Build search engines powered by the latest machine learning techniques and large language models.AI-Powered Search shows you how to build cutting-edge search engines that continuously learn from both your users and your content and drive more domain-aware and intelligent search. Inside you'll learn modern, data-science-driven search techniques like:
Semantic search using dense vector embeddings from foundation models
Retrieval augmented generation
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
Implementing machine-learned ranking models (learning to rank)
Building click models to automate machine-learned ranking
Generative search, hybrid search, and the search frontier
Today's search engines are expected to be smart, understanding the nuances of natural language queries, as well as each user's preferences and context. This book empowers you to build search engines that take advantage of user interactions and the hidden semantic relationships in your content to automatically deliver better, more relevant search experiences. You'll even learn how to integrate large language models (LLMs) like GPT and other foundation models to massively accelerate the capabilities of your search technology. About the bookAI-Powered Search is a hands-on guide to applying leading-edge data science techniques to search. It teaches you how to build search engines that automatically understand the intent of your users' queries in order to deliver significantly more relevant search results. You'll use LLMs for embeddings, question answering, and results summarization, as well as learning how to fine tune them for the best results. Working through code in interactive notebooks, you'll deploy intelligent AI-powered search systems that deliver real-time personalization and contextual understanding of each user, domain, and query through a self-learning search platform that continuously learns from evolving content and user interactions. About the reader For software developers and data scientists familiar with the basics of search engine technology. About the authorTrey Grainger is the Founder of Searchkernel (AI-powered search), CTO of Presearch (decentralized web search), and former Chief Algorithms Officer and SVP of Engineering at Lucidworks (ecommerce, site, and enterprise search). Trey also co-authored Solr in Action (Manning 2014). Doug Turnbull is a Principal Engineer at Reddit (social discussion search), former Staff Relevance Engineer at Spotify (Ecommerce Search) and is the former Chief Technical Officer at OpenSource Connections. Doug also co-authored Relevant Search (Manning 2016). Max Irwin is the Founder of Max.io (AI model scaling) and former Managing Consultant at OpenSource Connections (search relevance consulting).
