Graph Rag For Ai Applications
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Graph Rag For Ai Applications
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Author : Kenneth Charette
language : en
Publisher: Independently Published
Release Date : 2025-12-04
Graph Rag For Ai Applications written by Kenneth Charette and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-04 with Computers categories.
In a world where AI systems increasingly rely on factual accuracy, contextual awareness, and explainability, Graph RAG for AI Applications introduces the definitive framework for integrating structured knowledge graphs with retrieval-augmented generation (RAG). This book provides a complete roadmap for building intelligent retrieval systems that can reason, learn, and evolve - bridging the gap between semantic search, graph intelligence, and large language models. Written by a seasoned AI systems engineer and author recognized for authoritative works on LangChain, LangGraph, and agentic AI frameworks, this book delivers depth, clarity, and practical wisdom. Every chapter reflects hands-on expertise drawn from real-world enterprise deployments and production-grade AI architectures, ensuring that what you learn is both authentic and field-tested. About the Technology: At the heart of this book is Graph RAG (Graph-based Retrieval-Augmented Generation) - a next-generation architecture that enhances LLMs with structured knowledge graphs. Unlike traditional vector-based RAG, which retrieves text fragments based on similarity, Graph RAG connects entities and relationships, enabling AI to reason contextually, explain its decisions, and reduce hallucinations. You'll explore technologies like Neo4j, LangGraph, FAISS, MCP, SPARQL, and Graph Neural Networks, learning how they come together to create a unified knowledge reasoning pipeline. From ingestion and graph construction to hybrid retrieval and orchestration, this book covers it all in practical, implementation-driven detail. What's Inside: A complete breakdown of how RAG evolved and how Graph RAG redefines intelligent retrieval. Hands-on tutorials on constructing, storing, and querying knowledge graphs. Working code examples integrating Neo4j, LangChain, and FAISS for hybrid retrieval. Step-by-step instructions for deploying scalable Graph RAG pipelines using Docker, FastAPI, and CI/CD workflows. Techniques for semantic enrichment, dynamic subgraph selection, and reasoning with Graph Neural Networks. Evaluation methods for factual grounding, latency management, and observability with LangSmith and Weights & Biases. A forward-looking exploration of self-updating knowledge systems and autonomous graph agents. Every concept is presented with crystal-clear explanations, real-world case studies, and verified code implementations - ensuring that you not only understand the theory but can build systems that work in production. Who This Book Is For: This book is written for AI engineers, data scientists, knowledge graph developers, and machine learning practitioners who want to go beyond simple vector search and build intelligent, context-aware AI systems. It's equally valuable for researchers, architects, and enterprise teams exploring explainable AI, knowledge integration, or next-generation retrieval workflows. Whether you're scaling enterprise AI or designing your first knowledge-aware assistant, this guide provides everything you need. Step into the future of intelligent retrieval. Learn how to make your AI systems think contextually, reason intelligently, and explain transparently. Start building Graph-Augmented AI applications that redefine what's possible with knowledge, structure, and language. Get your copy of Graph RAG for AI Applications today - and lead the new era of knowledge-integrated, reasoning-aware AI systems.
Graph Rag For Ai Applications
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Author : Damian Zion
language : en
Publisher: Independently Published
Release Date : 2025-11-07
Graph Rag For Ai Applications written by Damian Zion and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-07 with Computers categories.
This book turns hard-won patterns into repeatable frameworks. Every chapter includes runnable Python, Cypher, and API snippets, with guardrails (hop coverage, two-citation evidence, as-of dates) that make systems reliable-not just impressive demos. About the Technology Traditional RAG stalls on ambiguity, multi-hop reasoning, and governance. Graph RAG fuses knowledge graphs (entities, relations, time, authority) with vector retrieval so LLMs fetch the right context, explain their answers, and obey policy. You'll learn to scope queries with graphs, retrieve inside that scope with hybrid ranking, and construct compact, faithful prompts. What's Inside Architecture blueprints: context engine, session memory, caching, and eval gates Extraction & graph build: NER/RE pipelines, ontology/shape design, ingestion CI Hybrid retrieval: dense + sparse + graph priors, query planning, context ranking Faithfulness & safety: validators, evidence packs, constrained edit/abstain loops Multimodality: diagrams and tables as first-class evidence (captions & rowsets) Agents & planning: task graphs, preconditions/effects, policy-constrained execution Scaling & ops: latency budgets, snapshots/rollbacks, observability with OTel Graph-native tuning: path-conditioned prompts, lightweight LoRA adapters Who this book is for Developers & Data Scientists building production RAG features ML/Platform Engineers responsible for latency, cost, and reliability Architects & Tech Leads defining knowledge-centric AI roadmaps Researchers/Students seeking practical, evaluable techniques beyond demos LLMs alone are no longer a moat. Teams adopting knowledge-centric infrastructure are cutting tokens, raising faithfulness, and shipping features faster. If your org can't explain why an answer is true-or roll back a bad knowledge push-you're already behind. One bad answer can cost more than this book 100× over. These patterns reduce hallucinations, stabilize latency, and make audits trivial. Expect fewer tokens per answer, fewer incidents, and faster, safer deploys-because knowledge is versioned, measured, and portable. Build AI your stakeholders can trust. Grab Graph RAG for AI Applications now, wire up the Context Engine in your stack this week, and ship knowledge-aware features that are accurate, explainable, and production-ready.
