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, youll 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, youll discover various tuning techniques and strategies to improve machine learning model deployment, including node scaling, configuration tuning, and load testing with Rally and Python. Youll 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, youll explore retrieval-augmented generation (RAG) and learn to integrate ChatGPT with Elasticsearch to leverage vectorized data, ELSERs capabilities, and RRFs refined search mechanism.

By the end of this NLP book, youll 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 youre 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

1. Introduction to Vectors and Embeddings
2. Getting started with Vector Search in Elastic
3. Model Management and Vector Considerations in Elastic
4. Performance Tuning - Working with data
5. Image Search
6. Redacting Personal Identifiable Information Using Elasticsearch
7. Next Generation of Observability Powered by Vectors
8. The Power of Vectors and Embedding in Bolstering Cybersecurity
9. Retrieval Augmented Generation With Elastic
10. Building an Elastic Plugin for ChatGPT