Essential Graphrag
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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.
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
Health Information Processing Evaluation Track Papers
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Author : Yanchun Zhang
language : en
Publisher: Springer Nature
Release Date : 2025-05-14
Health Information Processing Evaluation Track Papers written by Yanchun Zhang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-14 with Medical categories.
This book constitutes the refereed proceedings of the 10th China Health Information Processing Conference, CHIP 2024, held in Fuzhou, China, November 15–17, 2024. The CHIP 2024 Evaluation Track proceedings include 19 full papers which were carefully reviewed and grouped into these topical sections: syndrome differentiation thought in Traditional Chinese Medicine; lymphoma information extraction and automatic coding; and typical case diagnosis consistency.
Applied Intelligence
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Author : De-Shuang Huang
language : en
Publisher: Springer Nature
Release Date : 2025-02-24
Applied Intelligence 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 2025-02-24 with Computers categories.
This 2-volume set CCIS 2387 + 2388 constitutes the proceedings of the Second International Conference on Applied Intelligence, ICAI 2024, held in Zhengzhou, China, during November 22-25, 2024. The 72 full papers presented in this proceedings were carefully reviewed and selected from 228 submissions. The papers cover a wide range on theoretical aspects of biomedical data modeling and mining; computer vision; and deep learning. They were organized in topical sections as follows: Part I: Biomedical data modeling and mining; information security; pattern recognition; Part II: Image Processing; intelligent data analysis and prediction; machine learning;
Computational Intelligence In Engineering Science
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Author : Ngoc Thanh Nguyen
language : en
Publisher: Springer Nature
Release Date : 2025-07-18
Computational Intelligence In Engineering Science written by Ngoc Thanh Nguyen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-18 with Computers categories.
This four-volume set constitutes the refereed proceedings of the First International Conference on on Computational Intelligence in Engineering Science, ICCIES 2025, in Ho Chi Minh City, Vietnam, during July 23–25, 2025. The 115 full papers presented in these proceedings were carefully reviewed and selected from 210 submissions. The papers are organized in the following topical sections: Part I: Machine Learning; Wireless Networks (6G) Part II: Computer Vision; Natural Language Processing Part III: Intelligent Systems; Internet of Things Part IV: Machine Learning; Control Systems
E Business Generative Artificial Intelligence And Management Transformation
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Author : Yiliu Paul Tu
language : en
Publisher: Springer Nature
Release Date : 2025-07-09
E Business Generative Artificial Intelligence And Management Transformation written by Yiliu Paul Tu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-09 with Computers categories.
The three-volume set LNBIP 549 - 551 constitutes the refereed proceedings of the 24th Wuhan International Conference on E-Business, WHICEB 2025, which was held in Guangzhou, China, during June 6-8, 2025. The total of 92 papers included in the proceedings was carefully reviewed and selected from 324 submissions. The papers have been organized in topical sections as follows: Part I: Artificial Intelligence and New Ways of Working; Conversational Artificial Intelligence and Information Behavior; Data Analytics and Digital Governance; Data Intelligence and Social Computing on Digital Platforms; Digital Enablement and Digital Governance; Digital Innovation and Social Impact; Part II: Digital Technologies for Sustainable Development; Disruptive Technologies and Digital Transformation; E-business Strategy and Online Marketing; Emerging e-Commerce Initiatives Enables by Advanced Technologies; Engaging Technologies; Part III: Generative AI-enhanced Risk Analytics and Modelling; Healthcare Service and IT Management; Human-AI Integration in Organizations; Next-Gen Technologies and Social Commerce; Privacy and Security in Artificial Intelligence Generated Content; Transformative Digital Innovations: Education, Sports, and Entertainment; and General IS and Digital Business Topics.
Building Neo4j Powered Applications With Llms
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Author : Ravindranatha Anthapu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-06-20
Building Neo4j Powered Applications With Llms written by Ravindranatha Anthapu 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 2025-06-20 with Computers categories.
