AI-Powered Search

AI-Powered Search shows you how to build cutting-edge search engines that continuously learn from both your users and your content and drive more domain-aware and intelligent search.

Inside you’ll learn modern, data-science-driven search techniques like:

• Semantic search using dense vector embeddings from foundation models
• Retrieval augmented generation
• 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
• Implementing machine-learned ranking models (learning to rank)
• Building click models to automate machine-learned ranking
• Generative search, hybrid search, and the search frontier

Today’s search engines are expected to be smart, understanding the nuances of natural language queries, as well as each user’s preferences and context. This book empowers you to build search engines that take advantage of user interactions and the hidden semantic relationships in your content to automatically deliver better, more relevant search experiences. You’ll even learn how to integrate large language models (LLMs) like GPT and other foundation models to massively accelerate the capabilities of your search technology.

About the book

AI-Powered Search is a hands-on guide to applying leading-edge data science techniques to search. It teaches you how to build search engines that automatically understand the intent of your users’ queries in order to deliver significantly more relevant search results.

You’ll use LLMs for embeddings, question answering, and results summarization, as well as learning how to fine tune them for the best results. Working through code in interactive notebooks, you’ll deploy intelligent AI-powered search systems that deliver real-time personalization and contextual understanding of each user, domain, and query through a self-learning search platform that continuously learns from evolving content and user interactions.

About the reader

For software developers and data scientists familiar with the basics of search engine technology.

Trey Grainger is the Founder of Searchkernel (AI-powered search), CTO of Presearch (decentralized web search), and former Chief Algorithms Officer and SVP of Engineering at Lucidworks (ecommerce, site, and enterprise search). Trey also co-authored Solr in Action (Manning 2014).

Doug Turnbull is a Principal Engineer at Reddit (social discussion search), former Staff Relevance Engineer at Spotify (Ecommerce Search) and is the former Chief Technical Officer at OpenSource Connections. Doug also co-authored Relevant Search (Manning 2016).

Max Irwin is the Founder of Max.io (AI model scaling) and former Managing Consultant at OpenSource Connections (search relevance consulting).

Amazon page

AI-Powered Search MEAP V17
1. MEAP_VERSION_17
2. Welcome
3. 1_Introducing_AI-powered_search
4. 2_Working_with_natural_language
5. 3_Ranking_and_content-based_relevance
6. 4_Crowdsourced_relevance
7. 5_Knowledge_graph_learning
8. 6_Using_context_to_learn_domain-specific_language
9. 7_Interpreting_query_intent_through_semantic_search
10. 8_Signals_boosting_models
11. 10_Learning_to_rank_for_generalizable_search_relevance
12. 11_Automating_learning_to_rank_with_click_models
13. 12_Overcoming_bias_in_learned_relevance_models
14. 13_Semantic_search_with_dense_vectors
15. 14_Question_answering_with_a_fine-tuned_large_language_model
16. 15_Foundation_models_and_emerging_search_paradigms
17. Appendix._Running_the_code_examples