Making With Milvus AI-Infused Proptech for Personalized Real Estate Search

This post details how Beike used Milvus to build an AI-infused proptech platform for personalized real estate search. It explains the concept of vector similarity search, how Milvus manages vector data, and Beike's specific implementation. Beike converts property listings into feature vectors, indexes them in Milvus, and uses similarity search (cosine distance) to recommend similar homes based on user preferences and listing characteristics. The system achieves an average query time of 113ms on 3 million vectors and uses an A/B table switching mechanism for data updates.