This is the code repository for Vector Search for Practitioners with Elastic, published by Packt.
A toolkit for building NLP solutions for search, observability, and security using vector search
While natural language processing (NLP) is largely used in search use cases, this book aims to inspire you to start using vectors to overcome equally important domain challenges like observability and cybersecurity. The chapters focus mainly on integrating vector search with Elastic to enhance not only their search but also observability and cybersecurity capabilities.
This book covers the following exciting features:
- Optimize performance by harnessing the capabilities of vector search
- Explore image vector search and its applications
- Detect and mask personally identifiable information
- Implement log prediction for next-generation observability
- Use vector-based bot detection for cybersecurity
- Visualize the vector space and explore Search.Next with Elastic
- Implement a RAG-enhanced application using Streamlit
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
{'_source':
{
'redacted': '<PER> called in from 001-<PHONE>x1311. Their account
number is <SSN>'
'status': 'active'
}
Following is what you need for this book: If you're a data professional with experience in Elastic observability, search, or cybersecurity and are looking to expand your knowledge of vector search, this book is for you. This book provides practical knowledge useful for search application owners, product managers, observability platform owners, and security operations center professionals. Experience in Python, using machine learning models, and data management will help you get the most out of this book.
With the following software and hardware list you can run all code files present in the book (Chapter 1-10).
To fully benefit from this book, readers should possess a basic understanding of Elasticsearch operations, fundamental Python programming skills, and a general familiarity with search concepts. This foundational knowledge will enable readers to more effectively grasp the advanced techniques and applications discussed throughout the book.
System requirements are mentioned in the following table:
| Software/Hardware | Operating System requirements |
|---|---|
| Elasticsearch 8.11+ | Windows, Linus, and MacOS |
| Python 3.9+ | |
| Jupyter Notebook |
Bahaaldine Azarmi, Global VP Customer Engineering at Elastic, guides companies as they leverage data architecture, distributed systems, machine learning, and generative AI. He leads the customer engineering team, focusing on cloud consumption, and is passionate about sharing knowledge to build and inspire a community skilled in AI.
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 skill, 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.