Essential Graphrag
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Author : Tomaž Bratanic
language : en
Publisher: Simon and Schuster
Release Date : 2025-09-02
Essential Graphrag written by Tomaž Bratanic and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-02 with Computers categories.
Upgrade your RAG applications with the power of knowledge graphs. Retrieval Augmented Generation (RAG) is a great way to harness the power of generative AI for information not contained in a LLM’s training data and to avoid depending on LLM for factual information. However, RAG only works when you can quickly identify and supply the most relevant context to your LLM. Essential GraphRAG shows you how to use knowledge graphs to model your RAG data and deliver better performance, accuracy, traceability, and completeness. Inside Essential GraphRAG you’ll learn: • The benefits of using Knowledge Graphs in a RAG system • How to implement a GraphRAG system from scratch • The process of building a fully working production RAG system • Constructing knowledge graphs using LLMs • Evaluating performance of a RAG pipeline Essential GraphRAG is a practical guide to empowering LLMs with RAG. You’ll learn to deliver vector similarity-based approaches to find relevant information, as well as work with semantic layers, deliver agentic RAG, and generate Cypher statements to retrieve data from a knowledge graph. About the technology A Retrieval Augmented Generation (RAG) system automatically selects and supplies domain-specific context to an LLM, radically improving its ability to generate accurate, hallucination-free responses. The GraphRAG pattern employs a knowledge graph to structure the RAG’s input, taking advantage of existing relationships in the data to generate rich, relevant prompts. About the book Essential GraphRAG shows you how to build and deploy a production-quality GraphRAG system. You’ll learn to extract structured knowledge from text and how to combine vector-based and graph-based retrieval methods. The book is rich in practical examples, from building a vector similarity search retrieval tool and an Agentic RAG application, to evaluating performance and accuracy, and more. What's inside • Embeddings, vector similarity search, and hybrid search • Turning natural language into Cypher database queries • Microsoft’s GraphRAG pipeline • Agentic RAG About the reader For readers with intermediate Python skills and some experience with a graph database like Neo4j. About the author The author of Manning’s Graph Algorithms for Data Science and a contributor to LangChain and LlamaIndex, Tomaž Bratanic has extensive experience with graphs, machine learning, and generative AI. Oskar Hane leads the Generative AI engineering team at Neo4j. Table of Contents 1 Improving LLM accuracy 2 Vector similarity search and hybrid search 3 Advanced vector retrieval strategies 4 Generating Cypher queries from natural language questions 5 Agentic RAG 6 Constructing knowledge graphs with LLMs 7 Microsoft’s GraphRAG implementation 8 RAG application evaluation A The Neo4j environment Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.
Llm Graph Rag
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Author : MAXIME. LANE
language : en
Publisher: Independently Published
Release Date : 2025-02-05
Llm Graph Rag written by MAXIME. LANE and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-05 with Computers categories.
