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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

Hugging Face

As discussed briefly in the introduction, the primary goal of Hugging Face is to democratize access to state-of-the-art NLP technologies and facilitate their adoption across various industries and applications. By providing an extensive library of pre-trained models (over 120,000 at the time of this writing), user-friendly APIs, and a collaborative environment for model sharing and fine-tuning, Hugging Face empowers developers and researchers to create advanced language processing applications with ease.

Building upon that foundation, Hugging Face doesn’t just stop at providing an extensive library; it also ensures streamlined access and effective application management. One of the standout features to this end is the Model Hub.

Model Hub

Hugging Face offers resources and services focused on the needs of both researchers and businesses. These include the Model Hub, which serves as a central repository for pre-trained models including inference APIs that...

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