{"product_id":"ai-powered-search-9781617296970","title":"AI-Powered Search","description":"\u003cb\u003eApply 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.\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003eDelivering effective search is one of the biggest challenges you can face as an engineer. \u003ci\u003eAI-Powered Search\u003c\/i\u003e 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. \u003cp\u003e\u003c\/p\u003eInside you'll learn modern, data-science-driven search techniques like: \u003cp\u003e\u003c\/p\u003e- Semantic search using dense vector embeddings from foundation models\u003cbr\u003e - Retrieval augmented generation (RAG)\u003cbr\u003e - Question answering and summarization combining search and LLMs\u003cbr\u003e - Fine-tuning transformer-based LLMs\u003cbr\u003e - Personalized search based on user signals and vector embeddings\u003cbr\u003e - Collecting user behavioral signals and building signals boosting models\u003cbr\u003e - Semantic knowledge graphs for domain-specific learning\u003cbr\u003e - Semantic query parsing, query-sense disambiguation, and query intent classification\u003cbr\u003e - Implementing machine-learned ranking models (Learning to Rank)\u003cbr\u003e - Building click models to automate machine-learned ranking\u003cbr\u003e - Generative search, hybrid search, multimodal search, and the search frontier \u003cp\u003e\u003c\/p\u003e \u003ci\u003eAI-Powered Search\u003c\/i\u003e 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. \u003cp\u003e\u003c\/p\u003e Foreword by \u003cb\u003eGrant Ingersoll\u003c\/b\u003e. \u003cp\u003e\u003c\/p\u003e Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the technology\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e 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. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the book\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003ci\u003eAI-Powered Search\u003c\/i\u003e 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). \u003cp\u003e\u003c\/p\u003e What's inside \u003cp\u003e\u003c\/p\u003e- Sparse lexical and embedding-based semantic search\u003cbr\u003e - Question answering, RAG, and summarization using LLMs\u003cbr\u003e - Personalized search and signals boosting models\u003cbr\u003e - Learning to Rank, multimodal, and hybrid search \u003cp\u003e\u003c\/p\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e For software developers and data scientists familiar with the basics of search engine technology. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the author\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003cb\u003eTrey Grainger\u003c\/b\u003e is the Founder of Searchkernel and former Chief Algorithms Officer and SVP of Engineering at Lucidworks. \u003cb\u003eDoug Turnbull\u003c\/b\u003e is a Principal Engineer at Reddit and former Staff Relevance Engineer at Spotify. \u003cb\u003eMax Irwin\u003c\/b\u003e is the Founder of Max.io and former Managing Consultant at OpenSource Connections. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e Part 1\u003cbr\u003e 1 Introducing AI-powered search\u003cbr\u003e 2 Working with natural language\u003cbr\u003e 3 Ranking and content-based relevance\u003cbr\u003e 4 Crowdsourced relevance\u003cbr\u003e Part 2\u003cbr\u003e 5 Knowledge graph learning\u003cbr\u003e 6 Using context to learn domain-specific language\u003cbr\u003e 7 Interpreting query intent through semantic search\u003cbr\u003e Part 3\u003cbr\u003e 8 Signals-boosting models\u003cbr\u003e 9 Personalized search\u003cbr\u003e 10 Learning to rank for generalizable search relevance\u003cbr\u003e 11 Automating learning to rank with click models\u003cbr\u003e 12 Overcoming ranking bias through active learning\u003cbr\u003e Part 4\u003cbr\u003e 13 Semantic search with dense vectors\u003cbr\u003e 14 Question answering with a fine-tuned large language model\u003cbr\u003e 15 Foundation models and emerging search paradigms\u003cbr\u003e A Running the code examples\u003cbr\u003e B Supported search engines and vector database\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eTrey Grainger\u003c\/b\u003e is the Chief Algorithms Officer at Lucidworks, the AI-powered search company that powers hundreds of the world's leading organizations. Trey co-authored Solr in Action and has over 12 years experience building semantic search engines, recommendation engines, real-time analytics systems, and leading related engineering and data science teams. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eDoug Turnbull\u003c\/b\u003e is Staff Relevance Engineer at Spotify and is the former Chief Technical Officer at OpenSource Connections. He is the co-author of the book \u003ci\u003eRelevant Search\u003c\/i\u003e, and contributed chapters 10-12 on \"Learning to Rank\", \"Automated Learning to Rank with Click Models\", and \"Overcoming Bias in Learned Relevance Models\". \u003cp\u003e\u003c\/p\u003e\u003cb\u003eMax Irwin\u003c\/b\u003e is a Managing Consultant at OpenSource Connections, a leading search relevance consultancy. Max contributed chapters 13-14 on \"Semantic Search with Dense Vectors\" and \"Question Answering and the Search Frontier\".\u003cbr\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":51182839398674,"sku":"9781617296970","price":64.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_0cfd2337-0387-46de-88ec-d492e434affb.jpg?v=1744448006","url":"https:\/\/surprise-castle.myshopify.com\/products\/ai-powered-search-9781617296970","provider":"Surprise Castle","version":"1.0","type":"link"}