LLM Graph RAG: A Hands-On Guide to Building Advanced, Graph-Based Retrieval-Augmented Generation with LLMs Unlock the power of Graph-Based Retrieval-Augmented Generation (RAG) to build intelligent AI systems that retrieve, reason, and generate knowledge like never before! In the era of Large Language Models (LLMs), retrieval-augmented generation (RAG) has emerged as a game-changing technique to enhance accuracy, reduce hallucinations, and provide reliable responses. But what if we could go beyond traditional retrieval techniques and integrate the power of knowledge graphs and Graph Neural Networks (GNNs) for even deeper reasoning and richer knowledge representation? This comprehensive, hands-on guide takes you through the entire journey of Graph-Based RAG, from foundations to real-world applications. Whether you're an AI developer, machine learning researcher, data scientist, or knowledge engineer, this book equips you with the skills and tools to leverage knowledge graphs, advanced retrieval techniques, and multimodal AI architectures to build next-generation AI systems. What You'll Learn Inside This Book: Part I: Foundations of Graph-Based RAG ✔ The evolution of Retrieval-Augmented Generation (RAG) and why traditional approaches fall short. ✔ Introduction to graph theory, knowledge graphs, and their role in AI retrieval. ✔ How to build, query, and optimize graph databases (Neo4j, SPARQL, and Cypher). Part II: Building Graph-Based RAG Systems ✔ Understanding Graph Neural Networks (GNNs) and their application in retrieval. ✔ Implementing knowledge graph embeddings (Node2Vec, GraphSAGE, and GATs) for efficient search. ✔ Integrating GNNs with LLMs to enhance response accuracy and reasoning. Part III: Hands-On Implementation ✔ Setting up FAISS, PyTorch Geometric, and Neo4j to power Graph-Based RAG. ✔ End-to-end implementation of a knowledge-driven RAG pipeline. ✔ Deploying scalable Graph-Based RAG systems in cloud environments. Part IV: Advanced Topics & Future Directions ✔ Optimizing retrieval using hybrid methods (dense + sparse search). ✔ Exploring multimodal RAG with text, images, and video. ✔ Addressing bias, fairness, explainability, and ethical concerns in Graph-Based RAG. ✔ The future of LLMs, knowledge graphs, and AI-driven reasoning. Why This Book? ✅ Comprehensive & Up-to-Date - Covers the latest techniques in AI retrieval, knowledge graphs, and multimodal RAG. ✅ Hands-On & Practical - Includes fully explained code examples, real-world projects, and step-by-step tutorials. ✅ Real-World Applications - Explore use cases in healthcare, finance, research, and enterprise AI. ✅ Scalable & Production-Ready - Learn how to optimize, deploy, and scale Graph-Based RAG systems. Who Is This Book For? ✔ AI Developers & Engineers - Build advanced AI retrieval systems with knowledge graphs and LLMs. ✔ Machine Learning Practitioners - Improve retrieval quality using GNNs and vector search. ✔ Data Scientists & Researchers - Leverage Graph-Based RAG for data-intensive AI applications. ✔ NLP Enthusiasts - Enhance text retrieval and question-answering systems with graph-based reasoning. If you're looking to push the boundaries of Retrieval-Augmented Generation (RAG) and integrate the power of graphs and neural networks into AI-driven retrieval systems, this is the book you've been waiting for.
Artificial Intelligence Applications And Innovations
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Author : Ilias Maglogiannis
language : en
Publisher: Springer Nature
Release Date : 2025-06-23
Artificial Intelligence Applications And Innovations written by Ilias Maglogiannis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-23 with Computers categories.
This four-volume set constitutes the proceedings of the 21st IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2025, which was held in Limassol, Cyprus, during June 2025. The 123 full papers and 7 short papers were presented in this volume were carefully reviewed and selected from 303 submissions. They focus on ethical-moral AI aspects related to its Environmental impact, Privacy, Transparency, Bias, Discrimination and Fairness.
Graph Rag In Llms
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Author : RONALD. TAYLOR
language : en
Publisher: Independently Published
Release Date : 2025-01-20
Graph Rag In Llms written by RONALD. TAYLOR and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-20 with Computers categories.
