Technical Architecture

A deep dive into the database design, workflows, and technical structure that powers V.E.T.S.

Database Architecture

Core Tables

Animals & Herds

  • Individual Animals: Complete profile including name, breed, registration, DNA, microchip
  • Herds/Groups: Logical groupings of animals with shared characteristics
  • Relationships: Lineage tracking (sire, dam, offspring)
  • Ownership: Historical and current ownership records

Physical Items

  • Inventory Management: Track quantity, location, condition
  • Item Types: Equipment, medications, feed, IoT devices
  • Associations: Link items to specific animals or herds
  • Transactions: Purchase, sale, usage history

Actions

  • Action Types: Veterinary, training, competition, husbandry
  • Type-Specific Tables: Custom data structures for each action type
  • Action Chains: Link actions in sequences (e.g., pre-op ? surgery ? post-op)
  • Scheduling: Planned vs. completed actions

Knowledge Base

  • Tree Structure: Hierarchical organization with parent-child relationships
  • Content Nodes: Store AI-generated and expert-validated information
  • Versioning: Track changes and improvements over time
  • Permissions: Control access at the node level

System Components

Data Layer

  • SQL Server relational database
  • Stored procedures for business logic
  • Views for complex queries
  • Triggers for data integrity

Application Layer

  • Web-based interface
  • RESTful API endpoints
  • Real-time collaboration
  • Mobile-responsive design

AI Integration

  • LLM content generation
  • Expert validation workflow
  • Content caching and reuse
  • Continuous learning

IoT Integration

  • Device registration
  • Real-time data streaming
  • Alert triggers
  • Data visualization

Key Design Patterns

1. Entity-Relationship Model

Every entity in V.E.T.S. has a unique identifier (GUID) that remains stable across the system lifetime. This allows for:

  • Reliable references even as data moves or changes
  • Merge and synchronization across systems
  • Audit trails and historical tracking

2. Tree Navigation Structure

The knowledge base uses a recursive tree structure where each node can have:

  • ParentRef: Immediate parent node
  • RootRef: Top-level ancestor for quick traversal
  • SortOrder: Position among siblings
  • Type: Defines behavior (container, webpage, action, etc.)

3. Extensible Action System

Actions are implemented through a base table plus type-specific extension tables:

  • Base Actions Table: Common fields (date, animal, user, etc.)
  • Type-Specific Tables: Additional fields for each action type
  • Custom Programming: Type-specific business logic and validations
  • Action Chains: Link related actions into sequences

4. Permission System

Granular control over who can see and do what:

  • Role-Based Access: Owners, veterinarians, trainers, staff
  • Entity-Level Permissions: Control access to specific animals or herds
  • Action Permissions: Who can perform or view specific action types
  • Knowledge Permissions: Control access to knowledge base sections

Data Flow Examples

Veterinary Procedure Workflow

  1. Schedule: Create task for upcoming procedure
  2. Prepare: System suggests required items and preparation steps
  3. Execute: Record procedure details in action-specific table
  4. Document: Attach photos, notes, findings
  5. Follow-up: System suggests next actions based on findings
  6. Knowledge: Link to relevant protocols and best practices

AI-Enhanced Knowledge Creation

  1. Request: User asks for information on a topic
  2. Check Cache: System looks for existing validated content
  3. Generate: If needed, LLM creates initial content
  4. Review: Expert reviews and edits the generated content
  5. Validate: Approved content saved to knowledge base
  6. Reuse: Future requests use the validated content

Performance & Scalability

  • Indexing Strategy: Optimized indexes on frequently queried fields
  • Caching: Intelligent caching of knowledge base content
  • Pagination: Large result sets loaded incrementally
  • Lazy Loading: Tree branches loaded on-demand
  • Async Processing: Heavy operations run in background

Security

  • Authentication: Secure user authentication and session management
  • Encryption: Data encrypted in transit and at rest
  • Audit Logging: Complete trail of who did what and when
  • Backup: Regular automated backups with point-in-time recovery
  • HIPAA Consideration: Architecture designed for compliance requirements
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