Advanced Retrieval-Augmented Generation
Bridging Large Language Models and Knowledge Graphs
1st Edition
Will be published approx. on 15. September 2026
Book
Hardback
540 pages
978-1-394-37468-7 (ISBN)
Description
Build Accurate, Grounded, and Trustworthy AI Systems with Retrieval-Augmented Generation
Large language models are powerful-but they hallucinate. Advanced Retrieval-Augmented Generation offers a complete guide from the foundations of information retrieval (IR) to the cutting-edge frontiers of RAG. Bridging large language models (LLMs) and knowledge graphs (KGs), this book provides the theoretical principles, practical techniques, and hands-on frameworks needed to build reliable AI systems that minimize hallucinations and improve factual correctness. The book covers core concepts of Graph-RAG with applications across search, recommendation, and enterprise AI. Practical chapters demonstrate implementations using LlamaIndex, Neo4j, and leading Graph-RAG frameworks.
Readers will learn:
IR and LLM fundamentals - model paradigms, transformer architecture, model families, training techniques, prompt engineering, applications, and limitations
RAG pipeline engineering -chunking, indexing, retrieval, ranking, and generation
KG construction and analytics - schema design, extraction techniques, graph algorithms, embeddings, and GNNs
Graph-RAG architectures and evaluation - graph-based retrieval, graph-assisted generation, hybrid LLM-KG workflows, frameworks, benchmarks, and metrics
Emerging directions - multimodal KGs, dynamic graphs, explainable RAG, RL-based traversal, and enterprise-scale implementations
With extensive hands-on examples and production-ready patterns, Advanced Retrieval-Augmented Generation is an indispensable resource for AI practitioners, ML engineers, researchers, and architects building the next generation of reliable, knowledge-grounded AI systems.
Large language models are powerful-but they hallucinate. Advanced Retrieval-Augmented Generation offers a complete guide from the foundations of information retrieval (IR) to the cutting-edge frontiers of RAG. Bridging large language models (LLMs) and knowledge graphs (KGs), this book provides the theoretical principles, practical techniques, and hands-on frameworks needed to build reliable AI systems that minimize hallucinations and improve factual correctness. The book covers core concepts of Graph-RAG with applications across search, recommendation, and enterprise AI. Practical chapters demonstrate implementations using LlamaIndex, Neo4j, and leading Graph-RAG frameworks.
Readers will learn:
IR and LLM fundamentals - model paradigms, transformer architecture, model families, training techniques, prompt engineering, applications, and limitations
RAG pipeline engineering -chunking, indexing, retrieval, ranking, and generation
KG construction and analytics - schema design, extraction techniques, graph algorithms, embeddings, and GNNs
Graph-RAG architectures and evaluation - graph-based retrieval, graph-assisted generation, hybrid LLM-KG workflows, frameworks, benchmarks, and metrics
Emerging directions - multimodal KGs, dynamic graphs, explainable RAG, RL-based traversal, and enterprise-scale implementations
With extensive hands-on examples and production-ready patterns, Advanced Retrieval-Augmented Generation is an indispensable resource for AI practitioners, ML engineers, researchers, and architects building the next generation of reliable, knowledge-grounded AI systems.
More details
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
ISBN-13
978-1-394-37468-7 (9781394374687)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Persons
Wendy Ran Wei, PhD, is an expert in AI, ML, and LLMs, specializing in search and recommendation systems. She is a Machine Learning Engineer at Airbnb, where she develops retrieval and ranking models and brings LLM technologies into production. She previously held engineering roles at Meta, Pinterest, and Twitter, building large-scale search and recommendation solutions. Dr. Wei received her PhD in Statistics from The Ohio State University. Huijun Wu, PhD, is an Engineer at Samsung Research America with expertise in large-scale distributed systems and data processing. He received his PhD in Computer Science from Arizona State University.
