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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

Overview of image search

Image search is a specialized data retrieval methodology that focuses on finding images through the analysis and comparison of their visual content. The demand for effective image search technology has grown exponentially over the last few years due to an explosion in digital imagery across the internet, social media, and other digital platforms.

The evolution of image search

The origins of image search can be traced back to the early days of the internet when search engines could only analyze text associated with an image, such as filenames, alt text, or surrounding textual content, to match search queries. However, these methods had their limitations, as the actual content of images remained largely ignored.

With advancements in artificial intelligence (AI) and machine learning (ML), the capabilities of image search have greatly expanded. Now, modern image search technology can analyze the actual visual content of images, thanks to the development...

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