Unlock the Power of Graph RAG and LLMs to Build Smarter, Scalable, and More Intelligent AI Systems Are you ready to master the cutting-edge technology reshaping the AI landscape? "Graph RAG in LLMs: A Practical Guide to Graph Retrieval-Augmented Generation for Large Language Models and NLP Experts" is your comprehensive resource for diving deep into the world of Graph Retrieval-Augmented Generation (Graph RAG) and its transformative integration with Large Language Models (LLMs). This expertly crafted guide offers a step-by-step journey through the concepts, tools, and techniques needed to harness the combined potential of graph-structured data and LLMs. Whether you're a data scientist, NLP expert, ML engineer, or an AI enthusiast eager to stay ahead in your field, this book will empower you with the knowledge and skills to create advanced AI systems. What You'll Learn: Foundations of Graph RAG: Understand the fundamentals of graph theory and how it integrates with LLMs for enhanced AI capabilities. Building Smarter Pipelines: Learn how to design, optimize, and implement scalable RAG pipelines to manage and retrieve complex, interconnected data. Advanced Use Cases: Explore real-world applications in healthcare, legal, e-commerce, and more, demonstrating the practical value of Graph RAG. MLOps for RAG Pipelines: Discover best practices for deploying and maintaining robust AI systems using modern MLOps architectures. Cutting-Edge Techniques: Dive into the latest advancements in Graph Neural Networks, multi-agent AI systems, multimodal RAG, and LLM prompt programming. Why This Book? This is more than just a technical manual-it's a comprehensive guide that blends foundational concepts with advanced strategies. The book features hands-on examples, detailed explanations, and expert insights to bridge the gap between theory and real-world application. You'll find Python code illustrations to build, debug, and scale Graph RAG pipelines, empowering you to create AI systems that are not only intelligent but also explainable and efficient. Who Is This Book For? AI Developers: Gain the skills to design smarter, context-aware systems with LLMs and graph data. NLP Practitioners: Enhance your language models with structured graph knowledge for better performance. Data Scientists & Engineers: Learn scalable methods for integrating graphs and LLMs in diverse applications. AI Enthusiasts: Discover the future of AI-driven innovation and stay ahead in this rapidly evolving field. Why Graph RAG Matters Graph Retrieval-Augmented Generation represents the next leap in AI technology, enabling systems to process vast, complex datasets with structured reasoning and contextual understanding. From powering intelligent chatbots to optimizing multi-agent systems and building explainable AI, Graph RAG is the cornerstone of the future. Take Your Expertise to the Next Level Packed with insights into Knowledge Graphs, Graph Neural Networks, Retrieval-Augmented Generation, and more, this book will arm you with everything you need to build smarter, scalable, and more adaptive AI systems. Get Your Copy Today Transform the way you design and deploy AI systems. Whether you're working on cutting-edge NLP solutions, building smarter pipelines, or preparing for the future of AI innovation, Graph RAG in LLMs is your essential guide. Don't wait-grab your copy now and take the first step toward mastering the future of AI.
Getting Started With The Graph Query Language Gql
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Author : Ricky Sun
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-08-22
Getting Started With The Graph Query Language Gql written by Ricky Sun and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-22 with Computers categories.
Learn how to build and query graph databases with this first comprehensive guide to ISO-standard GQL, featuring 50+ hands-on examples and a real-world case study that will change the way you work with connected data Free with your book: DRM-free PDF version + access to Packt's next-gen Reader* Key Features Go beyond theory and apply key concepts and syntax through interactive tutorials and practical examples via the GQL Playground Leverage advanced features of GQL to manipulate graph data efficiently Explore GQL applications in data analytics and discover how to leverage graph knowledge in real-world scenarios Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionGraph Query Language is becoming the go-to standard for graph databases, especially with its support for interconnected analytics and GenAI capabilities. This book comes from a team of industry veterans who know exactly how to break down the fundamental GQL concepts, graph terms, definitions, catalog systems, and everything that matters in actual work. You’ll get to grips with graph data types, value expressions, graph matching patterns, and modifying statements through practical GQL examples. Once you've got the basics down, you’ll tackle advanced GQL topics such as path modes, complex path matching patterns, shortest path queries, composite statements, session and transaction commands, and procedures. You’ll also learn to create extensions and understand the design of graph databases to solve industry issues. The authors cover techniques like property graphs to help you optimize your graph queries and offer insights into the future of GQL and graph technology. By the end of this book, you’ll confidently query and update graph data, run graph algorithms, create visualizations, and apply your learnings to a real-world use case of money flow analysis for assessing bank client behaviors and detecting transaction risks. *Email sign-up and proof of purchase requiredWhat you will learn Experiment with GQL syntax on GQL Playground, including MATCH, RETURN, INSERT, UPDATE, and DELETE Work with operators, functions, and variables in an organized fashion Become familiar with complex topics such as varying path matching modes, repeated variables, shortest path, procedures, and transactions Enhance execution speed through indexing or caching systems Understand how to manage access control effectively Tackle real-world issues with a case study focused on money transaction analytics Who this book is for This book is for graph database developers, DBAs, programmers, data engineers, and analysts who want to learn the new graph database standard GQL. A basic understanding of graph and relational databases, data models, knowledge of SQL basics, and programming will make the content easy to grasp. While it is designed to be accessible even if you don’t have a background in graph theory, familiarity with concepts like nodes, edges, relationships, and the distinction between directed and undirected graphs will enhance your learning experience.
