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Vector Search for Practitioners with Elastic

By Bahaaldine Azarmi , Jeff Vestal
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50+ new titles added every month on new and emerging tech
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Download this book in EPUB and PDF formats
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  1. Free Chapter
    Part 1:Fundamentals of Vector Search
About this book
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. The book, which also features a foreword written by the founder of Elastic, begins by teaching you about NLP and the functionality of Elastic in NLP processes. Here you’ll delve into resource requirements and find out how vectors are stored in the dense-vector type along with specific page cache requirements for fast response times. As you advance, you’ll discover various tuning techniques and strategies to improve machine learning model deployment, including node scaling, configuration tuning, and load testing with Rally and Python. You’ll also cover techniques for vector search with images, fine-tuning models for improved performance, and the use of clip models for image similarity search in Elasticsearch. Finally, you’ll explore retrieval-augmented generation (RAG) and learn to integrate ChatGPT with Elasticsearch to leverage vectorized data, ELSER's capabilities, and RRF's refined search mechanism. By the end of this NLP book, you’ll have all the necessary skills needed to implement and optimize vector search in your projects with Elastic.
Publication date:
November 2023
Publisher
Packt
Pages
240
ISBN
9781805121022

 

Part 1:Fundamentals of Vector Search

Explore the foundational elements of vector search with Elastic in this section. Beginning with an introduction to vectors and embeddings, this part lays the groundwork to understand their role in data representation and search. This part is essential for grasping the basic concepts and methodologies that form the bedrock of advanced vector search techniques, tailored for newcomers and experienced practitioners alike.

This part has the following chapters:

  • Chapter 1, Introduction to Vectors and Embeddings
  • Chapter 2, Getting Started with Vector Search in Elastic
About the Authors
  • Bahaaldine Azarmi

    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.

    Browse publications by this author
  • Jeff 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.

    Browse publications by this author
Vector Search for Practitioners with Elastic
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