{"product_id":"138969","title":"A Practical Guide to Vector Search Using Elasticsearch ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jXPeMA8fF0PSq4pYxxzQkmHVTvPCh.png?v=1765071356\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e A Practical Guide to Vector Search Using Elasticsearch \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/127948088\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eLearn how to optimize vector search, observability, cybersecurity, and ChatGPT integration with Elasticsearch!\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003eWhile natural language processing (NLP) is primarily used for search use cases, this book aims to inspire others to leverage vectors to solve important domain challenges such as observability and cybersecurity.\u003cbr\u003e Each chapter focuses on integrating vector search with Elasticsearch to improve not only search use cases but also observability and cybersecurity capabilities.\u003cbr\u003e\u003cbr\u003e This book first introduces NLP and Elastic's capabilities in NLP processes.\u003cbr\u003e Next, we'll look at how vectors are stored in a dense vector format, along with resource requirements and specific page cache requirements for fast response times.\u003cbr\u003e As you continue reading, you'll discover various tuning techniques and strategies to improve your machine learning model deployment, including node scaling, configuration tuning, and load testing using Rally and Python. \u003cbr\u003eWe also cover vector search techniques using images, model fine-tuning for performance, and how to use the CLIP model for image similarity search in Elasticsearch.\u003cbr\u003e Finally, we'll explore retrieval-augmented generation (RAG) and learn how to integrate ChatGPT with Elasticsearch to leverage vectorized data, the capabilities of ELSER, and the refined search mechanisms of RRF.\u003cbr\u003e By the end of this book, you'll have all the skills you need to implement and optimize vector search in your projects using Elasticsearch.\u003cbr\u003e\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e \",\"\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003e[Part 1] The Basics of Vector Search\u003cbr\u003e\u003cbr\u003e ▣ 01: Introduction to Vectors and Embeddings\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 1.1 Examining the Role of Supervised and Unsupervised Learning in Vector Search\u003cbr\u003e ___What is an embedding\/vector?\u003cbr\u003e ___What problems are vectors used to solve?\u003cbr\u003e ___Developer Environment \u003cbr\u003e___Hugging Face\u003cbr\u003e ___Accelerating the market environment and developer experience\u003cbr\u003e 1.2 Use Cases and Applications\u003cbr\u003e ___AI-powered search\u003cbr\u003e ___Named Entity Recognition (NER)\u003cbr\u003e ___Sentiment Analysis\u003cbr\u003e ___Text classification\u003cbr\u003e ___Questions and Answers (QA)\u003cbr\u003e ___Text Summary\u003cbr\u003e 1.3 What role does Elastic play in this area?\u003cbr\u003e ___Basic Concepts of Observability and Cybersecurity\u003cbr\u003e summation\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ 02: Starting a Vector Search in Elastic\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 2.1 Vector Search Previous Search Experience in Elastic\u003cbr\u003e ___How Data Types Affect Relevance\u003cbr\u003e ___Relevance Model\u003cbr\u003e 2.2 Evolution of the search experience\u003cbr\u003e Limitations of keyword-based search\u003cbr\u003e ___vector representation\u003cbr\u003e 2.3 New vector data type and vector search query API\u003cbr\u003e ___Sparse and dense vectors\u003cbr\u003e ___Getting Started with Elastic Cloud\u003cbr\u003e ___Dense Vector Mapping\u003cbr\u003e ___Brute-force kNN search\u003cbr\u003e ___kNN search\u003cbr\u003e summation\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Part 2] Advanced Applications and Performance Optimization\u003cbr\u003e\u003cbr\u003e ▣ 03: Model Management and Vector Considerations in Elastic\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 3.1 Hugging Face \u003cbr\u003e___Model Hub\u003cbr\u003e ___Datasets\u003cbr\u003e ___Spaces\u003cbr\u003e 