How to Rerank Product Search Results with Milvus Function Chain

Semantic similarity can retrieve relevant results—but the closest vector match is not always the best result for your users or your business. In this video, we explore how Milvus Function Chain brings retrieval, feature processing, scoring, and re-ranking into a single search workflow. Using an e-commerce product search demo, you’ll learn how to: • Combine semantic similarity with price, ratings, freshness, clicks, and sales • Transform raw fields into useful ranking features • Use decay functions and weighted scoring • Apply an XGBoost model to produce a final business score • Reduce application-side ranking and orchestration work • Extend the same approach to news search, content platforms, and knowledge bases If your best result depends on more than semantic similarity, Function Chain helps you retrieve relevant candidates—and rank them according to what actually matters to your application. 🔗 Resources Demo source code: https://github.com/zc277584121/milvus3-demos/tree/main/demos/function-chain-rerank Milvus official website: https://milvus.io/ Milvus documentation: https://milvus.io/docs Milvus GitHub: https://github.com/milvus-io/milvus ⏱️ Chapters 00:00 Why Semantic Similarity Isn’t Enough 01:14 The Problem with Application-Side Re-ranking 01:48 What Is Milvus Function Chain? 02:16 E-commerce Ranking Demo 03:42 How Server-Side Ranking Works 04:18 Feature Engineering: Price, Freshness, and Popularity 05:15 Ranking with XGBoost 05:52 Building a Function Chain 06:05 Supported Scoring and Re-ranking Functions 07:40 Use Cases Beyond E-commerce 08:24 Key Takeaways If you found this demo useful, give the video a like and star the Milvus repository on GitHub. #Milvus #VectorDatabase #SemanticSearch #RAG #Reranking #MachineLearning #ProductSearch