Design and Modeling Vector Database

De: Ajit Singh

Narrado por: Virtual Voice

This book, "Design and Modeling Vector Database," provides a comprehensive, hands-on, and academically rigorous introduction to the world of vector databases. It is designed to serve as the primary textbook for undergraduate and postgraduate courses on modern database systems, AI/ML engineering, and data science.


Philosophy:

My core philosophy is "Learn by Building." I believe that the most profound understanding comes from bridging the gap between theory and practice. This book demystifies the magic behind AI-powered search and retrieval by breaking down complex systems into their fundamental components. I move beyond rote memorization of APIs and focus on the "why" behind the "how"—exploring the data structures, algorithms, and system design principles that enable vector databases to perform their remarkable feats. The content is presented in the simplest possible terms, ensuring that a student with a basic understanding of programming and data structures can confidently master the material.


Key Features

1. Beginner to Advanced: The content is scaffolded to cater to students at all levels, from those new to databases to those with existing software engineering experience.
2. Comprehensive Coverage: Covers the entire lifecycle of a vector database, from theoretical modeling and design to implementation, deployment, and future trends.
3. Vendor-Agnostic Principles: While we use popular open-source tools for examples, the core principles taught are universal and applicable to any vector database system.
4. Focus on Applications: Strong emphasis on real-world applications such as semantic search, recommendation engines, and Retrieval-Augmented Generation (RAG) for LLMs.


To Whom This Book Is For

1. B.Tech/M.Tech Computer Science Students: As a core or elective textbook for courses on Database Management Systems, AI & Machine Learning, or Big Data.
2. Aspiring AI/ML Engineers and Data Scientists: Who need to understand how to build the data infrastructure for intelligent applications.
3. Software Developers and Architects: Who want to upskill and learn how to integrate vector search capabilities into their existing or new projects.
4. Self-Taught Learners and Enthusiasts: Who are curious about the technology powering the current AI revolution.