3.2 Eland\u003cbr\u003e Importing an embedding model from ___Hugging Face to Elasticsearch\u003cbr\u003e ___Configuring Elasticsearch Authentication\u003cbr\u003e ___Get models from Hugging Face Hub\u003cbr\u003e Download the ___model\u003cbr\u003e ___Loading a model into Elasticsearch\u003cbr\u003e Starting the ___ model\u003cbr\u003e Deploying the ___model\u003cbr\u003e ___Create a query vector\u003cbr\u003e 3.3 Creating Vectors Inside Elasticsearch\u003cbr\u003e 3.4 Establishing Cluster Resource Planning\u003cbr\u003e ___CPU and memory requirements\u003cbr\u003e ___Disk Requirements\u003cbr\u003e ___Index Disk Usage Analysis API\u003cbr\u003e 3.5 Machine Learning Node Capacity\u003cbr\u003e 3.6 Storage Efficiency Strategies\u003cbr\u003e ___dimensional reduction\u003cbr\u003e ___quantization\u003cbr\u003e Excluding dense_vector from ___source\u003cbr\u003e summation\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ 04: Performance Tuning - Verification through Data\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 4.1 Deploying the NLP Model\u003cbr\u003e ___Loading the model into Elasticsearch\u003cbr\u003e ___Settings related to model deployment\u003cbr\u003e 4.2 Load Testing\u003cbr\u003e ___Rally\u003cbr\u003e ___Memory (RAM) Usage Prediction\u003cbr\u003e ___Slowdown problem solved\u003cbr\u003e summation\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Part 3] Special Use Cases\u003cbr\u003e\u003cbr\u003e ▣ 05: Image Search\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e5.1 Image Search Overview\u003cbr\u003e ___Image search advancements\u003cbr\u003e ___Image search method\u003cbr\u003e ___The role of vector similarity search\u003cbr\u003e ___Image search example\u003cbr\u003e 5.2 Image Vector Search\u003cbr\u003e ___Image Vectorization\u003cbr\u003e ___Indexing Image Vectors in Elasticsearch\u003cbr\u003e ___k-nearest neighbor (kNN) search\u003cbr\u003e ___Challenges and Limitations in Image Search\u003cbr\u003e 5.3 Multimodal Model for Vector Search\u003cbr\u003e ___The Need for Multimodality\u003cbr\u003e ___Understanding the vector space of multimodal models\u003cbr\u003e ___Introducing the OpenAI clip-ViT-B-32-multilingual-v1 model\u003cbr\u003e ___Applying vector search to various media types\u003cbr\u003e summation\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ 06: Removing Personally Identifiable Information Using Elasticsearch\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 6.1 PII and Redaction Overview\u003cbr\u003e Data types that may include ___PII\u003cbr\u003e Risks of PII stored in ___logs\u003cbr\u003e ___Types of PII leaks and losses\u003cbr\u003e 6.2 PII Removal Using NER Model and Regular Expression Patterns\u003cbr\u003e ___NER model\u003cbr\u003e ___regular expression pattern\u003cbr\u003e Combining NER model and regular expression (Grok) patterns for ___PII removal \u003cbr\u003e6.3 Elasticsearch's PII Removal Pipeline\u003cbr\u003e ___Creating fake PII\u003cbr\u003e ___Basic pipeline settings\u003cbr\u003e ___Expected Results\u003cbr\u003e 6.4 Options for Extending and Detailing the PII Removal Pipeline\u003cbr\u003e ___Customizing the Basic PII Example\u003cbr\u003e ___Duplicate the pipeline and create a new version for the new data stream.\u003cbr\u003e ___Fine-tuning a NER model for a specific dataset\u003cbr\u003e ___The need for context-aware technology and how to apply it\u003cbr\u003e summation\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ 07: Vector-based next-generation observability\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 7.1 Introduction to Observability and Its Importance in Modern Software Systems\u003cbr\u003e ___Observability - Key Elements\u003cbr\u003e ___Log Analysis and Its Role in Observability\u003cbr\u003e 7.2 A New Approach to Applying Vectors and Embeddings to Log Analysis\u003cbr\u003e ___Approach 1 - Train or fine-tune an existing model for logs\u003cbr\u003e ___Approach 2 - Generate human-readable descriptions, then vectorize them.