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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

Very excited to announce our next Search Meetup!

This time, I would like to use the opportunity that Doug Turnbull will be in town and discuss with him about issues related to offline search evaluation:

In my talk, I will discuss the impact of missing judgments on offline evaluation and ways of mitigating it, while Doug will somehow provocatively say "NDCG is overrated" and look into alternatives to offline evaluation.

• Doors will open at 18:30
• Talks and networking: 19:00 - 21:30

Speakers:

Doug Turnbull has been enthusiastic about search relevance since 2013. He co-authored Relevant Search and AI Powered Search. He created Quepid and Splainer for search relevance testing. He co-created the Elasticsearch Learning to Rank plugin with Wikimedia Foundation and Snagajob. Doug loves learning from other search practitioners, and hopes you'll bring inquisitive curiosity and experiences to this talk. Doug currently works at Reddit where he's helping bring Machine Learning to search. Recently Doug worked at Shopify to help improve merchant search attributed revenue by 19% year over year.. Doug spent 8 years consulting at dozens of organizations during his time as CTO at OpenSource Connections. Doug blogs about search and other topics at https://softwaredoug.com

René Kriegler has worked in search for almost 16 years, including on projects for some of the top 10 German e-commerce sites. He is co-founder and co-organiser of MICES (Mix-Camp E-commerce Search), an event that brings together the e-commerce search community each year. He created and maintains the Querqy open source library for query rewriting. René is co-initiator of the Chorus project – an open source software stack that combines Querqy with other powerful tools to build e-commerce search and to measure and improve search quality. He works as Director E-commerce at OpenSource Connections.

Many thanks to HeyJobs for hosting and sponsoring the event!

See you at the meetup! René Kriegler

PS: If you can't wait to meet with other search enthusiasts, 'Haystack - The Search Relevance Conference' will take place in Berlin on 20th/21st September.

Relevant Search demystifies relevance work. Using Elasticsearch, it teaches you how to return engaging search results to your users, helping you understand and leverage the internals of Lucene-based search engines. About the Technology Users are accustomed to and expect instant, relevant search results. To achieve this, you must master the search engine. Yet for many developers, relevance ranking is mysterious or confusing. About the Book Relevant Search demystifies the subject and shows you that a search engine is a programmable relevance framework. You'll learn how to apply Elasticsearch or Solr to your business's unique ranking problems. The book demonstrates how to program relevance and how to incorporate secondary data sources, taxonomies, text analytics, and personalization. In practice, a relevance framework requires softer skills as well, such as collaborating with stakeholders to discover the right relevance requirements for your business. By the end, you'll be able to achieve a virtuous cycle of provable, measurable relevance improvements over a search product's lifetime. What's Inside Techniques for debugging relevance Applying search engine features to real problems Using the user interface to guide searchers A systematic approach to relevance A business culture focused on improving search About the Reader For developers trying to build smarter search with Elasticsearch or Solr. About the Authors Doug Turnbull is lead relevance consultant at OpenSource Connections, where he frequently speaks and blogs. John Berryman is a data engineer at Eventbrite, where he specializes in recommendations and search. Quotes One of the best and most engaging technical books I’ve ever read. - From the Foreword by Trey Grainger, Author of "Solr in Action" Will help you solve real-world search relevance problems for Lucene-based search engines. - Dimitrios Kouzis-Loukas, Bloomberg L.P. An inspiring book revealing the essence and mechanics of relevant search. - Ursin Stauss, Swiss Post Arms you with invaluable knowledge to temper the relevancy of search results and harness the powerful features provided by modern search engines. - Russ Cam, Elastic