# OpenBB Integration Deployment Architecture

## Environment and Dependencies

### Base Requirements
- **Python Version**: 3.8 or higher
- **Core Dependencies**: As specified in `requirements.txt` (Streamlit, etc.)

### Optional OpenBB Dependency
- **OpenBB Library**: `openbb>=4.1.0`
- **Installation**: Not included in default `requirements.txt` to maintain lightweight base installation
- **Activation**: Install via `pip install "openbb>=4.1.0"` when needed

## Configuration Management

### Environment Variables
- **Standard Project Variables**: Managed through Doppler (RAPIDAPI_KEY, GOOGLE_API_KEY, etc.)
- **OpenBB Provider Variables**: 
  - For public data sources (like yfinance): No specific configuration required
  - For premium data sources (e.g., Polygon, FMP):
    - Variables are managed by OpenBB internally
    - Follow OpenBB documentation for provider-specific setup
    - Example: `POLYGON_API_KEY` for Polygon.io data

### Feature Flags
- **JIXIA_MEMORY_BACKEND**: When set to "cloudflare", enables Cloudflare AutoRAG as memory backend
- **GOOGLE_GENAI_USE_VERTEXAI**: When set to "TRUE", enables Vertex AI for memory bank

## Deployment Scenarios

### 1. Base Deployment (OpenBB Not Installed)
- **Characteristics**: 
  - Lightweight installation
  - Relies on existing RapidAPI-based perpetual engine
  - UI falls back to demo or synthetic data in OpenBB tab
- **Use Cases**: 
  - Minimal environment setups
  - Systems where OpenBB installation is not feasible
  - Development environments focusing on other features

### 2. Full Deployment (With OpenBB)
- **Characteristics**:
  - Enhanced data capabilities through OpenBB
  - Access to multiple data providers
  - Improved data quality and coverage
- **Use Cases**:
  - Production environments requiring comprehensive market data
  - Advanced financial analysis and debate scenarios
  - Integration with premium data sources

### 3. Hybrid Deployment (Selective Features)
- **Characteristics**:
  - Selective installation of OpenBB providers
  - Mix of OpenBB and perpetual engine data sources
  - Fallback mechanisms ensure continuous operation
- **Use Cases**:
  - Cost optimization by using free providers where possible
  - Gradual migration from perpetual engine to OpenBB
  - Testing new data sources without full commitment

## Containerization (Docker)

### Base Image
- Python 3.10-slim or equivalent

### Multi-Stage Build
1. **Builder Stage**:
   - Install build dependencies
   - Install Python dependencies
2. **Runtime Stage**:
   - Copy installed packages from builder
   - Copy application code
   - Install optional OpenBB dependencies if specified

### Docker Compose Configuration
- Service definitions for main application
- Optional service for database (if using persistent memory backends)
- Volume mounts for configuration and data persistence

## Cloud Deployment

### Google Cloud Platform (GCP)
- **App Engine**:
  - Standard environment with custom runtime
  - Environment variables configured through `app.yaml`
- **Cloud Run**:
  - Containerized deployment
  - Secrets managed through Secret Manager
- **Compute Engine**:
  - Full control over VM configuration
  - Persistent disks for data storage

### Considerations for Cloud Deployment
- **API Key Security**: 
  - Use secret management services (Google Secret Manager, Doppler)
  - Never store keys in code or environment files
- **Memory Backend Configuration**:
  - For Vertex AI Memory Bank: Configure GOOGLE_CLOUD_PROJECT_ID and authentication
  - For Cloudflare AutoRAG: Configure CLOUDFLARE_ACCOUNT_ID and API token
- **Scalability**:
  - Stateless application design allows horizontal scaling
  - Memory backends provide persistence across instances

## Memory Backend Integration

### Vertex AI Memory Bank (Default/Primary)
- **Activation**: Requires GOOGLE_GENAI_USE_VERTEXAI=true and proper GCP authentication
- **Dependencies**: `google-cloud-aiplatform` (installed with google-adk)
- **Deployment**: Requires GCP project with Vertex AI API enabled

### Cloudflare AutoRAG (Alternative)
- **Activation**: Requires JIXIA_MEMORY_BACKEND=cloudflare and Cloudflare credentials
- **Dependencies**: `aiohttp` (already in requirements)
- **Deployment**: Requires Cloudflare account with Vectorize and Workers AI enabled

## Monitoring and Observability

### Health Checks
- Application startup verification
- OpenBB availability check endpoint
- Memory backend connectivity verification

### Logging
- Structured logging for data access patterns
- Error tracking for failed data retrievals
- Performance metrics for data loading times

### Metrics Collection
- API usage counters (both RapidAPI and OpenBB)
- Fallback trigger rates
- Memory backend operation statistics

## Security Posture

### Data Security
- In-memory data processing
- No persistent storage of sensitive financial data
- Secure handling of API responses

### Access Control
- Streamlit authentication (if enabled)
- API key isolation per data provider
- Memory backend access controls through provider mechanisms

### Network Security
- HTTPS for all external API calls
- Outbound firewall rules for API endpoints
- Secure credential injection mechanisms

## Disaster Recovery and Business Continuity

### Data Source Redundancy
- Multiple API providers through OpenBB
- Fallback to perpetual engine when OpenBB fails
- Synthetic data generation for UI continuity

### Memory Backend Failover
- Local simulation mode when cloud backends are unavailable
- Graceful degradation of memory features

### Recovery Procedures
- Automated restart on critical failures
- Manual intervention procedures for configuration issues
- Rollback capabilities through version control

## Performance Optimization

### Caching Strategies
- OpenBB's internal caching mechanisms
- Streamlit's built-in caching for UI components
- Memory backend for persistent agent knowledge

### Resource Management
- Asynchronous data loading where possible
- Memory-efficient data structures
- Connection pooling for API requests

### Scaling Considerations
- Horizontal scaling for handling concurrent users
- Vertical scaling for memory-intensive operations
- Load balancing for distributed deployments