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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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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

Summary

In this chapter, we delved into the intricacies of the Hugging Face ecosystem and the capabilities of Elasticsearch’s Eland Python library, offering practical examples for using embedding models within Elasticsearch. We explored the Hugging Face platform, highlighting its datasets, model selection, and the potential of its Spaces. Furthermore, we provided a hands-on approach to the Eland library, illustrating its functionalities and addressing pivotal considerations such as mappings, ML nodes, and model integration. We also touched upon the nuances of cluster capacity planning, emphasizing RAM, disk size, and CPU considerations. Finally, we underscored several storage efficiency tactics, focusing on dimensionality reduction, quantization, and mapping settings to ensure optimal performance and resource conservation for your Elasticsearch cluster.

In the next chapter, we will dive into the operational phase of working with data and learn how to tune performance for...

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