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Vector Search for Practitioners with Elastic

Vector Search for Practitioners with Elastic

By : Bahaaldine Azarmi, Jeff Vestal, Vestal
4.9 (15)
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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)
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Part 1:Fundamentals of Vector Search
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Part 2: Advanced Applications and Performance Optimization
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Chapter 4: Performance Tuning – Working with Data
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Part 3: Specialized Use Cases
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Part 4: Innovative Integrations and Future Directions
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Chapter 10: Building an Elastic Plugin for ChatGPT
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Index
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Introduction to observability and its importance in modern software systems

Before getting into applying vectors to observability, it is important to take the time to understand the high-level concepts of observability itself.

Observability is the ability to understand the internal state of any layer that composes a system based on its external outputs. This was a relatively easy task when these systems were monoliths with multiple layers but produced a centralized log output that described the behavior of this “box.” However, the process became more and more complicated as companies adopted decentralized cloud-native architectures such as microservices. The output for observability here becomes decorrelated, distributed, and composed of multiple signals, logs, metrics, and traces, to list but a few. Most vendors out there, including Elastic, have responded to this by striving to provide an integrated observability experience with the main goal of reducing mean time...

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