Database design

Database requirements

The requirements for the database aspect for the speech and vision recognition with RAG AI pattern focus on the following:

• The database is required for customers x86 workloads on the cloud.
• The database is required for container workloads.
• The database is required for AI including RAG, chat, and image and video analysis.

Enterprise application databases considerations

The Enterprise or bespoke applications require a backend database to store various entity data and their relations. Some of the essential aspects that an enterprise application would require include:

• Scalability: The database must be able to scale horizontally by adding more nodes and vertically by increasing the processing power as the application grows.
• High availability: The database must help ensure high availability by providing features like replication, load balancing, and automatic failover if node failure occurs.
• Security: The database must provide robust security features, including:
  • Authentication and authorization controls
  • Data encryption both at rest and in transit
  • Access control lists (ACLs) to restrict data access
• Performance: The database must be able to handle high volumes of read and write operations while maintaining low latency and high throughput.
• Data consistency: The database must help ensure data consistency across all nodes and replicas, with features like multi-version concurrency control and snapshot isolation.
• Backup and recovery: The database must provide robust backup and recovery mechanisms to help ensure business continuity if unexpected failures or errors occur.
• Compliance: The database must comply with relevant industry regulations and standards, such as GDPR, HIPAA, PCI-DSS, and SOX.

Design considerations for Vector databases

Enterprises are increasingly favoring the retrieval augmented generation (RAG) approach in generative AI workflows for its faster time-to-market, efficient inference, and reliable output, particularly in key use cases such as customer care, HR and talent. RAG helps ensure that the model is linked to the most current, reliable facts and that users have access to the model’s sources, so that its claims can be checked for accuracy. RAG is core to the ability to anchor large language models in trusted data to reduce model hallucinations.