A comprehensive guide to building cutting-edge generative AI applications using Neo4j's knowledge graphs and vector search capabilities Key Features Design vector search and recommendation systems with LLMs using Neo4j GenAI, Haystack, Spring AI, and LangChain4j Apply best practices for graph exploration, modeling, reasoning, and performance optimization Build and consume Neo4j knowledge graphs and deploy your GenAI apps to Google Cloud Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionEmbark on an expert-led journey into building LLM-powered applications using Retrieval-Augmented Generation (RAG) and Neo4j knowledge graphs. Written by Ravindranatha Anthapu, Principal Consultant at Neo4j, and Siddhant Agrawal, a Google Developer Expert in GenAI, this comprehensive guide is your starting point for exploring alternatives to LangChain, covering frameworks such as Haystack, Spring AI, and LangChain4j. As LLMs (large language models) reshape how businesses interact with customers, this book helps you develop intelligent applications using RAG architecture and knowledge graphs, with a strong focus on overcoming one of AI’s most persistent challenges—mitigating hallucinations. You'll learn how to model and construct Neo4j knowledge graphs with Cypher to enhance the accuracy and relevance of LLM responses. Through real-world use cases like vector-powered search and personalized recommendations, the authors help you build hands-on experience with Neo4j GenAI integrations across Haystack and Spring AI. With access to a companion GitHub repository, you’ll work through code-heavy examples to confidently build and deploy GenAI apps on Google Cloud. By the end of this book, you’ll have the skills to ground LLMs with RAG and Neo4j, optimize graph performance, and strategically select the right cloud platform for your GenAI applications.What you will learn Design, populate, and integrate a Neo4j knowledge graph with RAG Model data for knowledge graphs Integrate AI-powered search to enhance knowledge exploration Maintain and monitor your AI search application with Haystack Use LangChain4j and Spring AI for recommendations and personalization Seamlessly deploy your applications to Google Cloud Platform Who this book is for This LLM book is for database developers and data scientists who want to leverage knowledge graphs with Neo4j and its vector search capabilities to build intelligent search and recommendation systems. Working knowledge of Python and Java is essential to follow along. Familiarity with Neo4j, the Cypher query language, and fundamental concepts of databases will come in handy.
Azure Openai Essentials
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Author : Amit Mukherjee
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-02-27
Azure Openai Essentials written by Amit Mukherjee 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 2025-02-27 with Computers categories.
Build innovative, scalable, and ethical AI solutions by harnessing the full potential of generative AI with this exhaustive guide Key Features Explore the capabilities of Azure OpenAI’s LLMs Craft end-to-end applications by utilizing the synergy of Azure OpenAI and Cognitive Services Design enterprise-grade GenAI solutions with effective prompt engineering, fine-tuning, and AI safety measures Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionFind out what makes Azure OpenAI a robust platform for building AI-driven solutions that can transform how businesses operate. Written by seasoned experts from Microsoft, this book will guide you in understanding Azure OpenAI from fundamentals through to advanced concepts and best practices. The book begins with an introduction to large language models (LLMs) and the Azure OpenAI Service, detailing how to access, use, and optimize its models. You'll learn how to design and implement AI-driven solutions, such as question-answering systems, contact center analytics, and GPT-powered search applications. Additionally, the chapters walk you through advanced concepts, including embeddings, fine-tuning models, prompt engineering, and building custom AI applications using LangChain and Semantic Kernel. You'll explore real-world use cases such as QnA systems, document summarizers, and SQLGPT for database querying, as well as gain insights into securing and operationalizing these solutions in enterprises. By the end of this book, you'll be ready to design, develop, and deploy scalable AI solutions, ensuring business success through intelligent automation and data-driven insights.What you will learn Understand the concept of large language models and their capabilities Interact with different models in Azure OpenAI using APIs or web interfaces Use content filters and mitigations to prevent harmful content generation Develop solutions with Azure OpenAI for content generation, summarization, semantic search, NLU, code and image generation and analysis Integrate Azure OpenAI with other Azure Cognitive services for enhanced functionality Apply best practices for data privacy, security, and prompt engineering with Azure OpenAI Who this book is for This book is for software developers, data scientists, AI engineers, ML engineers, system architects, LLM engineers, IT professionals, product managers, and business professionals who want to learn how to use Azure OpenAI to create innovative solutions with generative AI. To fully benefit from this book, you must have both an Azure subscription and Azure OpenAI access, along with knowledge of Python.
