The Vector Database Handbook: Efficient Scaling for AI-Driven Search

By Thompson Carter Carter

Traditional databases store data. Vector databases understand it.

Semantic search, recommendations, and AI-powered retrieval systems all rely on vector databases. This book provides a clear, beginner-friendly introduction to vector databases and shows how tools like Pinecone, Milvus, and Weaviate power modern AI applications.

If you want to build systems that search by meaning instead of keywords, this book is your starting point.

What You'll Learn in This Book

What vector databases are and why they matter
How embeddings enable semantic search
Core concepts behind similarity search
Practical use cases for AI and LLM applications
How Pinecone, Milvus, and Weaviate compare
When to use managed vs. self-hosted solutions
Designing scalable retrieval systems for real projects

No prior experience with vector databases is required.

Who This Book Is For

This guide is ideal for:

Beginners in AI and machine learning

Software developers and data engineers

Developers building LLM-powered applications

Data scientists exploring semantic search

Startup founders and product builders

Basic programming knowledge is helpful but not required.

Why Vector Databases Matter

Keyword search breaks down when data grows complex.
Vector databases enable systems to:

Search by meaning and context

Power retrieval-augmented generation (RAG)

Scale AI-driven recommendations

Handle unstructured data efficiently

This book explains these ideas clearly, practically, and without unnecessary math.

• Format: Paperback
• Language: English
• ISBN: B0GK8WLZXP
• ISBN13: 9798242198012
• Release Date: January 2026
• Publisher: Independently Published
• Length: 200 Pages
• Weight: 0.60 lbs.
• Dimensions: 0.4" x 6.0" x 9.0"