Knowledge Graph Enhanced Rag
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Essential Graphrag
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Author : Tomaž Bratanic
language : en
Publisher: Simon and Schuster
Release Date : 2025-08-19
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 2025-08-19 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
Knowledge Graph Enhanced Rag
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Author : Calissa Corinne
language : en
Publisher: Independently Published
Release Date : 2025-11-25
Knowledge Graph Enhanced Rag written by Calissa Corinne and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-25 with Computers categories.
The era of simple vector search is ending. Modern AI systems demand retrieval that is structured, explainable, multi-hop, and capable of reasoning across relationships-not just matching embeddings. This book shows you how to build the next generation of Retrieval-Augmented Generation (RAG): systems enhanced with knowledge graphs, hybrid indexing, graph traversal, and agentic AI workflows that plan, retrieve, reason, and explain. Knowledge-Graph Enhanced RAG is the definitive, hands-on guide for developers, engineers, data scientists, and AI practitioners who want to build retrieval systems that outperform standard RAG in accuracy, reasoning ability, reliability, and transparency. You will learn how to construct high-quality knowledge graphs from real data, integrate them into vector-based retrieval pipelines, design multi-hop reasoning workflows, and deploy advanced agentic systems that use graph structures to guide decisions. Through practical explanations, step-by-step implementations, real code examples, and industry-grade mini-projects, this book teaches you not just how Graph-RAG works-but how to build it yourself. You will see how to extract entities, relationships, and schemas from documents; design graph databases with Neo4j, Memgraph, and many more; create hybrid retrieval pipelines using LangChain and LlamaIndex; apply graph-guided planning for complex queries; and deploy end-to-end solutions for healthcare, law, finance, cybersecurity, enterprise automation, and scientific research. Whether you are building AI copilots, domain-specific expert systems, enterprise knowledge assistants, reasoning-driven chatbots, or large-scale information architectures, this book gives you the frameworks, tooling, and mental models required to build systems that think in structure, not just text. What You Will Learn - How to design high-quality knowledge graphs that unlock multi-hop reasoning, context precision, and transparent retrieval - How to build complete Graph-RAG pipelines that combine vector search, graph traversal, and LLM synthesis - How to extract entities, relations, and canonicalized concepts from real documents using LLMs and rule-based tools - How to structure ontologies, taxonomies, and schemas for scalable domain modeling - How to use Neo4j, Memgraph, ArangoDB, and AWS Neptune for production-ready graph storage - How to write queries with Cypher, SPARQL, Gremlin, and emerging GQL standards - How to implement hybrid retrieval architectures and two-layer indexing for high-accuracy answers - How to build intelligent agents that plan retrieval steps, call tools, and traverse graphs autonomously - How to evaluate Graph-RAG systems using faithfulness, multi-hop consistency, and context-coverage metrics - How real companies use Graph-RAG across healthcare, legal, finance, cybersecurity, and research domains Who This Book Is For - AI developers and engineers building advanced RAG applications - Enterprise teams building internal knowledge systems or AI copilots - Data scientists and ML researchers exploring graph-structured reasoning - Students and professionals entering the agentic AI and RAG ecosystem Why This Book Matters Traditional RAG is useful but shallow. It retrieves isolated text chunks-often inconsistent, redundant, or lacking semantic structure-leaving LLMs to guess the connections. Graph-RAG fixes this by adding knowledge graphs, relationships, hierarchies, (entity, relation) triples, and reasoning paths that guide retrieval with precision and interpretability. This book shows you how to make that leap: from simple embedding search to structured, reasoning-driven retrieval systems powered by graph intelligence and agentic planning. A Complete, Hands-On Guide to the Future of Retrieval-Augmented AI
Graph Enhanced Retrieval Augmented Generation
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Author : Dwayne Daniel
language : en
Publisher: Independently Published
Release Date : 2025-08-30
Graph Enhanced Retrieval Augmented Generation written by Dwayne Daniel and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-30 with Computers categories.
