AI-Powered Search dives deep into techniques like semantic search query understanding, signals boosting and personalized search, learning to rank, knowledge graph learning, multimodal and hybrid search, vector embeddings, question answering, and generative AI techniques like Retrieval Augmented Generation (RAG), while retaining a data-driven approach familiar from the earlier book. Interactive notebooks are heavily used to demonstrate concepts and techniques, but unlike Relevant Search a more generic approach to technology is shown, making no assumptions about which underlying search engine will be used – a sensible approach, considering that there has been a recent explosion in available technologies. An associated Github project provides Dockerized example code and a growing list of ‘pluggable’ search engines. We expect the book to join Relevant Search on every search developer, search data scientist, and search product owner’s bookshelf as an essential reference and learning resource.
AI-Powered Search dives deep into techniques like semantic search query understanding, signals boosting and personalized search, learning to rank, knowledge graph learning, multimodal and hybrid search, vector embeddings, question…
Learn More