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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 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
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
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 .
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
The largest book retailer in Canada also offers toys, music, home décor, gifts and lifestyle products. What's Inside...Books, Magazines, CD’s and DVD’s, Toys and Gifts, Home Accents, Electronics, Baby’s and Children’s Section, Bath and Body, Kitchen and Bedroom, Stationary Located outside in the exterior plaza.
