Unlock AI Innovation: Risk-Free Vector Search for Existing Apps

This blog post demonstrates how to seamlessly integrate vector search into existing applications without the need for complete re-platforming. The author uses a simple recommendation engine example, combining cat image embeddings with TPCC purchase history data to recommend products based on visually similar cats. This showcases how AI functionalities can be added to existing apps using enhanced SQL syntax and APIs, highlighting the importance of testing database engines, vector indexes, and I/O subsystems under heavy concurrent workloads. The author emphasizes the low-hanging fruit of adding AI to existing infrastructure.