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  • Book Overview & Buying Vector Search for Practitioners with Elastic
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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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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

Evolution of search experience

We are now going to see how users’ demand for a better search experience requires us to consider other techniques than just keyword-based search. In this section, we will approach the limitations of keyword-based search, understand what vector representation entails, and how the meta representation HNSW emerged to facilitate information retrieval with vector.

The limits of keyword-based search

For those of you who are comparatively new to the subject matter, before we talk about vector representation, we need to understand why the industry and keyword-based search experience have reached their limits, failing to fully meet end-user requirements.

Keyword-based search relies on exact matches between the user query and the terms contained in documents, which could lead to missed relevant results if the search system is not refined enough with synonyms, abbreviations, alternative phrasings, and so on. Therefore, it is important for the search...

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