# Virtual Board Member AI System

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

```bash
# 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
```bash
# 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
```bash
# 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
```bash
# 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
```bash
docker-compose -f docker-compose.dev.yml up -d
```

### Staging
```bash
kubectl apply -f k8s/staging/
```

### Production
```bash
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

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

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 🆘 Support

- **Documentation**: [docs/](docs/)
- **Issues**: [GitHub Issues](https://github.com/your-org/virtual-board-member/issues)
- **Discussions**: [GitHub Discussions](https://github.com/your-org/virtual-board-member/discussions)

## 🗺️ Roadmap

- [ ] Advanced analytics dashboard
- [ ] Mobile application
- [ ] Advanced AI capabilities
- [ ] Additional integrations
- [ ] Performance optimizations

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