Azarmi / Banon / Vestal Vector Search for Practitioners with Elastic

A toolkit for building NLP solutions for search, observability, and security using vector search
1. Auflage 2024
ISBN: 978-1-80512-741-3
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 240 Seiten

"This book delves into the practical applications of vector search in Elastic and embodies a broader philosophy. It underscores the importance of search in the age of Generative Al and Large Language Models. This narrative goes beyond the 'how' to address the 'why' - highlighting our belief in the transformative power of search and our dedication to pushing boundaries to meet and exceed customer expectations." Shay Banon Founder & CTO at ElasticKey Features - Install, configure, and optimize the ChatGPT-Elasticsearch plugin with a focus on vector data
- Learn how to load transformer models, generate vectors, and implement vector search with Elastic
- Develop a practical understanding of vector search, including a review of current vector databases
- Purchase of the print or Kindle book includes a free PDF eBook
Book DescriptionWhile 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.What you will learn - 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
Who this book is forIf 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.

Autoren/Hrsg.

Azarmi, Bahaaldine

Banon, Shay

Vestal, Jeff

Weitere Mitwirkende

Banon, Shay

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie

Inhaltsverzeichnis

Table of Contents - Introduction to Vectors and Embeddings
- Getting started with Vector Search in Elastic
- Model Management and Vector Considerations in Elastic
- Performance Tuning - Working with data
- Image Search
- Redacting Personal Identifiable Information Using Elasticsearch
- Next Generation of Observability Powered by Vectors
- The Power of Vectors and Embedding in Bolstering Cybersecurity
- Retrieval Augmented Generation With Elastic
- Building an Elastic Plugin for ChatGPT

Über Autor(innen)

Azarmi Bahaaldine :

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.Vestal Jeff :

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.Banon Shay :

Founder and CTO at Elastic