Optimize your search capabilities in Elastic by operationalizing and fine-tuning vector search and enhance your search relevance while improving overall search performance
Key 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 Description
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 begins by teaching you about NLP and the functionality of Elastic in NLP processes. Next, 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 for
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.
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