Content
Contents
ForewordxiiiPreface xv Acknowledgmentsxix Introduction xxi
Part I From Traditional Information Retrieval to Modern RAG 1
1 InformationRetrieval 3
1.1 Definition and Historical Evolution 3
1.1.1 Definition 3
1.1.2 Historical Evolution 4
1.2 Information Retrieval Components 14
1.2.1 Overview 14
1.2.2 Pre-retrieval 16
1.2.3 Retrieval Models 21
1.2.4 Post-retrieval 30
1.3 Information Retrieval Applications 34
1.3.1 Search Systems 35
1.3.2 Recommendation Systems 38
1.3.3 Example: WANDS Dataset 40
1.4 Challenges with IR Systems 45
1.4.1 Lack of Context Awareness 45
1.4.2 Cold-start Problem 45
1.4.3 Scalability 45
1.4.4 Difficulty in Handling Complex Queries 45
1.5 Summary 46
References 46
2 Large Language Models 49 2.1 LLMs Overview 49
2.1.1 Evolution of ML Paradigms 49
2.1.2 LLM Fundamentals 54
2.1.3 LLM Families 75
2.1.4 LLM Paradigms 83
viii Contents
2.2
LLM Use Case in Information Retrieval 91
2.2.1
Use LLM for Search Systems 91
2.2.2
Use LLM for Recommendation Systems 102
2.3
Challenges of LLMs for Information Retrieval 108
2.3.1
Core Limitations: Hallucinations and Knowledge Staleness
108
2.3.2
The RAG Solution: Dynamic Knowledge Integration 110
2.3.3 Practice: LLM for Search and Recommendation Tasks 114
2.3.4 Practice: Contextual LLM Responses 115
2.4 Summary 119
References 119
3 Retrieval-augmented Generation 123 3.1 RAG Overview 123
3.1.1 RAG Components 123
3.1.2 From Tokens to Passage Embeddings 124
3.1.3 RAG as a Retrieval-agnostic Architecture 125
3.2 Data Preparation and Indexing 126
3.2.1 Understanding Data Source Types 126
3.2.2 Data Preprocessing 129
3.2.3 Chunking Techniques 131
3.2.4 Indexing 136
3.3 Retrieval Approaches 139
3.3.1 The Distinctive Nature of RAG Retrieval 140
3.3.2 Core Retrieval Methods in RAG 140
3.3.3 Advanced Retrieval Patterns 145
3.3.4 Retrieval Performance Optimization 146
3.3.5 Reranking and Filtering 147
3.4 Generation 148
3.4.1 Generation Pipeline 149
3.4.2 Model Selection Framework 150
3.4.3 Parameter Tuning 150
3.4.4 Inference Speed and Optimization Techniques 151
3.5 Summary 153
References 153
4 Practice: RAG Implementation 157 4.1 Overview of LangChain and LlamaIndex 157
4.1.1 LangChain and LangGraph 157
4.1.2 LlamaIndex 158
4.1.3 Comparison Summary 159
4.2 Implementing RAG Pipelines with LlamaIndex 159
4.2.1 Offline Stages: Loading and Indexing 160
4.2.2 Online Stage: Querying 166
4.2.3 Key Takeaways 175
4.3 Example: Implementing RAG on the WANDS Dataset 175
4.3.1 Indexing Phase 175
4.3.2 Query Phase 176
Contents ix
4.3.3 Key Takeaways 189
4.4 Implementing Agentic RAG 189
4.4.1 Offline Index Construction 190
4.4.2 Offline Agent Construction 191
4.4.3 Online Query Processing 192
4.4.4 Key Takeaways 195
4.5 Summary 195
References 196
Part II Graphs and Knowledge Graphs 197
5 GraphsandGraphDatabases 199
5.1 Introduction to Graphs 199
5.1.1 Types of Graphs 200
5.1.2 Graph Representation 207
5.1.3 Graph Operations 210
5.1.4 Graph Algorithms 213
5.1.5 Applications of Graphs 216
5.2 Graph Databases: Comparison and Analysis 217
5.2.1 Notable Graph Databases 218
5.2.2 Comparison Summary 221
5.3 Introduction to Neo4j and Cypher 221
5.3.1 Cypher Basic Operations 222
5.3.2 Neo4j Utility Functions: APOC 224
5.3.3 Neo4j Graph Analytics: GDS 226
5.4 Practice: Flight Network Analysis and Optimization 228
5.4.1 Problem Setting 228
5.4.2 Understanding the Network Structure 229
