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119019902
AI-Powered Search
90% of respondents would recommend this to a friend
Nº de artículo:
119019902
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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 modelsRetrieval augmented generation (RAG)Question answering and summarization combining search and LLMsFine-tuning transformer-based LLMsPersonalized search based on user signals and vector embeddingsCollecting user behavioral signals and building signals boosting modelsSemantic knowledge graphs for domain-specific learningSemantic query parsing, query-sense disambiguation, and query intent classificationImplementing machine-learned ranking models (Learning to Rank)Building click models to automate machine-learned rankingGenerative search, hybrid search, multimodal search, and the search frontierAI-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 technologyModern 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 bookAI-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 insideSparse lexical and embedding-based semantic searchQuestion answering, RAG, and summarization using LLMsPersonalized search and signals boosting modelsLearning to Rank, multimodal, and hybrid searchAbout the readerFor software developers and data scientists familiar with the basics of search engine technology.About the authorTrey 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 ContentsPart 11 Introducing AI-powered search2 Working with natural language3 Ranking and content-based relevance4 Crowdsourced relevancePart 25 Knowledge graph learning6 Using context to learn domain-specific language7 Interpreting query intent through semantic searchPart 38 Signals-boosting models9 Personalized search10 Learning to rank for generalizable search relevance11 Automating learning to rank with click models12 Overcoming ranking bias through active learningPart 413 Semantic search with dense vectors14 Question answering with a fine-tuned large language model15 Foundation models and emerging search paradigmsA Running the code examplesB Supported search engines and vector database
| Publisher | Manning Publications |
| Publication date | January 28, 2025 |
| Language | English |
| Print length | 520 pages |
| ISBN-10 | 161729697X |
| ISBN-13 | 978-1617296970 |
| Item Weight | 1.9 pounds (860 grams) |
| Dimensions | 7.38 x 1.18 x 9.25 inches (18.7 x 3 x 23.5 cm) |
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AI-Powered Search
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