Graph-Enhanced Retrieval-Augmented Generation: Building Explainable, Knowledge-Graph Powered RAG Systems for Smarter AI Reasoning What if your AI could not only retrieve information but also explain its reasoning in a way professionals can trust? As enterprises demand more from AI than raw predictions, the future lies in systems that combine the flexibility of Retrieval-Augmented Generation with the structure and transparency of knowledge graphs. This book is a practical and comprehensive guide to building Graph-Enhanced RAG systems-AI architectures that combine semantic vector search with graph-based reasoning for explainability, scalability, and smarter decision support. It is written for developers, data scientists, and AI practitioners who want to move beyond black-box models and design systems that are traceable, compliant, and enterprise-ready. Whether you work in healthcare, finance, law, or any field where reasoning chains matter, this book will equip you with both the technical foundations and the applied strategies to build systems that professionals can rely on. What sets this book apart? Unlike standard RAG resources that focus only on vector search, this book integrates the power of graphs throughout its chapters: Foundations of RAG: Understand its strengths and why explainability is essential. Knowledge Graphs as Engines of Reasoning: Explore ontologies, entities, and relationships that add structure to AI. Constructing Knowledge Graphs for RAG: Step-by-step examples and code for building and populating graphs. Graph Databases and Query Languages: Practical patterns with Cypher and SPARQL. Hybrid Retrieval Strategies: Learn how to fuse vector search with graph reasoning for richer context. Building Graph-Enhanced Pipelines: Architectures, integration techniques, and end-to-end examples. Explainability and Provenance: Techniques for traceability and human interpretation of reasoning chains. Domain-Specific Applications: Real-world use cases in healthcare, finance, and law. Advanced Topics: Graph embeddings, ontology-driven prompting, and scaling with distributed architectures. Deployment and Security: Guidance for cloud integration, monitoring, and compliance frameworks. Every chapter blends deep explanations, real-world insights, and reusable code snippets, making it practical for both experimentation and production. If you are ready to build AI systems that are not only intelligent but also explainable and trusted, this book is your essential guide. Equip yourself with the strategies, code patterns, and best practices to design knowledge-graph powered RAG pipelines that scale, comply, and deliver smarter reasoning. Add this book to your library today and take the next step toward building the future of explainable AI.
Knowledge Graphs Rag
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Author : MAXIME. LANE
language : en
Publisher: Independently Published
Release Date : 2025-02-03
Knowledge Graphs 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 2025-02-03 with Computers categories.
Knowledge Graphs RAG: A Practical Guide to Designing and Implementing Graph-Based Systems Unlock the full potential of interconnected data with Knowledge Graphs RAG: A Practical Guide to Designing and Implementing Graph-Based Systems. This comprehensive guide is your gateway to the cutting-edge world of graph-based technologies and Retrieval-Augmented Generation (RAG). Whether you're a data scientist, software engineer, or AI enthusiast, this book provides step-by-step insights into building, implementing, and optimizing knowledge graphs and graph-enhanced RAG systems. Dive deep into the fundamentals of knowledge graphs, explore advanced techniques for integrating large language models (LLMs) with graph data, and discover how to create dynamic, contextually enriched responses using graph RAG approaches. From mastering the concepts of llm knowledge graph integration to understanding graph rag strategies, this book covers it all. Inside, you'll learn how to: Build and leverage knowledge graphs: Understand the theory and practical applications behind knowledge graphs, and learn how to create knowledge graph-enhanced RAG systems that drive intelligent decision-making. Integrate RAG with graph data: Discover how to implement knowledge graphs rag and graph rag solutions that combine traditional graph theory with state-of-the-art large language models. Master graphrag techniques: Gain expert insights into graphrag mastery and learn the secrets of mastering graphrag to build scalable, high-performance systems. Enhance search and recommendation: Use graph rag strategies to elevate your search relevance and recommendation engines, delivering personalized user experiences that adapt in real time. Explore real-world applications: From enterprise knowledge graphs to digital transformation and beyond, see how graph rag books are revolutionizing industries by connecting data in powerful new ways. Whether you're interested in graph rag, large language models graph rag, or simply want to become proficient in knowledge graph-enhanced RAG, this book is the ultimate resource for you. It seamlessly combines theory with practical applications and hands-on projects, making it a must-have for anyone looking to stay ahead in the rapidly evolving landscape of graph-based AI. Embrace the future of data with Knowledge Graphs RAG: A Practical Guide to Designing and Implementing Graph-Based Systems and join the ranks of innovators who are shaping tomorrow's technology today.
Scaling Graph Learning For The Enterprise
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Author : Ahmed Menshawy
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2025-08-06
Scaling Graph Learning For The Enterprise written by Ahmed Menshawy and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-06 with Computers categories.
