June 2027
Intermediate to advanced
350 pages
10h 4m
English
Content preview from Scaling Search and Retrieval for Contextual AI
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Brief Table of Contents (Not Yet Final)
Part I: Search Fundamentals
Chapter 1: What Is a Search Engine, Really? (available)
Chapter 2: Data Structures for Search (available)
Chapter 3: Document Ingestion and Indexing Pipelines (available)
Chapter 4: Segment Files and Storage Models (available)
Chapter 5: Query Execution and Scoring (available)
Chapter 6: The Write Stuff: Buffering and Flushing (available)
Part II: Scaling Outward
Chapter 7: Sharding and Index Partitioning (available)
Chapter 8: Coordinated Distributed Search (available)
Chapter 9: Snapshots and Storage Strategy (unavailable)
Part III: Modern Retrieval and AI Integration
Chapter 10: Hybrid Search: Fields, Vectors, and Filters (unavailable)
Chapter 11: Retrieval for Contextual AI (unavailable)
Chapter 12: Multimodal and Semantic Pipelines (unavailable)
Part IV: Deploying and Operating a Search Platform
Chapter 13: Deployment Patterns and Architectures (unavailable)
Chapter 14: Security, Isolation, and Policy Enforcement (unavailable)
Chapter 15: Observability and Self-Healing Systems (unavailable)
Chapter 16: Cost Efficiency and System Resilience (unavailable)
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.