Vector Search for Practitioners with Elastic: A toolkit for building NLP solutions for search, observability, and security using vector search

Optimize your search capabilities in Elastic by operationalizing and fine-tuning vector search and enhance your search relevance while improving overall search performance

Key Features:

• Install, configure, and optimize the ChatGPT-Elasticsearch plugin with a focus on vector data
• Learn how to load transformer models, generate vectors, and implement vector search with Elastic
• Develop a practical understanding of vector search, including a review of current vector databases
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description:

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.

What You Will Learn:

• Optimize performance by harnessing the capabilities of vector search
• Explore image vector search and its applications
• Detect and mask personally identifiable information
• Implement log prediction for next-generation observability
• Use vector-based bot detection for cybersecurity
• Visualize the vector space and explore Search.Next with Elastic
• Implement a RAG-enhanced application using Streamlit

Who this book is for:

If you're a data professional with experience in Elastic observability, search, or cybersecurity and are looking to expand your knowledge of vector search, this book is for you. This book provides practical knowledge useful for search application owners, product managers, observability platform owners, and security operations center professionals. Experience in Python, using machine learning models, and data management will help you get the most out of this book.

在Elastic中優化您的搜索能力，通過操作和微調向量搜索來增強搜索相關性，同時提高整體搜索性能。

主要特點：
- 安裝、配置和優化ChatGPT-Elasticsearch插件，重點關注向量數據
- 學習如何加載轉換器模型，生成向量，並在Elastic中實現向量搜索
- 發展對向量搜索的實用理解，包括對當前向量數據庫的評估
- 購買印刷版或Kindle電子書將包括免費的PDF電子書

書籍描述：
儘管自然語言處理（NLP）主要用於搜索用例，但本書旨在激發您開始使用向量來克服同樣重要的領域挑戰，如可觀察性和網絡安全。本書的章節主要關注將向量搜索與Elastic集成，以增強其搜索、可觀察性和網絡安全能力。

本書首先介紹NLP和Elastic在NLP過程中的功能。接下來，您將深入研究資源需求，了解向量如何存儲在密集向量類型中，以及快速響應時間所需的特定頁面緩存需求。隨著進一步的學習，您將發現各種調優技術和策略，以改善機器學習模型部署，包括節點擴展、配置調優以及使用Rally和Python進行負載測試。您還將涵蓋圖像向量搜索技術、優化模型以提高性能的方法，以及在Elasticsearch中使用clip模型進行圖像相似性搜索的方法。最後，您將探索檢索增強生成（RAG）並學習將ChatGPT與Elasticsearch集成，以利用向量化數據、ELSER的能力和RRF的精緻搜索機制。

通過閱讀本書，您將具備在Elastic項目中實施和優化向量搜索所需的所有必要技能。

您將學到什麼：
- 通過利用向量搜索的能力來優化性能
- 探索圖像向量搜索及其應用
- 檢測和遮蔽個人身份信息
- 實現下一代可觀察性的日誌預測
- 使用基於向量的機器人檢測進行網絡安全
- 可視化向量空間，並使用Elastic探索Search.Next
- 使用Streamlit實現增強的RAG應用程式

本書適合對Elastic可觀察性、搜索或網絡安全有經驗的數據專業人士，並希望擴展對向量搜索的了解。本書提供了實用的知識，對搜索應用程式擁有者、產品經理、可觀察性平台擁有者和安全運營中心專業人員非常有用。具備Python、使用機器學習模型和數據管理的經驗將有助於您充分利用本書的內容。