Tackle the core challenges related to enterprise-ready graph representation and learning. With this hands-on guide, applied data scientists, machine learning engineers, and practitioners will learn how to build an E2E graph learning pipeline. You'll explore core challenges at each pipeline stage, from data acquisition and representation to real-time inference and feedback loop retraining. Drawing on their experience building scalable and production-ready graph learning pipelines, the authors take you through the process of building robust graph learning systems in a world of dynamic and evolving graphs. Understand the importance of graph learning for boosting enterprise-grade applications Navigate the challenges surrounding the development and deployment of enterprise-ready graph learning and inference pipelines Use traditional and advanced graph learning techniques to tackle graph use cases Use and contribute to PyGraf, an open source graph learning library, to help embed best practices while building graph applications Design and implement a graph learning algorithm using publicly available and syntactic data Apply privacy-preserving techniques to the graph learning process
Mastering Graph Rag Architecture
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Author : Robertto Tech
language : en
Publisher: Independently Published
Release Date : 2025-11-26
Mastering Graph Rag Architecture written by Robertto Tech and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-26 with Computers categories.
Unlock the full potential of Retrieval-Augmented Generation (RAG) systems with Mastering Graph-RAG Architecture, the definitive hands-on guide for advanced AI engineers, developers, and data scientists. This book takes you beyond theory, providing a practical roadmap to building scalable, knowledge graph-enhanced agentic AI systems powered by LLMs, vector search, and MCP. Inside, you will learn how to: Architect robust Graph-RAG pipelines capable of handling complex, multi-step tasks. Integrate LLMs with knowledge graphs for precise reasoning and contextual retrieval. Design production-ready systems with checkpointing, error handling, and human-in-the-loop controls. Implement vector search engines with FAISS, Pinecone, or Weaviate for high-performance retrieval. Deploy agentic AI safely and efficiently in real-world workflows, including automation, research assistance, and enterprise applications. Packed with runnable Python examples, line-by-line commentary, and operational best practices, this book equips you with the tools to confidently build, test, and deploy next-generation AI systems. Whether you're orchestrating multiple agents, implementing verification pipelines, or scaling knowledge-intensive applications, Mastering Graph-RAG Architecture delivers the expertise you need to succeed in the fast-evolving AI landscape. Take your AI engineering skills to the next level-master Graph-RAG architecture and build intelligent systems that are scalable, reliable, and production-ready.
A Simple Guide To Retrieval Augmented Generation
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Author : Abhinav Kimothi
language : en
Publisher: Simon and Schuster
Release Date : 2025-07-01
A Simple Guide To Retrieval Augmented Generation written by Abhinav Kimothi 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 2025-07-01 with Computers categories.
Everything you need to know about Retrieval Augmented Generation in one human-friendly guide. Augmented Generation—or RAG—enhances an LLM’s available data by adding context from an external knowledge base, so it can answer accurately about proprietary content, recent information, and even live conversations. RAG is powerful, and with A Simple Guide to Retrieval Augmented Generation, it’s also easy to understand and implement! In A Simple Guide to Retrieval Augmented Generation you’ll learn: • The components of a RAG system • How to create a RAG knowledge base • The indexing and generation pipeline • Evaluating a RAG system • Advanced RAG strategies • RAG tools, technologies, and frameworks A Simple Guide to Retrieval Augmented Generation gives an easy, yet comprehensive, introduction to RAG for AI beginners. You’ll go from basic RAG that uses indexing and generation pipelines, to modular RAG and multimodal data from images, spreadsheets, and more. About the Technology If you want to use a large language model to answer questions about your specific business, you’re out of luck. The LLM probably knows nothing about it and may even make up a response. Retrieval Augmented Generation is an approach that solves this class of problems. The model first retrieves the most relevant pieces of information from your knowledge stores (search index, vector database, or a set of documents) and then generates its answer using the user’s prompt and the retrieved material as context. This avoids hallucination and lets you decide what it says. About the Book A Simple Guide to Retrieval Augmented Generation is a plain-English guide to RAG. The book is easy to follow and packed with realistic Python code examples. It takes you concept-by-concept from your first steps with RAG to advanced approaches, exploring how tools like LangChain and Python libraries make RAG easy. And to make sure you really understand how RAG works, you’ll build a complete system yourself—even if you’re new to AI! What’s Inside • RAG components and applications • Evaluating RAG systems • Tools and frameworks for implementing RAG About the Readers For data scientists, engineers, and technology managers—no prior LLM experience required. Examples use simple, well-annotated Python code. About the Author Abhinav Kimothi is a seasoned data and AI professional. He has spent over 15 years in consulting and leadership roles in data science, machine learning and AI, and currently works as a Director of Data Science at Sigmoid. Table of Contents Part 1 1 LLMs and the need for RAG 2 RAG systems and their design Part 2 3 Indexing pipeline: Creating a knowledge base for RAG 4 Generation pipeline: Generating contextual LLM responses 5 RAG evaluation: Accuracy, relevance, and faithfulness Part 3 6 Progression of RAG systems: Naïve, advanced, and modular RAG 7 Evolving RAGOps stack Part 4 8 Graph, multimodal, agentic, and other RAG variants 9 RAG development framework and further exploration
Advanced Intelligent Computing Technology And Applications
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Author : De-Shuang Huang
language : en
Publisher: Springer Nature
Release Date : 2025-07-21
Advanced Intelligent Computing Technology And Applications written by De-Shuang Huang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-21 with Computers categories.
