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AI-Powered Search

By Trey Grainger

AI-Powered Search Computer Science

AI-Powered Search

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Description
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. Foreword by Grant Ingersoll. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. 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. Table of Contents Part 1 1 Introducing AI-powered search 2 Working with natural language 3 Ranking and content-based relevance 4 Crowdsourced relevance Part 2 5 Knowledge graph learning 6 Using context to learn domain-specific language 7 Interpreting query intent through semantic search Part 3 8 Signals-boosting models 9 Personalized search 10 Learning to rank for generalizable search relevance 11 Automating learning to rank with click models 12 Overcoming ranking bias through active learning Part 4 13 Semantic search with dense vectors 14 Question answering with a fine-tuned large language model 15 Foundation models and emerging search paradigms A Running the code examples B Supported search engines and vector database
Product details
Edition:
1
Number of Pages:
520
Release Date:
2025-01-28
Publication Date:
2025-02-14
Publisher:
Manning Publications
Languages:
Original: English
ISBN10:
161729697X
ISBN13:
9781617296970
GPSR Manufacturer Reference:
[email protected]
Weight:
940 g
Height:
186 cm
Width:
231 cm
Thickness:
35 cm
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