- Acknowledgements
- Introduction
- Why Hybrid Search
- 1. The Limits of Keyword Search
- 2. The Limits of Vector Search
- 3. The Case for Hybrid
- Architecture
- 4. Hybrid Search Architecture Patterns
- 5. Query Understanding
- 6. The Reranking Stage
- 7. Choosing Your Search Platform
- Models
- 8. Embedding Model Selection
- 9. Fine-Tuning Embeddings for Your Domain
- 10. Choosing and Training Reranker Models
- Evaluation
- 11. Search Quality Metrics
- 12. Building an Evaluation Pipeline
- 13. Online Evaluation and Experimentation
- Production Operations
- 14. Indexing at Scale
- 15. Latency, Throughput, and Scaling
- 16. Monitoring and Observability
- 17. Cost Optimization
- Applied Domains
- 18. Hybrid Search for RAG Pipelines
- 19. E-Commerce Product Search
- 20. Enterprise Knowledge Search
- Appendices
- A. Mathematical Foundations Quick Reference
- B. Benchmark Datasets for Search Evaluation
- C. Migration Playbook
- D. References
Designing Hybrid Search Systems
A Practitioner's Guide to Combining Lexical and Semantic Retrieval in Production
Keyword search misses meaning. Vector search misses precision. This book shows you how to combine them into production systems that deliver both, with architecture patterns, model selection frameworks, evaluation methodology, and operational guidance grounded in primary research.
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About
About the Book
Neither keyword search nor vector search is sufficient on its own. Keyword search fails silently on vocabulary mismatch. Vector search returns confidently wrong results for exact queries, negation, and domain-specific terms. Production systems need both, and combining them well is a distinct engineering discipline.
Designing Hybrid Search Systems is the first book-length treatment of hybrid search as a system design problem. It covers the full stack: fusion algorithms and pipeline architecture, embedding model selection and fine-tuning, reranker training, offline and online evaluation methodology, latency and cost optimization, and applied guidance for RAG pipelines, e-commerce, and enterprise knowledge search.
Every architectural pattern is vendor-neutral. Every quantitative claim cites its source. The book is grounded in primary research, translated into system design decisions for practitioners who build and operate search infrastructure.
Written for senior engineers, ML engineers, engineering managers, and enterprise architects who need to ship search systems that actually work.
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About the Author
László Csontos
Laszlo builds and fixes search systems: hybrid retrieval, custom embeddings, reranking, and RAG pipelines. His current work focuses on fine-tuned embeddings, cross-encoder reranking, hybrid search fusion, GraphRAG, and knowledge graph-powered retrieval. He previously built search and ML systems at an ed-tech platform, working on Elasticsearch, text classification, and content relevance at scale.
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