5.4.3 Set Up Database and Load Data 229
5.4.4 Graph Algorithms in Flight Networks 232
5.5 Summary 244
References 245
6 Knowledge Graphs 247 6.1 Understanding KGs 247
6.1.1 Core Structure and Components 248
6.1.2 Example: Marvel Universe KG 249
6.1.3 Types of KGs 253
6.1.4 Ontology in KGs 256
6.2 Construction and Management of KGs 261
6.2.1 Schema Design 261
6.2.2 Data Sources and Extraction Techniques 265
6.3 KGs Analytics and Enrichment 268
6.3.1 Graph Algorithms in KGs 268
6.3.2 Graph Embeddings 279
6.3.3 Graph Neural Networks 282
6.4 LLMs and KGs 287
x Contents
6.4.1 Comparisons and Unifications 288
6.4.2 Leveraging LLMs to Enrich KGs 288
6.4.3 Using KGs to Enhance LLMs: A Preview of Graph-RAG 292
6.5 Practice: Constructing KG from the WANDS Dataset 293
6.5.1 LlamaIndex Neo4j Integration 294
6.5.2 Extract Query-product Triples 294
6.5.3 Extract Product-Class Triples 296
6.5.4 Extract Product-category Triples 298
6.5.5 Load Documents into Neo4j 300
6.5.6 Key Takeaways 300
6.6 Practice: Construct KG from Unstructured Data 301
6.6.1 Key Takeaways 302
6.7 Summary 303
References 304
Part III Integrate RAG with Graph 307
7 Graph-basedRetrieval-augmentedGeneration 309
7.1 Introduction to Graph-RAG 309
7.1.1 The Graph-RAG Paradigm 309
7.1.2 Why Graph-RAG? 310
7.2 Architecture and Components of Graph-RAG 315
7.2.1 Graph-based Indexing 316
7.2.2 Graph-based Retrieval 318
7.2.3 Graph-based Generation 333
7.3 Applications 337
7.3.1 Downstream Tasks 337
7.3.2 Application Domains 338
7.4 Summary 340
References 341
8 Practice: Graph-RAG Implementations 345 8.1 Graph-RAG on WANDS Dataset with LlamaIndex 345
8.1.1 Querying with Neo4j and Chroma 345
8.1.2 Removing Chroma Dependency 351
8.1.3 Key Takeaways 352
8.2 Graph-RAG on Wiki and Kaggle Data with LangChain 352
8.2.1 Wikipedia Text Search and Question Answering 352
8.2.2 Answering Kaggle CSV Recommendation Questions 357
8.3 Summary 364
Reference 365
9 Graph-RAG Evaluations 367 9.1 Performance Metrics Framework 367
9.1.1 Retrieval-specific Metrics 367
9.1.2 Generation Quality Metrics 372
9.1.3 Graph-specific Metrics 376
Contents
xi
9.1.4 System Performance Metrics 380
9.2 Quality Assessment Methodologies 382
9.2.1 Human Evaluation Framework 383
9.2.2 Automated Evaluation Methods 386
9.2.3 Meta-evaluation 389
9.2.4 Best Practices for Quality Assessment 391
9.3 Benchmarking Frameworks 392
9.3.1 Established Benchmarks 392
9.3.2 Benchmark Design Principles 394
9.3.3 Comparative Analysis Framework 395
9.4 Tools and Platforms for Graph-RAG Evaluations 396
9.4.1 Tools and Platforms Overview 396
9.4.2 Integration Best Practices 399
9.4.3 Future Directions in Tooling 400
9.5 Practice: Evaluating RAG Pipelines Using Ragas 400
9.5.1 Preparing the Evaluation Dataset 400
9.5.2 Defining Evaluation Metrics 401
9.5.3 Executing the Evaluation Process 402
9.5.4 Key Takeaways 402
9.6 Summary 402
References 403
PartIVAdvancedImplementationsandFrontiers 405
10 Graph-RAGFrameworksforEnhancedInformationRetrieval 407
10.1 Overview of Graph-RAG Frameworks for Search and Recommendations
407
10.1.1 RAPTOR: Recursive Abstractive Processing for Tree-organized Retrieval
10.1.2 G-Retriever: Joint Text-graph Embeddings 411
10.1.3 Microsoft GraphRAG: Global Sensemaking Through Community Summaries 414
10.1.4 HippoRAG: Neurobiologically-inspired Long-term Memory 417
10.2 Graph-RAG Tools and Softwares Overview 421
