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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

How is Elastic playing a role in this space?

Now, what role does Elastic play here? There are multiple ways you can leverage Elastic – including Elasticsearch. Elasticsearch is a distributed and highly scalable data store. Its specialty is information retrieval. It’s one thing to run the preceding code in a notebook – it’s another to make it operational at scale and to be accessed by hundreds, thousands, or millions of users.

Furthermore, training can be long as it requires the algorithm to be able to access a large amount of data, indexed for fast access, at scale. There are very few data stores that are versatile enough to be able to cope with structured and unstructured data at the same time, manage a wide range of data types, and be scalable for ingestion and search.

Elasticsearch is a unique choice, not only because of its technical attributes but also because of its vibrant community and large adoption. As mentioned earlier, the field of AI-based...

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