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Advanced Retrieval-Augmented Generation Bridging Large Language Models and Knowledge Graphs

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

27.07.2026

Verlag

WILEY

Seitenzahl

560

Maße (L/B/H)

25,9/18,4/3,8 cm

Gewicht

1125 g

Auflage

1

Sprache

Englisch

ISBN

978-1-394-37468-7

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

27.07.2026

Verlag

WILEY

Seitenzahl

560

Maße (L/B/H)

25,9/18,4/3,8 cm

Gewicht

1125 g

Auflage

1

Sprache

Englisch

ISBN

978-1-394-37468-7

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: GPSR Kontakt

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  • Produktbild: Advanced Retrieval-Augmented Generation

  • Foreword xiii
    Preface xv
    Acknowledgments xix
    Introduction xxi

    Part I From Traditional Information Retrieval to Modern RAG 1

    1 Information Retrieval 3
    1.1 Definition and Historical Evolution 3
    1.2 Information Retrieval Components 14
    1.3 Information Retrieval Applications 34
    1.4 Challenges with IR Systems 45
    1.5 Summary 46

    2 Large Language Models 49
    2.1 LLMs Overview 49
    2.2 LLM Use Case in Information Retrieval 91
    2.3 Challenges of LLMs for Information Retrieval 108
    2.4 Summary 119

    3 Retrieval-augmented Generation 123
    3.1 RAG Overview 123
    3.2 Data Preparation and Indexing 126
    3.3 Retrieval Approaches 139
    3.4 Generation 148
    3.5 Summary 153

    4 Practice: RAG Implementation 157
    4.1 Overview of LangChain and LlamaIndex 157
    4.2 Implementing RAG Pipelines with LlamaIndex 159
    4.3 Example: Implementing RAG on the WANDS Dataset 175
    4.4 Implementing Agentic RAG 189
    4.5 Summary 195

    Part II Graphs and Knowledge Graphs 197

    5 Graphs and Graph Databases 199
    5.1 Introduction to Graphs 199
    5.2 Graph Databases: Comparison and Analysis 217
    5.3 Introduction to Neo4j and Cypher 221
    5.4 Practice: Flight Network Analysis and Optimization 228
    5.5 Summary 244

    6 Knowledge Graphs 247
    6.1 Understanding KGs 247
    6.2 Construction and Management of KGs 261
    6.3 KGs Analytics and Enrichment 268
    6.4 LLMs and KGs 287
    6.5 Practice: Constructing KG from the WANDS Dataset 293
    6.6 Practice: Construct KG from Unstructured Data 301
    6.7 Summary 303

    Part III Integrate RAG with Graph 307

    7 Graph-based Retrieval-augmented Generation 309
    7.1 Introduction to Graph-RAG 309
    7.2 Architecture and Components of Graph-RAG 315
    7.3 Applications 337
    7.4 Summary 340

    8 Practice: Graph-RAG Implementations 345
    8.1 Graph-RAG on WANDS Dataset with LlamaIndex 345
    8.2 Graph-RAG on Wiki and Kaggle Data with LangChain 352
    8.3 Summary 364

    9 Graph-RAG Evaluations 367
    9.1 Performance Metrics Framework 367
    9.2 Quality Assessment Methodologies 382
    9.3 Benchmarking Frameworks 392
    9.4 Tools and Platforms for Graph-RAG Evaluations 396
    9.5 Practice: Evaluating RAG Pipelines Using Ragas 400
    9.6 Summary 402

    Part IV Advanced Implementations and Frontiers 405

    10 Graph-RAG Frameworks for Enhanced Information Retrieval 407
    10.1 Overview of Graph-RAG Frameworks for Search and Recommendations 407
    10.2 Graph-RAG Tools and Softwares Overview 421
    10.3 Practice: Run Graph-RAG Frameworks 424
    10.4 Summary 453

    11 Frontiers of Graph-RAG 455
    11.1 Emerging Trends in Graph-RAG 455
    11.2 Ethical Considerations and Bias Mitigation 478
    11.3 Future Research Directions 482
    11.4 Summary 490
    11.5 Conclusion and Final Thoughts 491

    A Set Up Experiment Servers 495
    A.1 Two Models Hosted by vLLM 497
    A.2 LLM Gateway 499
    A.3 UI for LLM Models 500
    A.4 Vector Store 500
    A.5 Graph Database 501

    B Prepare Synthetic Recommendation Data from WANDS 503
    B.1 Step 1: Download the WANDS Dataset 503
    B.2 Step 2: Load the Dataset 503
    B.3 Step 3: Prepare the Data for Recommendation Tasks 504
    B.4 Step 4: Generate Synthetic Recommendation Data 504
    B.5 Step 5: Dataset Statistics and Sample Entries 509

    Index 511