An enterprise-grade AI assistant that provides document analysis, commitment tracking, strategic insights, and decision support for board members and executives.

🚀 Quick Start

Prerequisites

Python 3.11+
Docker Desktop 4.0+
16GB RAM minimum
50GB free disk space

Local Development Setup

# Clone the repository
git clone <repository-url>
cd virtual_board_member

# Copy environment configuration
cp .env.example .env.local

# Start services with Docker Compose
docker-compose -f docker-compose.dev.yml up -d

# Install Python dependencies
poetry install

# Run database migrations
poetry run alembic upgrade head

# Start the development server
poetry run uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

The application will be available at http://localhost:8000

📋 Features

Core Capabilities

Document Analysis: Multi-format document ingestion and processing
Natural Language Querying: Ask questions in plain English about your documents
Commitment Tracking: Automatic extraction and tracking of action items
Strategic Insights: Risk identification and strategic alignment analysis
Meeting Support: Automated preparation and real-time assistance

Document Support

PDF, XLSX, CSV, PPTX, TXT formats
Up to 100MB per document
Batch upload (50 files simultaneously)
OCR for scanned documents

Security & Compliance

OAuth 2.0/OIDC authentication
Role-based access control (RBAC)
AES-256 encryption at rest
TLS 1.3 encryption in transit
Comprehensive audit logging
GDPR, SOX compliance ready

🏗️ Architecture

┌─────────────────────────────────────────────────────────────────┐
│                        CLIENT LAYER                              │
├─────────────────┬───────────────────┬──────────────────────────┤
│   Web Portal    │   Mobile Apps     │   API Clients            │
└────────┬────────┴────────┬──────────┴────────┬─────────────────┘
         │                 │                   │
         ▼                 ▼                   ▼
┌─────────────────────────────────────────────────────────────────┐
│                     API GATEWAY (FastAPI)                       │
│  • Rate Limiting  • Authentication  • Request Routing           │
└────────┬─────────────────────────────────────┬──────────────────┘
         │                                     │
         ▼                                     ▼
┌──────────────────────────────┬─────────────────────────────────┐
│       SECURITY LAYER         │       ORCHESTRATION LAYER        │
├──────────────────────────────┼─────────────────────────────────┤
│ • OAuth 2.0/OIDC            │ • LangChain Controller           │
│ • JWT Validation            │ • Workflow Engine                │
│ • 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)   │
└─────────────┴──────────────┴──────────────┴─────────────────┘

🛠️ Technology Stack

Component	Technology	Version
Backend	Python, FastAPI	3.11+, 0.100+
AI/ML	LangChain, OpenRouter	0.1+, Latest
Vector DB	Qdrant	1.7+
Cache	Redis	7.0+
Message Queue	Kafka/AWS SQS	3.5+
Container	Docker	24+
Orchestration	Kubernetes	1.28+
Monitoring	Prometheus, Grafana	2.45+

📁 Project Structure

virtual_board_member/
├── app/                          # Main application code
│   ├── api/                     # API endpoints
│   ├── core/                    # Core configuration
│   ├── models/                  # Data models
│   ├── services/                # Business logic
│   └── utils/                   # Utility functions
├── services/                     # Microservices
│   ├── llm_service/             # LLM orchestration
│   ├── rag_service/             # RAG pipeline
│   ├── doc_processor/           # Document processing
│   └── analytics/               # Analytics service
├── tests/                       # Test suite
├── docker/                      # Docker configurations
├── k8s/                         # Kubernetes manifests
├── docs/                        # Documentation
└── scripts/                     # Utility scripts

🔧 Development

Running Tests

# Run all tests
poetry run pytest

# Run with coverage
poetry run pytest --cov=app --cov-report=html

# Run specific test file
poetry run pytest tests/test_document_processing.py

Code Quality

# Format code
poetry run black app/ tests/
poetry run isort app/ tests/

# Type checking
poetry run mypy app/

# Security scanning
poetry run bandit -r app/
poetry run safety check

Database Migrations

# Create new migration
poetry run alembic revision --autogenerate -m "Description"

# Apply migrations
poetry run alembic upgrade head

# Rollback migration
poetry run alembic downgrade -1

🚀 Deployment

Development

docker-compose -f docker-compose.dev.yml up -d

Staging

kubectl apply -f k8s/staging/

Production

kubectl apply -f k8s/production/

📊 Monitoring

Application Metrics: Prometheus + Grafana
Logs: ELK Stack (Elasticsearch, Logstash, Kibana)
Tracing: Jaeger with OpenTelemetry
Alerting: AlertManager with Slack/Email notifications

🔒 Security

Authentication: OAuth 2.0/OIDC with Auth0/AWS Cognito
Authorization: RBAC with attribute-based access control
Encryption: AES-256 at rest, TLS 1.3 in transit
Audit: Comprehensive logging of all operations
Compliance: GDPR, SOX, CCPA ready

📈 Performance

Response Time: < 5 seconds for 95% of queries
Throughput: 100+ concurrent users
Document Processing: 500+ documents/hour
Availability: 99.9% uptime target

🤝 Contributing

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

Documentation: docs/
Issues: GitHub Issues
Discussions: GitHub Discussions

🗺️ Roadmap

Advanced analytics dashboard
Mobile application
Advanced AI capabilities
Additional integrations
Performance optimizations

Built with ❤️ for better board governance