The 12-volume set CCIS 2564-2575, together with the 28-volume set LNCS/LNAI/LNBI 15842-15869, constitutes the refereed proceedings of the 21st International Conference on Intelligent Computing, ICIC 2025, held in Ningbo, China, during July 26-29, 2025. The 523 papers presented in these proceedings books were carefully reviewed and selected from 4032 submissions. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was "Advanced Intelligent Computing Technology and Applications".
Proceedings Of 20th Iberian Conference On Information Systems And Technologies Cisti 2025
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Author : Alvaro Rocha
language : en
Publisher: Springer Nature
Release Date : 2026-01-01
Proceedings Of 20th Iberian Conference On Information Systems And Technologies Cisti 2025 written by Alvaro Rocha and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-01-01 with Computers categories.
This book comprises peer-reviewed papers selected for presentation and discussion at the 20th Iberian Conference on Information Systems and Technologies (CISTI'2025), held from June 16 to 19, 2025, at ISEG—Lisbon School of Economics and Management, University of Lisbon, Portugal. CISTI’2025 is a leading international forum that brings together researchers, practitioners, and industry experts to exchange the latest research findings, innovative solutions, emerging trends, professional experiences, and key challenges across various domains of information systems and technologies. The conference also emphasizes recent technological advancements and their practical applications. The book covers essential topics such as: A) organizational models and information systems; B) knowledge management and decision support systems; C) software systems, architectures, applications, and tools; D) computer networks, mobility, and pervasive systems; E) human-centered computing; F) health informatics; G) information technologies in education; and H) architecture and engineering of construction. The primary audience for this publication includes postgraduate students, researchers, and academics in information systems and technologies. It also serves as an essential reference for undergraduate students and professionals in related fields.
Lmplementing Knowledge Graph And Large Language Model Enhanced Retrieval Augmented Generation To Automate Technical Service Desk A Proof Of Concept
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Author : Tuong Vy Ly Ngoc
language : en
Publisher:
Release Date :
Lmplementing Knowledge Graph And Large Language Model Enhanced Retrieval Augmented Generation To Automate Technical Service Desk A Proof Of Concept written by Tuong Vy Ly Ngoc and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
'Traditional service desks are highly independent an human intervention and therefore, often face the challenge of inefficient knowledge management, especially due to skill shortages as well as the time and resource consuming process of ticket management. These challenges often result in escalations, delayed ticket resolutions and inconsistent service quality, which adversely impact customer satisfaction.This thesis explores the integration of artificial intelligence (AI) technologies, particularly Large Language Model (LLM), Retrieval-Augmented Generation (RAG), and Knowledge Graphs (KG), to address two use cases in technical service desk: ticket classification and ticket resolution suggestion. A proof-of-concept system is developed to automate these tasks for an Austrian IT service provider catering to Business-to-Business (B2B) banking clients. Using historical ticket data from the past 18 months, the proof-of-concept leverages the LLM Open AI GPT-4o to construct a knowledge base in the KG database (Graph DB)and embed this knowledge in the vector database (Weaviate).This provides a solid basis for the system to classify, retrieve the right information and generate the optimal answer in natural language for the user problem.For the first use case, a KG-enhanced LLM classifier is developed to automate the ticket classification process. This is identified as a multi-dass classification problem with a highly imbalanced dataset. The classifier demonstrates significant potential for automation,achieving the highest accuracy of 87% across four iterations. This is accomplished with a clean high-quality training and test dataset and by using techniques such as prompt tuning and few-shot leaning.The second use case employs a KG- and LLM-enhanced RAG system to provide real-time resolution suggestions for repetitive technical problems that do not require further expert assistance. The RAG 8ystem only delivers an average result of 42.9%, implying the needs of better strategies to improve its performance.The successful automation of these two use cases has the potential to reduce the workload for first-level support staff, allowing them to concentrate on solving more complex ticket problems. The proposed framework should enhance customer satisfaction by optimising resource allocation, accelerating the ticket management process and ultimately, lay the foundation for future AI-driven automation initiatives. However, while this approach promises significant benefits, it also involves the potential risk of reputation damage if the expected performance are not met