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Knowledge Graphs and LLMs in Action - Alessandro Negro with Vlastimil Kus, Giuseppe Futia and Fabio Montagna
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, you’ll discover the theory of knowledge graphs and learn how to build services that can demonstrate intelligent behavior. You’ll learn to create KGs from first principles and go hands-on to develop advisor applications for real-world domains like healthcare and finance.
·manning.com·
Knowledge Graphs and LLMs in Action - Alessandro Negro with Vlastimil Kus, Giuseppe Futia and Fabio Montagna
Building AI Agents with LLMs, RAG, and Knowledge Graphs: A practical guide to autonomous and modern AI agents
Building AI Agents with LLMs, RAG, and Knowledge Graphs: A practical guide to autonomous and modern AI agents
𝐁𝐨𝐨𝐤 𝐩𝐫𝐨𝐦𝐨𝐭𝐢𝐨𝐧 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐢𝐬 𝐨𝐧𝐞 𝐢𝐬 𝐰𝐨𝐫𝐭𝐡 𝐢𝐭.. 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐚𝐭 𝐢𝐭𝐬 𝐛𝐞𝐬𝐭.. This masterpiece was published by Salvatore Raieli and Gabriele Iuculano, and it is available for orders from today, and it's already a 𝐁𝐞𝐬𝐭𝐬𝐞𝐥𝐥𝐞𝐫! 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 𝘙𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭-𝘈𝘶𝘨𝘮𝘦𝘯𝘵𝘦𝘥 𝘎𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘰𝘯 (𝘙𝘈𝘎) 𝘢𝘯𝘥 𝘒𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 𝘎𝘳𝘢𝘱𝘩𝘴. 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
·linkedin.com·
Building AI Agents with LLMs, RAG, and Knowledge Graphs: A practical guide to autonomous and modern AI agents
The Developer's Guide to GraphRAG
The Developer's Guide to GraphRAG
Find out how to combine a knowledge graph with RAG for GraphRAG. Provide more complete GenAI outputs.
You’ve 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.   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 Developer’s Guide to GraphRAG. You’ll learn how to: Prepare a knowledge graph for GraphRAG Combine a knowledge graph with native vector search Implement three GraphRAG retrieval patterns
·neo4j.com·
The Developer's Guide to GraphRAG