The audiobook version of "Knowledge Graphs and LLMs in Action" is now available
๐ง Exciting news! The audiobook version of "Knowledge Graphs and LLMs in Action" is now available!
Are you busy but would love to learn how to build powerful and explainable AI solutions? No problem! Manning has just released the audio version of our book.
Now you can listen while you're:
- Running and training for your next marathon ๐
- Commuting to the office ๐
- Sitting in the parking lot waiting for your kids to finish their violin lesson ๐ป
Your schedule is packed, but that shouldn't stop you from mastering these powerful AI techniques.
Get your copy here: https://hubs.la/Q03MVhhk0
And don't forget to use discount code: lagraphs40 for 40% off!
Clever solutions for smart people.
The audiobook version of "Knowledge Graphs and LLMs in Action" is now available
Knowledge Graphs and LLMs in Action - Alessandro Negro with Vlastimil Kus, Giuseppe Futia and Fabio Montagna
Knowledge graphs help understand relationships between the objects, events, situations, and concepts in your data so you can readily identify important patterns and make better decisions. This book provides tools and techniques for efficiently labeling data, modeling a knowledge graph, and using it to derive useful insights.
In Knowledge Graphs and LLMs in Action you will learn how to:
Model knowledge graphs with an iterative top-down approach based in business needs
Create a knowledge graph starting from ontologies, taxonomies, and structured data
Use machine learning algorithms to hone and complete your graphs
Build knowledge graphs from unstructured text data sources
Reason on the knowledge graph and apply machine learning algorithms
Move beyond analyzing data and start making decisions based on useful, contextual knowledge. The cutting-edge knowledge graphs (KG) approach puts that power in your hands. In Knowledge Graphs and LLMs in Action, youโll discover the theory of knowledge graphs and learn how to build services that can demonstrate intelligent behavior. Youโll learn to create KGs from first principles and go hands-on to develop advisor applications for real-world domains like healthcare and finance.
โ Why I Wrote This Book?
In the past two to three years, we've witnessed a revolution. First with ChatGPT, and now with autonomous AI agents. This is only the beginning. In the years ahead, AI will transform not only how we work but how we live. At the core of this transformation lies a single breakthrough technology: large language models (LLMs). Thatโs why I decided to write this book.
This book explores what an LLM is, how it works, and how it develops its remarkable capabilities. It also shows how to put these capabilities into practice, like turning an LLM into the beating heart of an AI agent. Dissatisfied with the overly simplified or fragmented treatments found in many current books, Iโve aimed to provide both solid theoretical foundations and hands-on demonstrations. You'll learn how to build agents using LLMs, integrate technologies like retrieval-augmented generation (RAG) and knowledge graphs, and explore one of todayโs most fascinating frontiers: multi-agent systems. Finally, Iโve included a section on open research questions (areas where todayโs models still fall short, ethical issues, doubts, and so on), and where tomorrowโs breakthroughs may lie.
๐ง Who is this book for?
Anyone curious about LLMs, how they work, and how to use them effectively. Whether you're just starting out or already have experience, this book offers both accessible explanations and practical guidance. It's for those who want to understand the theory and apply it in the real world.
๐ Who is this book not for?
Those who dismiss AI as a passing fad or have no interest in what lies ahead. But for everyone else this book is for you. Because AI agents are no longer speculative. Theyโre real, and theyโre here.
A huge thanks to my co-author Gabriele Iuculano, and the Packt's team: Gebin George, Sanjana Gupta, Ali A., Sonia Chauhan, Vignesh Raju., Malhar Deshpande
#AI #LLMs #KnowledgeGraphs #AIagents #RAG #GenerativeAI #MachineLearning #NLP #Agents #DeepLearning
| 22 comments on LinkedIn
Building AI Agents with LLMs, RAG, and Knowledge Graphs: A practical guide to autonomous and modern AI agents
๐๐จ๐จ๐ค ๐ฉ๐ซ๐จ๐ฆ๐จ๐ญ๐ข๐จ๐ง ๐๐๐๐๐ฎ๐ฌ๐ ๐ญ๐ก๐ข๐ฌ ๐จ๐ง๐ ๐ข๐ฌ ๐ฐ๐จ๐ซ๐ญ๐ก ๐ข๐ญ.. ๐๐ ๐๐ง๐ญ๐ข๐ ๐๐ ๐๐ญ ๐ข๐ญ๐ฌ ๐๐๐ฌ๐ญ..
This masterpiece was published by Salvatore Raieli and Gabriele Iuculano, and it is available for orders from today, and it's already a ๐๐๐ฌ๐ญ๐ฌ๐๐ฅ๐ฅ๐๐ซ!
While many resources focus on LLMs or basic agentic workflows, what makes this book stand out is its deep dive into grounding LLMs with real-world data and action through the powerful combination of ๐๐ฆ๐ต๐ณ๐ช๐ฆ๐ท๐ข๐ญ-๐๐ถ๐จ๐ฎ๐ฆ๐ฏ๐ต๐ฆ๐ฅ ๐๐ฆ๐ฏ๐ฆ๐ณ๐ข๐ต๐ช๐ฐ๐ฏ (๐๐๐) ๐ข๐ฏ๐ฅ ๐๐ฏ๐ฐ๐ธ๐ญ๐ฆ๐ฅ๐จ๐ฆ ๐๐ณ๐ข๐ฑ๐ฉ๐ด.
This isn't just about building Agents; it's about building AI that reasons, retrieves accurate information, and acts autonomously by leveraging structured knowledge alongside advanced LLMs.
The book offers a practical roadmap, packed with concrete Python examples and real-world case studies, guiding you from concept to deployment of intelligent, robust, and hallucination-minimized AI solutions, even orchestrating multi-agent systems.
Order your copy here - https://packt.link/RpzGM
#AI #LLMs #KnowledgeGraphs #AIAgents #RAG #GenerativeAI #MachineLearning
Find out how to combine a knowledge graph with RAG for GraphRAG. Provide more complete GenAI outputs.
Youโve built a RAG system and grounded it in your own data. Then you ask a complex question that needs to draw from multiple sources. Your heart sinks when the answers you get are vague or plain wrong.ย ย
How could this happen?
Traditional vector-only RAG bases its outputs on just the words you use in your prompt. It misses out on valuable context because it pulls from different documents and data structures. Basically, it misses out on the bigger, more connected picture.
Your AI needs a mental model of your data with all its context and nuances. A knowledge graph provides just that by mapping your data as connected entities and relationships. Pair it with RAG to create a GraphRAG architecture to feed your LLM information about dependencies, sequences, hierarchies, and deeper meaning.
Check out The Developerโs Guide to GraphRAG. Youโll learn how to:
Prepare a knowledge graph for GraphRAG
Combine a knowledge graph with native vector search
Implement three GraphRAG retrieval patterns