Building Ai Agents With Llms Rag And Knowledge Graphs
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Author : Salvatore Raieli
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-07-11
Building Ai Agents With Llms Rag And Knowledge Graphs written by Salvatore Raieli 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 2025-07-11 with Computers categories.
Master LLM fundamentals to advanced techniques like RAG, reinforcement learning, and knowledge graphs to build, deploy, and scale intelligent AI agents that reason, retrieve, and act autonomously Key Features Implement RAG and knowledge graphs for advanced problem-solving Leverage innovative approaches like LangChain to create real-world intelligent systems Integrate large language models, graph databases, and tool use for next-gen AI solutions Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis AI agents book addresses the challenge of building AI that not only generates text but also grounds its responses in real data and takes action. Authored by AI specialists with deep expertise in drug discovery and systems optimization, this guide empowers you to leverage retrieval-augmented generation (RAG), knowledge graphs, and agent-based architectures to engineer truly intelligent behavior. By combining large language models (LLMs) with up-to-date information retrieval and structured knowledge, you'll create AI agents capable of deeper reasoning and more reliable problem-solving. Inside, you'll find a practical roadmap from concept to implementation. You’ll discover how to connect language models with external data via RAG pipelines for increasing factual accuracy and incorporate knowledge graphs for context-rich reasoning. The chapters will help you build and orchestrate autonomous agents that combine planning, tool use, and knowledge retrieval to achieve complex goals. Concrete Python examples built on popular libraries, along with real-world case studies, reinforce each concept and show you how these techniques come together. By the end of this book, you’ll be well-equipped to build intelligent AI agents that reason, retrieve, and interact dynamically, empowering you to deploy powerful AI solutions across industries.What you will learn Learn how LLMs work, their structure, uses, and limits, and design RAG pipelines to link them to external data Build and query knowledge graphs for structured context and factual grounding Develop AI agents that plan, reason, and use tools to complete tasks Integrate LLMs with external APIs and databases to incorporate live data Apply techniques to minimize hallucinations and ensure accurate outputs Orchestrate multiple agents to solve complex, multi-step problems Optimize prompts, memory, and context handling for long-running tasks Deploy and monitor AI agents in production environments Who this book is for If you are a data scientist or researcher who wants to learn how to create and deploy an AI agent to solve limitless tasks, this book is for you. To get the most out of this book, you should have basic knowledge of Python and Gen AI. This book is also excellent for experienced data scientists who want to explore state-of-the-art developments in LLM and LLM-based applications.
The Ai Centered Enterprise
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Author : Ram Bala
language : en
Publisher: CRC Press
Release Date : 2025-07-14
The Ai Centered Enterprise written by Ram Bala and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-14 with Business & Economics categories.
The explosion of generative AI has sparked a wave of case studies showing how quickly and profoundly it is transforming businesses. Yet most of these use cases still apply the technology to enhancing existing systems. This book makes the case for why business leaders must revisit the fundamentals of generative AI and look beyond short-term, tactical gains. Tools like ChatGPT mark just the beginning of Context Aware AI—systems that grasp both the content and intent of unstructured human input. Drawing on real-world examples and academic research, we demonstrate how Context Aware AI can enhance organizational interactions, unlock new forms of collaboration, and usher in the era of the AI-Centered Enterprise. By augmenting baseline Large Language Models (LLMs) with techniques like prompt engineering, retrieval-augmented generation (RAG), knowledge graphs, and notably agentic systems, organizations can build customized tools that adapt to individual users’ thinking patterns and the collaborative workflows they are a part of. We present a practical framework—the 3Cs: Calibrate, Clarify, Channelize, to help leaders navigate this radical shift across multiple levels of organizations.