Artificial Intelligence Concepts Techniques And Applications
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Author : Dr. Amir Barhoi
language : en
Publisher: Chyren Publication
Release Date : 2025-04-16
Artificial Intelligence Concepts Techniques And Applications written by Dr. Amir Barhoi and has been published by Chyren Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-16 with Antiques & Collectibles categories.
Ict Analysis And Applications
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Author : Simon Fong
language : en
Publisher: Springer Nature
Release Date : 2025-11-03
Ict Analysis And Applications written by Simon Fong and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-03 with Computers categories.
This book proposes new technologies and discusses future solutions for ICT design infrastructures, as reflected in high-quality papers presented at the 10th International Conference on ICT for Sustainable Development (ICT4SD 2025), held in Goa, India, on 17–19 July 2025. The book covers topics such as big data and data mining, data fusion, IoT programming toolkits and frameworks, green communication systems and network, use of ICT in smart cities, sensor networks and embedded system, network and information security, wireless and optical networks, security, trust, and privacy, routing and control protocols, cognitive radio and networks, and natural language processing. Bringing together experts from different countries, the book explores a range of central issues from an international perspective.
Knowledge Graphs And Llms In Action
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Author : Alessandro Negro
language : en
Publisher: Simon and Schuster
Release Date : 2025-11-11
Knowledge Graphs And Llms In Action written by Alessandro Negro and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-11 with Computers categories.
Combine knowledge graphs with large language models to deliver powerful, reliable, and explainable AI solutions. Knowledge graphs model relationships between the objects, events, situations, and concepts in your domain so you can readily identify important patterns in your own data and make better decisions. Paired up with large language models, they promise huge potential for working with structured and unstructured enterprise data, building recommendation systems, developing fraud detection mechanisms, delivering customer service chatbots, or more. This book provides tools and techniques for efficiently organizing data, modeling a knowledge graph, and incorporating KGs into the functioning of LLMs—and vice versa. 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 • Build knowledge graphs from unstructured data sources using LLMs • Use machine learning algorithms to complete your graphs and derive insights from it • Reason on the knowledge graph and build KG-powered RAG systems for LLMs In Knowledge Graphs and LLMs in Action, you’ll discover the theory of knowledge graphs then put them into practice with LLMs to build working intelligence systems. You’ll learn to create KGs from first principles, go hands-on to develop advisor applications for real-world domains like healthcare and finance, build retrieval augmented generation for LLMs, and more. About the technology Using knowledge graphs with LLMs reduces hallucinations, enables explainable outputs, and supports better reasoning. By naturally encoding the relationships in your data, knowledge graphs help create AI systems that are more reliable and accurate, even for models that have limited domain knowledge. About the book Knowledge Graphs and LLMs in Action shows you how to introduce knowledge graphs constructed from structured and unstructured sources into LLM-powered applications and RAG pipelines. Real-world case studies for domain-specific applications—from healthcare to financial crime detection—illustrate how this powerful pairing works in practice. You’ll especially appreciate the expert insights on knowledge representation and reasoning strategies. What's inside • Design knowledge graphs for real-world needs • Build KGs from structured and unstructured data • Apply machine learning to enrich, complete, and analyze graphs • Pair knowledge graphs with RAG systems About the reader For ML and AI engineers, data scientists, and data engineers. Examples in Python. About the author Alessandro Negro is Chief Scientist at GraphAware and author of Graph-Powered Machine Learning. Vlastimil Kus, Giuseppe Futia, and Fabio Montagna are seasoned ML and AI professionals specializing in Knowledge Graphs, Large Language Models, and Graph Neural Networks. Table of Contents Part 1 1 Knowledge graphs and LLMs: A killer combination 2 Intelligent systems: A hybrid approach Part 2 3 Create your first knowledge graph from ontologies 4 From simple networks to multisource integration Part 3 5 Extracting domain-specific knowledge from unstructured data 6 Building knowledge graphs with large language models 7 Named entity disambiguation 8 NED with open LLMs and domain ontologies Part 4 9 Machine learning on knowledge graphs: A primer approach 10 Graph feature engineering: Manual and semiautomated approaches 11 Graph representation learning and graph neural networks 12 Node classification and link prediction with GNNs Part 5 13 Knowledge graph–powered retrieval-augmented generation 14 Asking a KG questions with natural language 15 Building a QA agent with LangGraph