start development server
poetry run uvicorn app.main:app --reload
11.2 Testing Architecture
class TestingStrategy:
"""
Comprehensive testing approach for AI systems
"""
test_levels = {
"unit_tests": {
"coverage_target": "80%",
"framework": "pytest",
"mocking": "unittest.mock for LLM calls",
"execution": "On every commit"
},
"integration_tests": {
"scope": "Service boundaries",
"framework": "pytest + testcontainers",
"data": "Synthetic test fixtures",
"execution": "On pull requests"
},
"e2e_tests": {
"scope": "Full user workflows",
"framework": "Playwright",
"environment": "Staging",
"execution": "Before production deploy"
},
"llm_tests": {
"framework": "DeepEval",
"metrics": ["correctness", "relevance", "hallucination"],
"dataset": "Golden test set of 100 queries",
"threshold": "90% pass rate"
}
}
test_data_strategy = {
"synthetic_generation": "Faker + custom generators",
"anonymization": "Production data scrubbing",
"volume": "1000 documents minimum",
"diversity": "All document types represented"
}
12. Migration Strategy
12.1 Local to Cloud Migration Path
migration_phases:
phase_1_local:
duration: Weeks 1-4
environment: Docker Compose
components:
- vector_db: Chroma (local)
- llm: OpenRouter dev keys
- storage: Local filesystem
goals:
- Validate core functionality
- Establish development workflow
- Create initial test suite
phase_2_hybrid:
duration: Weeks 5-8
environment: Local + Cloud services
components:
- vector_db: Qdrant Cloud
- llm: OpenRouter production
- storage: AWS S3
goals:
- Test cloud service integration
- Validate performance at scale
- Implement security controls
phase_3_cloud:
duration: Weeks 9-12
environment: Full cloud deployment
infrastructure: Kubernetes (EKS/GKE)
components:
- All services containerized
- Multi-region deployment
- Full monitoring stack
goals:
- Production readiness
- High availability setup
- Disaster recovery validation
12.2 Data Migration Strategy
class DataMigrationPlan:
"""
Zero-downtime data migration strategy
"""
migration_steps = [
{
"step": 1,
"action": "Setup parallel environments",
"duration": "2 days",
"rollback": "No impact - parallel setup"
},
{
"step": 2,
"action": "Initial data sync",
"duration": "1-3 days depending on volume",
"rollback": "Delete cloud copies"
},
{
"step": 3,
"action": "Enable dual writes",
"duration": "1 day",
"rollback": "Disable dual writes"
},
{
"step": 4,
"action": "Validation and reconciliation",
"duration": "2 days",
"rollback": "Fix discrepancies and retry"
},
{
"step": 5,
"action": "Traffic cutover",
"duration": "1 hour",
"rollback": "DNS switch back"
}
]
validation_criteria = {
"document_count": "100% match",
"vector_similarity": "> 99% cosine similarity",
"metadata_integrity": "100% match",
"query_results": "95% similarity in top-10 results"
}
13. Performance Requirements
13.1 Service Level Objectives (SLOs)
slos:
availability:
target: 99.9%
measurement_window: 30 days
exclusions: Planned maintenance windows
latency:
p50: < 2 seconds
p95: < 5 seconds
p99: < 10 seconds
measurement: End-to-end including LLM calls
error_rate:
target: < 1%
exclusions: Client errors (4xx)
measurement_window: 1 hour rolling
throughput:
sustained: 100 requests/second
burst: 500 requests/second for 60 seconds
concurrent_users: 100
13.2 Capacity Planning
class CapacityPlanning:
"""
Resource requirements for different scales
"""
sizing_tiers = {
"small": {
"users": "< 50",
"documents": "< 10,000",
"queries_per_day": "< 1,000",
"infrastructure": {
"compute": "8 vCPUs, 32GB RAM",
"storage": "500GB SSD",
"database": "Qdrant 2-node cluster"
},
"monthly_cost": "$2,000 - $3,000"
},
"medium": {
"users": "50-500",
"documents": "10,000-100,000",
"queries_per_day": "1,000-10,000",