10.2.1 What Is Available in the Current Market for Graph-RAG? 421
10.2.2 When to Choose Which Framework? 423
10.3 Practice: Run Graph-RAG Frameworks 424
10.3.1 RAPTOR 424
10.3.2 G-Retriever 429
10.3.3 Microsoft GraphRAG 432
10.3.4 HippoRAG 444
10.4 Summary 453
References 454
408
11 Frontiers of Graph-RAG 455
11.1 Emerging Trends in Graph-RAG 455
11.1.1 Multimodal Knowledge Graphs 455
11.1.2 Temporal and Dynamic Graphs 462
11.1.3 Advanced GNNs for RAG 468
xii Contents
11.2
Ethical Considerations and Bias Mitigation 478
11.2.1
The Dual Nature of Bias in Graph-RAG 478
11.2.2
Comprehensive Mitigation Strategies 479
11.2.3
Ethical AI Governance Framework 481
11.3
Future Research Directions 482
11.3.1
Explainable Graph-RAG: Making AI Reasoning Transparent
482
11.3.2
Automated Knowledge Graph Construction and Maintenance
483
11.3.3
RL for Intelligent Graph Traversal 485
11.3.4
Federated Learning for Privacy-preserving Graph-RAG 486
11.3.5
Transformative Industry Applications 488
11.4
Summary 490
11.5
Conclusion and Final Thoughts 491
References 492
AA.1 A.1.1
A.1.2 A.2
A.3
A.4
A.5
Set Up Experiment Servers495 Two Models Hosted by vLLM 497 Chat Model Service 497 Embedding Model Service 498 LLM Gateway 499
UI for LLM Models 500
Vector Store 500
Graph Database 501
B
B.1
B.2
B.3
B.4 B.4.1
B.4.2
B.4.3 B.5
PrepareSyntheticRecommendationDatafromWANDS 503
Step 1: Download the WANDS Dataset 503
Step 2: Load the Dataset 503
Step 3: Prepare the Data for Recommendation Tasks 504 Step 4: Generate Synthetic Recommendation Data 504 Session-based Recommendation Data 507
User-query Synthetic Profiles 508
Next-item Prediction Sequences 508
Step 5: Dataset Statistics and Sample Entries 509
Index 511
ForewordxiiiPreface xv Acknowledgmentsxix Introduction xxi
Part I From Traditional Information Retrieval to Modern RAG 1
1 InformationRetrieval 3
1.1 Definition and Historical Evolution 3
1.1.1 Definition 3
1.1.2 Historical Evolution 4
1.2 Information Retrieval Components 14
1.2.1 Overview 14
1.2.2 Pre-retrieval 16
1.2.3 Retrieval Models 21
1.2.4 Post-retrieval 30
1.3 Information Retrieval Applications 34
1.3.1 Search Systems 35
1.3.2 Recommendation Systems 38
1.3.3 Example: WANDS Dataset 40
1.4 Challenges with IR Systems 45
1.4.1 Lack of Context Awareness 45
1.4.2 Cold-start Problem 45
1.4.3 Scalability 45
1.4.4 Difficulty in Handling Complex Queries 45
1.5 Summary 46
References 46
2 Large Language Models 49 2.1 LLMs Overview 49
2.1.1 Evolution of ML Paradigms 49
2.1.2 LLM Fundamentals 54
2.1.3 LLM Families 75
2.1.4 LLM Paradigms 83
viii Contents
2.2
LLM Use Case in Information Retrieval 91
2.2.1
Use LLM for Search Systems 91
2.2.2
Use LLM for Recommendation Systems 102
2.3
Challenges of LLMs for Information Retrieval 108
2.3.1
Core Limitations: Hallucinations and Knowledge Staleness
108
2.3.2
The RAG Solution: Dynamic Knowledge Integration 110
2.3.3 Practice: LLM for Search and Recommendation Tasks 114
2.3.4 Practice: Contextual LLM Responses 115
2.4 Summary 119
References 119
3 Retrieval-augmented Generation 123 3.1 RAG Overview 123
3.1.1 RAG Components 123
3.1.2 From Tokens to Passage Embeddings 124
3.1.3 RAG as a Retrieval-agnostic Architecture 125
3.2 Data Preparation and Indexing 126
3.2.1 Understanding Data Source Types 126
3.2.2 Data Preprocessing 129
3.2.3 Chunking Techniques 131
3.2.4 Indexing 136
3.3 Retrieval Approaches 139
