Modern search systems increasingly rely on various vector representations—dense embeddings for semantic similarity, sparse vectors for lexical matching, and behavioral vectors from collaborative filtering. But these vector spaces typically operate in isolation, forcing us to choose between different notions of relevance. This talk introduces the concept of "wormhole vectors"—a technique for dynamically connecting and traversing between heterogeneous vector spaces using retrieved documents as a bridge. Rather than running parallel searches and merging results (the most common hybrid search approach), we can start in one vector space (e.g., using sparse embeddings for keyword search), retrieve documents, and then derive wormhole vectors to instantly transport us to another space (e.g., using dense embeddings for semantic search) to continue searching with a fundamentally different notion of similarity. We'll explore practical uses of this approach across three vector spaces: dense semantic embeddings, sparse lexical/keyword vectors, and behavioral vectors from matrix factorization. We’ll demonstrate how document sets can dynamically materialize bridges between these vector spaces, enabling novel retrieval patterns like semantic → behavioral → lexical traversal that surface user intent patterns invisible to single-space approaches.

Apply cutting-edge machine learning techniques—from crowdsourced relevance and knowledge graph learning, to Large Language Models (LLMs)—to enhance the accuracy and relevance of your search results. Delivering effective search is one of the biggest challenges you can face as an engineer. AI-Powered Search is an in-depth guide to building intelligent search systems you can be proud of. It covers the critical tools you need to automate ongoing relevance improvements within your search applications. Inside you’ll learn modern, data-science-driven search techniques like: Semantic search using dense vector embeddings from foundation models Retrieval augmented generation (RAG) Question answering and summarization combining search and LLMs Fine-tuning transformer-based LLMs Personalized search based on user signals and vector embeddings Collecting user behavioral signals and building signals boosting models Semantic knowledge graphs for domain-specific learning Semantic query parsing, query-sense disambiguation, and query intent classification Implementing machine-learned ranking models (Learning to Rank) Building click models to automate machine-learned ranking Generative search, hybrid search, multimodal search, and the search frontier AI-Powered Search will help you build the kind of highly intelligent search applications demanded by modern users. Whether you’re enhancing your existing search engine or building from scratch, you’ll learn how to deliver an AI-powered service that can continuously learn from every content update, user interaction, and the hidden semantic relationships in your content. You’ll learn both how to enhance your AI systems with search and how to integrate large language models (LLMs) and other foundation models to massively accelerate the capabilities of your search technology. About the Technology Modern search is more than keyword matching. Much, much more. Search that learns from user interactions, interprets intent, and takes advantage of AI tools like large language models (LLMs) can deliver highly targeted and relevant results. This book shows you how to up your search game using state-of-the-art AI algorithms, techniques, and tools. About the Book AI-Powered Search teaches you to create a search that understands natural language and improves automatically the more it is used. As you work through dozens of interesting and relevant examples, you’ll learn powerful AI-based techniques like semantic search on embeddings, question answering powered by LLMs, real-time personalization, and Retrieval Augmented Generation (RAG). What's Inside Sparse lexical and embedding-based semantic search Question answering, RAG, and summarization using LLMs Personalized search and signals boosting models Learning to Rank, multimodal, and hybrid search About the Reader For software developers and data scientists familiar with the basics of search engine technology. About the Author Trey Grainger is the Founder of Searchkernel and former Chief Algorithms Officer and SVP of Engineering at Lucidworks. Doug Turnbull is a Principal Engineer at Reddit and former Staff Relevance Engineer at Spotify. Max Irwin is the Founder of Max.io and former Managing Consultant at OpenSource Connections. Quotes Belongs on the shelf of every search practitioner! - Khalifeh AlJadda, Google A treasure map! Now you have decades of semantic search knowledge at your fingertips. - Mark Moyou, NVIDIA Modern and comprehensive! Everything you need to build world-class search experiences. - Kelvin Tan, SearchStax Kick starts your ability to implement AI search with easy to understand examples. - David Meza, NASA

Solr in Action is a comprehensive guide to implementing scalable search using Apache Solr. This clearly written book walks you through well-documented examples ranging from basic keyword searching to scaling a system for billions of documents and queries. It will give you a deep understanding of how to implement core Solr capabilities. About the Technology About the Book Whether you're handling big (or small) data, managing documents, or building a website, it is important to be able to quickly search through your content and discover meaning in it. Apache Solr is your tool: a ready-to-deploy, Lucene-based, open source, full-text search engine. Solr can scale across many servers to enable real-time queries and data analytics across billions of documents. Solr in Action teaches you to implement scalable search using Apache Solr. This easy-to-read guide balances conceptual discussions with practical examples to show you how to implement all of Solr's core capabilities. You'll master topics like text analysis, faceted search, hit highlighting, result grouping, query suggestions, multilingual search, advanced geospatial and data operations, and relevancy tuning. What's Inside How to scale Solr for big data Rich real-world examples Solr as a NoSQL data store Advanced multilingual, data, and relevancy tricks Coverage of versions through Solr 4.7 About the Reader This book assumes basic knowledge of Java and standard database technology. No prior knowledge of Solr or Lucene is required. About the Authors Trey Grainger is a director of engineering at CareerBuilder. Timothy Potter is a senior member of the engineering team at LucidWorks. The authors work on the scalability and reliability of Solr, as well as on recommendation engine and big data analytics technologies. Quotes The knowledge and techniques you need. - From the Foreword by Yonik Seeley, Creator of Solr Readable and immediately applicable ... an excellent book. - John Viviano, InterCorp, Inc. The go-to guide for Solr ... a definitive resource for both beginners and experts. - Scott Anthony, Business Instruments A well-dosed combination of deep technical knowledge and real-world experience. - Alexandre Madurell, Piksel, Inc.