\u003cbr\u003e 7.3 Log vectorization\u003cbr\u003e ___synthetic log\u003cbr\u003e ___Log Expansion with OpenAI \u003cbr\u003e7.4 Log Semantic Search\u003cbr\u003e ___log vector index\u003cbr\u003e ___Model Loading\u003cbr\u003e ___Collection Pipeline\u003cbr\u003e ___Semantic Search\u003cbr\u003e summation\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ 08: The Impact of Vectors and Embeddings on Strengthening Cybersecurity\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 8.1 Understanding the Importance of Email Phishing Detection\u003cbr\u003e ___What is phishing?\u003cbr\u003e ___Various types of phishing attacks\u003cbr\u003e ___Statistics on the frequency of phishing attacks\u003cbr\u003e ___The Challenges of Phishing Email Detection\u003cbr\u003e ___The role of automatic detection\u003cbr\u003e ___Supplementing existing technologies with natural language processing technology\u003cbr\u003e 8.2 Introduction to ELSER\u003cbr\u003e 8.3 The Role of ELSER in Generative AI\u003cbr\u003e 8.4 Enron Email Data Set (Ham or Spam)\u003cbr\u003e 8.5 Seeing ELSER in action\u003cbr\u003e ___Hardware Considerations\u003cbr\u003e ___Download the ELSER model to Elasticsearch\u003cbr\u003e ___Index setup and data collection pipeline settings\u003cbr\u003e Semantic search using ___ELSER\u003cbr\u003e Limitations of ___ELSER\u003cbr\u003e summation\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Part 4] Innovative Integration and Future Direction\u003cbr\u003e\u003cbr\u003e ▣ 09: Augmented Search Creation with Elastic\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 9.1 Preparing for RAG Enhanced Search Using ELSER and RRF\u003cbr\u003e Semantic search using ___ELSER \u003cbr\u003eSummary of Essential Considerations for ___RAG\u003cbr\u003e ELSER Integration Using ___RRF\u003cbr\u003e ___Language Model and RAG\u003cbr\u003e 9.2 In-Depth Case Study - Building a RAG-Based CookBot\u003cbr\u003e ___Dataset Overview - Explore the Allrecipes.com Dataset\u003cbr\u003e ___Preparing data for RAG enhanced search\u003cbr\u003e RRF finder using ___ELSER\u003cbr\u003e ___Using the search engine and creating a generator\u003cbr\u003e summation\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ 10: Building an Elastic Plugin for ChatGPT\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 10.1 Contextual Basics\u003cbr\u003e ___Dynamic Context Paradigm\u003cbr\u003e 10.2 DCL Plugin - Structure and Operation\u003cbr\u003e 10.3 Implementing DCL\u003cbr\u003e ___Get the latest information from the Elastic documentation\u003cbr\u003e ___Raising the Data Level with Embedchain\u003cbr\u003e ___Integrate with ChatGPT to create real-time conversation partners\u003cbr\u003e ___distribution\u003cbr\u003e summation\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Appendix: Creating Elastic Guide GPT\u003c\/b\u003e\n\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e \",\"\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate4558\/MidCate002\/455710469.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e \",\"\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003e★ What this book covers ★\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ◎ Optimizing performance using vector search functions\u003cbr\u003e ◎ Exploring image vector search and its applications \u003cbr\u003e◎ Detecting and masking personally identifiable information\u003cbr\u003e ◎ Implementing log analysis and search for next-generation observability\u003cbr\u003e ◎ Utilizing vector-based bot detection for cybersecurity\u003cbr\u003e ◎ Explore vector space visualization and Elasticsearch's latest search capabilities.\u003cbr\u003e ◎ Implementing a RAG-enhanced application using Streamlit\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e \"]\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e June 26, 2024\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 276 pages | 175*235*14mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791158395223 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default Title","offer_id":43893333753898,"sku":"138969","price":38.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/f6e86c1f04da3cdb76502cf522d7c857.jpg?v=1765396761","url":"https:\/\/librairie.coreenne.fr\/en\/products\/138969","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}