3.3.1 The Distinctive Nature of RAG Retrieval 140
3.3.2 Core Retrieval Methods in RAG 140
3.3.3 Advanced Retrieval Patterns 145
3.3.4 Retrieval Performance Optimization 146
3.3.5 Reranking and Filtering 147
3.4 Generation 148
3.4.1 Generation Pipeline 149
3.4.2 Model Selection Framework 150
3.4.3 Parameter Tuning 150
3.4.4 Inference Speed and Optimization Techniques 151
3.5 Summary 153
References 153
4 Practice: RAG Implementation 157 4.1 Overview of LangChain and LlamaIndex 157
4.1.1 LangChain and LangGraph 157
4.1.2 LlamaIndex 158
4.1.3 Comparison Summary 159
4.2 Implementing RAG Pipelines with LlamaIndex 159
4.2.1 Offline Stages: Loading and Indexing 160
4.2.2 Online Stage: Querying 166
4.2.3 Key Takeaways 175
4.3 Example: Implementing RAG on the WANDS Dataset 175
4.3.1 Indexing Phase 175
4.3.2 Query Phase 176
Contents ix
4.3.3 Key Takeaways 189
4.4 Implementing Agentic RAG 189
4.4.1 Offline Index Construction 190
4.4.2 Offline Agent Construction 191
4.4.3 Online Query Processing 192
4.4.4 Key Takeaways 195
4.5 Summary 195
References 196
Part II Graphs and Knowledge Graphs 197
5 GraphsandGraphDatabases 199
5.1 Introduction to Graphs 199
5.1.1 Types of Graphs 200
5.1.2 Graph Representation 207
5.1.3 Graph Operations 210
5.1.4 Graph Algorithms 213
5.1.5 Applications of Graphs 216
5.2 Graph Databases: Comparison and Analysis 217
5.2.1 Notable Graph Databases 218
5.2.2 Comparison Summary 221
5.3 Introduction to Neo4j and Cypher 221
5.3.1 Cypher Basic Operations 222
5.3.2 Neo4j Utility Functions: APOC 224
5.3.3 Neo4j Graph Analytics: GDS 226
5.4 Practice: Flight Network Analysis and Optimization 228
5.4.1 Problem Setting 228
5.4.2 Understanding the Network Structure 229
5.4.3 Set Up Database and Load Data 229
5.4.4 Graph Algorithms in Flight Networks 232
5.5 Summary 244
References 245
6 Knowledge Graphs 247 6.1 Understanding KGs 247
6.1.1 Core Structure and Components 248
6.1.2 Example: Marvel Universe KG 249
6.1.3 Types of KGs 253
6.1.4 Ontology in KGs 256
6.2 Construction and Management of KGs 261
6.2.1 Schema Design 261
6.2.2 Data Sources and Extraction Techniques 265
6.3 KGs Analytics and Enrichment 268
6.3.1 Graph Algorithms in KGs 268
6.3.2 Graph Embeddings 279
6.3.3 Graph Neural Networks 282
6.4 LLMs and KGs 287
x Contents
6.4.1 Comparisons and Unifications 288
6.4.2 Leveraging LLMs to Enrich KGs 288
6.4.3 Using KGs to Enhance LLMs: A Preview of Graph-RAG 292
6.5 Practice: Constructing KG from the WANDS Dataset 293
6.5.1 LlamaIndex Neo4j Integration 294
6.5.2 Extract Query-product Triples 294
6.5.3 Extract Product-Class Triples 296
6.5.4 Extract Product-category Triples 298
6.5.5 Load Documents into Neo4j 300
6.5.6 Key Takeaways 300
6.6 Practice: Construct KG from Unstructured Data 301
6.6.1 Key Takeaways 302
6.7 Summary 303
References 304
Part III Integrate RAG with Graph 307
7 Graph-basedRetrieval-augmentedGeneration 309
7.1 Introduction to Graph-RAG 309
7.1.1 The Graph-RAG Paradigm 309
7.1.2 Why Graph-RAG? 310
7.2 Architecture and Components of Graph-RAG 315
7.2.1 Graph-based Indexing 316
7.2.2 Graph-based Retrieval 318
7.2.3 Graph-based Generation 333
7.3 Applications 337
7.3.1 Downstream Tasks 337
7.3.2 Application Domains 338
7.4 Summary 340
References 341
8 Practice: Graph-RAG Implementations 345 8.1 Graph-RAG on WANDS Dataset with LlamaIndex 345
8.1.1 Querying with Neo4j and Chroma 345
8.1.2 Removing Chroma Dependency 351
