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Vector Search for Practitioners with Elastic

You're reading from   Vector Search for Practitioners with Elastic A toolkit for building NLP solutions for search, observability, and security using vector search

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Product type Paperback
Published in Nov 2023
Publisher Packt
ISBN-13 9781805121022
Length 240 pages
Edition 1st Edition
Languages
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Authors (2):
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Jeff Vestal Jeff Vestal
Author Profile Icon Jeff Vestal
Jeff Vestal
Bahaaldine Azarmi Bahaaldine Azarmi
Author Profile Icon Bahaaldine Azarmi
Bahaaldine Azarmi
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Toc

Table of Contents (17) Chapters Close

Preface 1. Part 1:Fundamentals of Vector Search
2. Chapter 1: Introduction to Vectors and Embeddings FREE CHAPTER 3. Chapter 2: Getting Started with Vector Search in Elastic 4. Part 2: Advanced Applications and Performance Optimization
5. Chapter 3: Model Management and Vector Considerations in Elastic 6. Chapter 4: Performance Tuning – Working with Data 7. Part 3: Specialized Use Cases
8. Chapter 5: Image Search 9. Chapter 6: Redacting Personal Identifiable Information Using Elasticsearch 10. Chapter 7: Next Generation of Observability Powered by Vectors 11. Chapter 8: The Power of Vectors and Embedding in Bolstering Cybersecurity 12. Part 4: Innovative Integrations and Future Directions
13. Chapter 9: Retrieval Augmented Generation with Elastic 14. Chapter 10: Building an Elastic Plugin for ChatGPT 15. Index 16. Other Books You May Enjoy

Evolution of search experience

We are now going to see how users’ demand for a better search experience requires us to consider other techniques than just keyword-based search. In this section, we will approach the limitations of keyword-based search, understand what vector representation entails, and how the meta representation HNSW emerged to facilitate information retrieval with vector.

The limits of keyword-based search

For those of you who are comparatively new to the subject matter, before we talk about vector representation, we need to understand why the industry and keyword-based search experience have reached their limits, failing to fully meet end-user requirements.

Keyword-based search relies on exact matches between the user query and the terms contained in documents, which could lead to missed relevant results if the search system is not refined enough with synonyms, abbreviations, alternative phrasings, and so on. Therefore, it is important for the search...

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