"infrastructure": {
"compute": "32 vCPUs, 128GB RAM",
"storage": "2TB SSD",
"database": "Qdrant 4-node cluster"
},
"monthly_cost": "$5,000 - $8,000"
},
"large": {
"users": "> 500",
"documents": "> 100,000",
"queries_per_day": "> 10,000",
"infrastructure": {
"compute": "100+ vCPUs, 400GB+ RAM",
"storage": "10TB+ SSD",
"database": "Qdrant 8+ node cluster"
},
"monthly_cost": "$15,000+"
}
}
14. Compliance & Governance
14.1 Regulatory Compliance
compliance_requirements:
data_privacy:
gdpr:
- data_minimization: Collect only necessary data
- right_to_erasure: Implement data deletion
- data_portability: Export user data on request
- consent_management: Track and manage consent
ccpa:
- disclosure: What data is collected
- deletion: Honor deletion requests
- opt_out: Allow opt-out of data sale
- non_discrimination: No penalty for exercising rights
industry_standards:
soc2_type2:
- security: Encryption and access controls
- availability: SLA compliance
- processing_integrity: Data accuracy
- confidentiality: Data protection
- privacy: Personal information handling
iso_27001:
- risk_assessment: Annual assessment
- security_controls: 114 controls implemented
- continuous_improvement: Regular audits
- documentation: Complete ISMS
14.2 Audit Architecture
class AuditArchitecture:
"""
Comprehensive audit logging and compliance tracking
"""
audit_events = {
"authentication": ["login", "logout", "failed_auth", "mfa_challenge"],
"authorization": ["permission_grant", "permission_deny", "role_change"],
"data_access": ["document_view", "document_download", "query_execution"],
"data_modification": ["document_upload", "document_delete", "metadata_update"],
"system_changes": ["config_change", "deployment", "user_management"],
"ai_operations": ["model_selection", "prompt_execution", "output_filtering"]
}
audit_log_schema = {
"timestamp": "ISO 8601 with timezone",
"user_id": "Authenticated user identifier",
"session_id": "Unique session identifier",
"event_type": "Category and specific event",
"resource": "Affected resource identifier",
"action": "Specific action performed",
"result": "Success/failure",
"metadata": "Additional context",
"ip_address": "Client IP (hashed)",
"user_agent": "Client information"
}
retention_policy = {
"audit_logs": "7 years",
"system_logs": "90 days",
"performance_metrics": "13 months",
"security_events": "7 years"
}
15. Appendices
Appendix A: Technology Stack Summary
| Layer | Technology | Version | License |
|---|---|---|---|
| Language | Python | 3.11+ | PSF |
| Framework | FastAPI | 0.100+ | MIT |
| LLM Orchestration | LangChain | 0.1+ | MIT |
| Vector Database | Qdrant | 1.7+ | Apache 2.0 |
| Cache | Redis | 7.0+ | BSD |
| Message Queue | Kafka | 3.5+ | Apache 2.0 |
| Container | Docker | 24+ | Apache 2.0 |
| Orchestration | Kubernetes | 1.28+ | Apache 2.0 |
| Monitoring | Prometheus | 2.45+ | Apache 2.0 |
Appendix B: Network Architecture
network_topology:
dmz:
- Load balancer
- WAF
- CDN endpoints
application_tier:
- API servers
- Web servers
- WebSocket servers
service_tier:
- Microservices
- Background workers
- Scheduled jobs
data_tier:
- Databases
- Cache layers
- File storage
management_tier:
- Monitoring
- Logging
- CI/CD
Appendix C: Security Checklist
- TLS 1.3 for all communications
- Secrets management via Vault/KMS
- Regular dependency updates
- Security scanning in CI/CD
- Penetration testing quarterly
- Security training for developers
- Incident response plan documented
- Data encryption at rest
- Network segmentation implemented
- Zero-trust architecture adopted
Document Approval
| Role | Name | Signature | Date |
|---|---|---|---|
| Chief Architect | |||
| Security Architect | |||
| DevOps Lead | |||
| CTO |