가야대학교 분성도서관
주메뉴
자료검색
- Home
- 상세정보
상세정보
부가기능
Vector search for practitioners with elastic : a toolkit for building NLP solutions for search, observability, and security using vector search / [electronic resource]
상세 프로파일
| 자료유형 | E-Book |
|---|---|
| 개인저자 | Azarmi, Bahaaldine, author. Vestal, Jeff, author. Banon, Shay, writer of foreword. |
| 서명/저자사항 | Vector search for practitioners with elastic[electronic resource] :a toolkit for building NLP solutions for search, observability, and security using vector search /Bahaaldine Azarmi, Jeff Vestal ; foreword by Shay Banon. |
| 판사항 | 1st edition. |
| 발행사항 | Birmingham, UK : Packt Publishing Ltd., 2023. |
| 형태사항 | 1 online resource |
| 소장본 주기 | Added to collection customer.56279.3 |
| ISBN | 9781805127413 1805127411 |
| 내용주기 | Cover -- Title Page -- Copyright and Credit -- Dedication -- Foreword -- Contributors -- Table of Contents -- Preface -- Part 1: Fundamentals of Vector Search -- Chapter 1: Introduction to Vectors and Embeddings -- Exploring the roles of supervised and unsupervised learning in vector search -- What's an embedding/vector? -- What challenges are vectors solving? -- The developer experience -- Hugging Face -- The market landscape and how it has accelerated the developer experience -- Use cases and domains of application -- AI-based search -- Named Entity Recognition (NER) -- Sentiment analysis -- Text classification -- Question-answering (QA) -- Text summarization -- How is Elastic playing a role in this space? -- A primer on observability and cybersecurity -- Summary -- Chapter 2: Getting Started with Vector Search in Elastic -- Search experience in Elastic before vectors -- Data type and its impact on relevancy -- The relevancy model -- Evolution of search experience -- The limits of keyword-based search -- Vector representation -- The new vector data type and the vector search query API -- Sparse and dense vectors -- An Elastic Cloud quick start -- Dense vector mapping -- Brute-force KNN search -- KNN search -- Summary -- Part 2: Advanced Applications and Performance Optimization -- Chapter 3: Model Management and Vector Considerations in Elastic -- Technical requirements -- Hugging Face -- Model Hub -- Datasets -- Spaces -- Eland -- Loading a Sentence Transformer from Hugging Face into Elasticsearch -- Configuring Elasticsearch authentication -- Loading a model from the Hugging Face Hub -- Downloading the model -- Loading the model into Elasticsearch -- Starting the model -- Deploying the model -- Generating a vector for a query -- Generating vectors in Elasticsearch -- Planning for cluster capacity and resources. CPU and memory requirements -- Disk requirements -- Analyze Index Disk Usage API -- ML node capacity -- Storage efficiency strategies -- Reducing dimensionality -- Quantization -- Excluding dense_vector from _source -- Summary -- Chapter 4: Performance Tuning -- Working with Data -- Deploying an NLP model -- Loading a model into Elasticsearch -- Model deployment configurations -- Load testing -- Rally -- RAM estimation -- Troubleshooting slowdown -- Summary -- Part 3: Specialized Use Cases -- Chapter 5: Image Search -- Overview of image search -- The evolution of image search -- The mechanism behind image search -- The role of vector similarity search -- Image search in practice -- Vector search with images -- Image vectorization -- Indexing image vectors in Elasticsearch -- k-Nearest Neighbor (kNN) search -- Challenges and limitations with image search -- Multi-modal models for vector search -- Introduction and rationale -- Understanding the concept of vector space in multi-modal models -- Introduction to the OpenAI clip-ViT-B-32-multilingual-v1 model -- Implementing vector search for diverse media types -- Summary -- Chapter 6: Redacting Personal Identifiable Information Using Elasticsearch -- Overview of PII and redaction -- Types of data that may contain PII -- Risks of storing PII in logs -- How PII is leaked or lost -- Redacting PII with NER models and regex patterns -- NER models -- Regex patterns -- Combining NER models and regex (or grok) patterns for PII redaction -- PII redaction pipeline in Elasticsearch -- Generating synthetic PII -- Installing the default pipeline -- Expected results -- Expanding and customizing options for the PII redaction pipeline in Elasticsearch -- Customizing the default PII example -- Cloning the pipeline to create different versions for different data streams -- Fine-tuning NER models for particular datasets. Logic for contextual awareness -- Summary -- Chapter 7: Next Generation of Observability Powered by Vectors -- Introduction to observability and its importance in modern software systems -- Observability: main pillars -- Log analytics and its role in observability -- A new approach-applying vectors and embeddings to log analytics -- Approach 1-training or fine-tuning an existing model for logs -- Approach 2-generating human-understandable descriptions and vectorizing these descriptions -- Log vectorization -- Synthetic log -- Expanding logs at write with OpenAI -- Semantic search on our logs -- Building a query using log vectorization -- Loading a model -- Ingest pipeline -- Semantic search -- Summary -- Chapter 8: The Power of Vectors and Embedding in Bolstering Cybersecurity -- Technical requirements -- Understanding the importance of email phishing detection -- What is phishing? -- Different types of phishing attacks -- Statistics on the frequency of phishing attacks -- Challenges in detecting phishing emails -- Role of automated detection -- Augmenting existing techniques with natural language processing -- Introducing ELSER -- The role of ELSER in GenAI -- Introduction to the Enron email dataset (ham or spam) -- Seeing ELSER in action -- Hardware consideration -- Downloading the ELSER model in Elastic -- Setting up the index and ingestion pipeline -- Semantic search with ELSER -- Limitations of ELSER -- Summary -- Part 4: Innovative Integrations and Future Directions -- Chapter 9: Retrieval Augmented Generation with Elastic -- Preparing for RAG-enhanced search with ELSER and RRF -- Semantic search with ELSER -- A recap of essential considerations for RAG -- Integrating ELSER with RRF -- Language models and RAG -- In-depth case study-implementing a RAG-enhanced CookBot -- Dataset overview -- an introduction to the Allrecipes.com dataset. Preparing data for RAG-enhanced search -- Building the retriever-RRF with ELSER -- Leveraging the retriever and implementing the generator -- Summary -- Chapter 10: Building an Elastic Plugin for ChatGPT -- Contextual foundations -- The paradigm of dynamic context -- Dynamic Context Layer plugin vision-architecture and flow -- Building the DCL -- Fetching the latest information from Elastic documentation -- Elevating data with Embedchain -- Integrating with ChatGPT-creating a real-time conversationalist -- Deployment -- Summary -- Index -- Other Books You May Enjoy. |
| 요약 | 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. |
| 일반주제명 | Natural language processing (Computer science) |
| 언어 | 영어 |
| 기타형태 저록 | Print version:18051210229781805121022 |
| 대출바로가기 | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=3743713 |
소장정보
- 소장정보
인쇄
| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00030481 | 006.3/5 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출가능 |
|
카피라이터
