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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

Product type Paperback

Published in Nov 2023

Publisher Packt

ISBN-13 9781805121022

Length 240 pages

Edition 1st Edition

Languages

Python

Tools

Streamlit

Concepts

Data Analysis

Authors (2):

Jeff Vestal

Bahaaldine Azarmi

View More author details

Table of Contents (17) Chapters

Preface

1. Part 1:Fundamentals of Vector Search FREE CHAPTER

2. Chapter 1: Introduction to Vectors and Embeddings

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

Why subscribe?

16. Other Books You May Enjoy

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

AI-based search 12

data collection 12

data representation 12

ML application 13

personalization 13

personalization, integrating 13

Allrecipes.com dataset 173

fields 174

reference link 173

Amazon Bedrock

URL 172

application performance monitoring (APM) 123

approximate nearest neighbor (ANN) 96

automated detection

role 146, 147

average document length (avgdl) 25

B

Bag-of-Words (BoW) 28

BERT model 41

bi-gram 23

BM25 5, 24, 25

BM25 sub-search 171

brute-force kNN search 44

brute-force search 94

Business Email Compromise (BEC) 143

C

ChatGPT integration, with DCL

manifest 203

OpenAPI definition 203, 204

plugin code 199

real-time conversationalist, creating 198

real-time data flow, handling 198

repository...

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Authors (2)

Bahaaldine Azarmi

Bahaaldine Azarmi

Bahaaldine Azarmi, or Baha for short, is the head of solutions architecture in the EMEA South region at Elastic. Prior to this position, Baha co-founded ReachFive, a marketing data platform focused on user behavior and social analytics. He has also worked for a number of different software vendors, including Talend and Oracle, where he held positions as a solutions architect and architect. Prior to Machine Learning with the Elastic Stack, Baha authored books including Learning Kibana 5.0, Scalable Big Data Architecture, and Talend for Big Data. He is based in Paris and holds an MSc in computer science from Polytech'Paris.

See other products by Bahaaldine Azarmi

Vestal

Vestal

Jeff Vestal has a rich background spanning over a decade in financial trading firms and extensive experience with Elasticsearch. He offers a unique blend of operational acumen, engineering skills, and machine learning expertise. As a Principal Customer Enterprise Architect, he excels at crafting innovative solutions, leveraging Elasticsearch's advanced search capabilities, machine learning features, and generative AI integrations, adeptly guiding users to transform complex data challenges into actionable insights.

See other products by Vestal

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