Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Vector Search for Practitioners with Elastic
  • Table Of Contents Toc
  • Feedback & Rating feedback
Vector Search for Practitioners with Elastic

Vector Search for Practitioners with Elastic

By : Bahaaldine Azarmi, Jeff Vestal, Vestal
4.9 (15)
close
close
Vector Search for Practitioners with Elastic

Vector Search for Practitioners with Elastic

4.9 (15)
By: Bahaaldine Azarmi, Jeff Vestal, Vestal

Overview of 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.
Table of Contents (17 chapters)
close
close
Free Chapter
1
Part 1:Fundamentals of Vector Search
4
Part 2: Advanced Applications and Performance Optimization
7
Part 3: Specialized Use Cases
12
Part 4: Innovative Integrations and Future Directions

What this book covers

Chapter 1, Introduction to Vectors and Embeddings, covers the essentials of embeddings in machine learning.

Chapter 2, Getting Started with Vector Search in Elastic, explores the evolution of search in Elastic, from traditional keyword-based methods to advanced vector search.

Chapter 3, Model Management and Vector Considerations in Elastic, dives into managing embedding models in Elasticsearch, exploring Hugging Face’s platform, Elastic’s Eland library, and integration strategies.

Chapter 4, Performance Tuning—Working with Data, delves into optimizing vector search performance in Elasticsearch using ML model deployment tuning and node capacity estimation. This chapter will also cover load testing with Rally and troubleshooting kNN search response times.

Chapter 5, Image Search, explores the advancing field of image similarity search and its growing significance in discovery applications.

Chapter 6, Redacting Personal Identifiable Information Using Elasticsearch, covers how to build and tailor a PII Redaction Pipeline in Elasticsearch, crucial for data privacy and security.

Chapter 7, Next Generation of Observability Powered by Vectors, delves into integrating vectors with observability on the Elastic platform, focusing on log analytics, metric analytics, and application performance monitoring.

Chapter 8, The Power of Vectors and Embedding in Bolstering Cybersecurity, explores Elastic Learned Sparse EncodeR (ELSER) and its role in semantic search for cybersecurity. It explains ELSER’s capabilities in text analysis and phishing detection.

Chapter 9, Retrieval Augmented Generation with Elastic, dives into Retrieval Augmented Generation (RAG) in Elastic, blending lexical, vector, and contextual searches.

Chapter 10, Building an Elastic Plugin for ChatGPT, shows how to enhance ChatGPT’s context awareness with Elasticsearch and Embedchain, creating a Dynamic Contextual Layer (DCL) for up-to-date information retrieval.

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY