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 begins by teaching you about NLP and the functionality of Elastic in NLP processes. Next, 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.

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Spis treści

Vector Search for Practitioners with Elastic. A toolkit for building NLP solutions for search, observability, and security using vector search eBook -- spis treści

• 1. Introduction to Vectors and Embeddings
• 2. Getting started with Vector Search in Elastic
• 3. Model Management and Vector Considerations in Elastic
• 4. Performance Tuning - Working with data
• 5. Image Search
• 6. Redacting Personal Identifiable Information Using Elasticsearch
• 7. Next Generation of Observability Powered by Vectors
• 8. The Power of Vectors and Embedding in Bolstering Cybersecurity
• 9. Retrieval Augmented Generation With Elastic
• 10. Building an Elastic Plugin for ChatGPT