A01=Nitin Borwankar
Author_Nitin Borwankar
Category=UFM
Category=UND
Category=UYQL
Category=UYQN
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
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eq_nobargain
eq_non-fiction
vector database embeddings LLMs Retrieval Augmented Generation (RAG) LLama2 langchain llamaindex
Product details
- ISBN 9781098177591
- Publication Date: 15 May 2026
- Publisher: O'Reilly Media
- Publication City/Country: US
- Product Form: Paperback
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The AI revolution is here, and at its core lies a game-changing technology that most developers haven't fully explored: vector databases. From powering semantic search to enabling large language models (LLMs) and generative AI, vector databases are reshaping how we build applications with unstructured data like text, images, and audio. But how do you go from curious to capable with this vital technology? That's where this book comes in.
In this hands-on guide, author Nitin Borwankar takes you through the "why, what, and how" of vector databases, starting with the basic theory behind vector embeddings and progressing to building applications with real-world tools. You'll learn about Word2vec, how to convert open source SQL databases like SQLite3 and PostgreSQL into vector databases, and integrate them into retrieval-augmented generation (RAG) applications. Whether you're a Python developer, data engineer, or ML practitioner, this book gives you the foundation to leverage vector databases confidently in your AI projects.
- Understand the connection between vector databases, embeddings, and LLMs
- Learn practical approaches for transforming SQL databases into vector databases
- Build RAG applications for both personal and enterprise use
- Apply vector databases to solve real-world AI challenges
- Learn how to use vector databases with LLMs to build applications
Nitin Borwankar is a seasoned data scientist and database professional with a background in the development and implementation of enterprise data solutions. With a career spanning over three decades, Nitin is known for his work on data science education, advocacy for the use of open-source tools for data science, and contributions to open-source machine learning curriculum. He is a frequent speaker at conferences and approaches AI and LLMs from a pragmatic data application perspective.
