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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
The codebase for the book "AI-Powered Search" (Manning Publications, 2025) and associated "AI-Powered Search: Modern Retrieval for Humans & Agents" Maven course
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| 2026-07-24 | 400 |
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| 2026-08-06 | 402 |
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# AI-Powered Search Code examples for the book [_AI-Powered Search_](https://aipoweredsearch.com) by [Trey Grainger](https://www.linkedin.com/in/treygrainger/), [Doug Turnbull](https://www.linkedin.com/in/softwaredoug/), and [Max Irwin](https://www.linkedin.com/in/maxirwin/). Published by [Manning Publications](https://www.manning.com). <br/> <a href="https://aipoweredsearch.com/the-book?origin=code-readme"><img src="https://aipoweredsearch.com/images/book-cover.jpg?origin=code-readme" width="250px"></a> ## Book Overview [_AI-Powered Search_](https://aipoweredsearch.com) teaches you the latest machine learning techniques to build search engines that continuously learn from your users and your content to drive more domain-aware and intelligent search. Search engine technology is rapidly evolving, with Artificial Intelligence (AI) driving much of that innovation. Crowdsourced relevance and the integration of large language models (LLMs) like GPT and other foundation models are massively accelerating the capabilities and expectations of search technology. AI-Powered Search will teach you 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. ## How to run For simplicity of setup, all code is shipped in Jupyter Notebooks and packaged in Docker containers. This means that installing Docker and then pulling (or building) and running the book's Docker containers is the only necessary setup. [Appendix A](https://livebook.manning.com/book/ai-powered-search/appendix-a?origin=code-base) of the book provides full step-by-step instructions for running the code examples, but you can run the following to get up and running quickly: If you haven't already pulled the source code locally, run: ``` git clone https://github.com/treygrainger/ai-powered-search.git ``` Then, to build and start the codebase with interactive Jupyter notebooks, run: ``` cd ai-powered-search docker compose up ``` That's all it takes! Once the containers are built and running (this may take a while, especially on the first build), visit: `http://localhost:8888` to launch the [Welcome](chapters/welcome.ipynb) notebook and see a Table of Contents for all the live code examples from throughout the book. <img src="https://aipoweredsearch.com/images/welcome.ipynb.jpg?origin=code-readme" width="1024px"></a> ## Supported Technologies AI-Powered Search teaches many modern search techniques leveraging machine learning approaches. While we utilize specific technologies to demonstrate concepts, most techniques are applicable to many modern search engines and vector databases. Throughout the book, all code examples are in **Python**, with **PySpark** (the Python interface to **Apache Spark**) being utilized heavily for data processing tasks. The default search engine leveraged by the book's examples is **Apache Solr**, but most examples are abstracted away from the particular search engine, and swappable implementation will be soon available for most popular search engines and vector databases. For more information about the search engi
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matched fp:ccd2a3f5a148b255, topic:vector-database, topic:vector-search, topic:semantic-search
matched fp:ccd2a3f5a148b255, topic:large-language-models, topic:foundation-models