8.1.3 Key Takeaways 352
8.2 Graph-RAG on Wiki and Kaggle Data with LangChain 352
8.2.1 Wikipedia Text Search and Question Answering 352
8.2.2 Answering Kaggle CSV Recommendation Questions 357
8.3 Summary 364
Reference 365
9 Graph-RAG Evaluations 367 9.1 Performance Metrics Framework 367
9.1.1 Retrieval-specific Metrics 367
9.1.2 Generation Quality Metrics 372
9.1.3 Graph-specific Metrics 376
Contents
xi
9.1.4 System Performance Metrics 380
9.2 Quality Assessment Methodologies 382
9.2.1 Human Evaluation Framework 383
9.2.2 Automated Evaluation Methods 386
9.2.3 Meta-evaluation 389
9.2.4 Best Practices for Quality Assessment 391
9.3 Benchmarking Frameworks 392
9.3.1 Established Benchmarks 392
9.3.2 Benchmark Design Principles 394
9.3.3 Comparative Analysis Framework 395
9.4 Tools and Platforms for Graph-RAG Evaluations 396
9.4.1 Tools and Platforms Overview 396
9.4.2 Integration Best Practices 399
9.4.3 Future Directions in Tooling 400
9.5 Practice: Evaluating RAG Pipelines Using Ragas 400
9.5.1 Preparing the Evaluation Dataset 400
9.5.2 Defining Evaluation Metrics 401
9.5.3 Executing the Evaluation Process 402
9.5.4 Key Takeaways 402
9.6 Summary 402
References 403
PartIVAdvancedImplementationsandFrontiers 405
10 Graph-RAGFrameworksforEnhancedInformationRetrieval 407
10.1 Overview of Graph-RAG Frameworks for Search and Recommendations
407
10.1.1 RAPTOR: Recursive Abstractive Processing for Tree-organized Retrieval
10.1.2 G-Retriever: Joint Text-graph Embeddings 411
10.1.3 Microsoft GraphRAG: Global Sensemaking Through Community Summaries 414
10.1.4 HippoRAG: Neurobiologically-inspired Long-term Memory 417
10.2 Graph-RAG Tools and Softwares Overview 421
10.2.1 What Is Available in the Current Market for Graph-RAG? 421
10.2.2 When to Choose Which Framework? 423
10.3 Practice: Run Graph-RAG Frameworks 424
10.3.1 RAPTOR 424
10.3.2 G-Retriever 429
10.3.3 Microsoft GraphRAG 432
10.3.4 HippoRAG 444
10.4 Summary 453
References 454
408
11 Frontiers of Graph-RAG 455
11.1 Emerging Trends in Graph-RAG 455
11.1.1 Multimodal Knowledge Graphs 455
11.1.2 Temporal and Dynamic Graphs 462
11.1.3 Advanced GNNs for RAG 468
xii Contents
11.2
Ethical Considerations and Bias Mitigation 478
11.2.1
The Dual Nature of Bias in Graph-RAG 478
11.2.2
Comprehensive Mitigation Strategies 479
11.2.3
Ethical AI Governance Framework 481
11.3
Future Research Directions 482
11.3.1
Explainable Graph-RAG: Making AI Reasoning Transparent
482
11.3.2
Automated Knowledge Graph Construction and Maintenance
483
11.3.3
RL for Intelligent Graph Traversal 485
11.3.4
Federated Learning for Privacy-preserving Graph-RAG 486
11.3.5
Transformative Industry Applications 488
11.4
Summary 490
11.5
Conclusion and Final Thoughts 491
References 492
AA.1 A.1.1
A.1.2 A.2
A.3
A.4
A.5
Set Up Experiment Servers495 Two Models Hosted by vLLM 497 Chat Model Service 497 Embedding Model Service 498 LLM Gateway 499
UI for LLM Models 500
Vector Store 500
Graph Database 501
B
B.1
B.2
B.3
B.4 B.4.1
B.4.2
B.4.3 B.5
PrepareSyntheticRecommendationDatafromWANDS 503
Step 1: Download the WANDS Dataset 503
Step 2: Load the Dataset 503
Step 3: Prepare the Data for Recommendation Tasks 504 Step 4: Generate Synthetic Recommendation Data 504 Session-based Recommendation Data 507
User-query Synthetic Profiles 508
Next-item Prediction Sequences 508
Step 5: Dataset Statistics and Sample Entries 509
Index 511