Название: AI-Powered Search (Final Release) Автор: Trey Grainger, Doug Turnbull, Max Irwin Издательство: Manning Publications Год: 2025 Страниц: 520 Язык: английский Формат: pdf (true) Размер: 35.8 MB
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.
• 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.
Foreword by Grant Ingersoll.
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: This book is for search engineers, software engineers, and data scientists who want to learn how to build cutting-edge search engines integrating the latest Machine Learning techniques to drive more domain-aware and intelligent search. The book also provides a thorough overview of AI-powered search for product managers and business leaders who may not be able to implement the techniques themselves, but who want to understand the possibilities and limitations of AI-powered search.
Technical readers who want to get the most out of this book can follow along with the Python code examples. Familiarity with SQL (Structured Query Language) syntax is assumed, as we’ve chosen to implement many of the data aggregations in this standardized representation when possible. A basic understanding of how search engines (such as Elasticsearch, Apache Solr, or OpenSearch) or vector databases work is also helpful, but not required.
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AI-Powered Search (Final Release) 
