VECTOR DATABASE & RAG ENGINEERING: DESIGNING SCALABLE, LOW-LATENCY RETRIEVAL SYSTEMS FOR LLMS, AI AGENTS, AND PRODUCTION APPLICATIONS

Unlock the full power of Large Language Models (LLMs), AI agents, and production-grade AI applications with Vector Database & RAG Engineering. This advanced guide equips developers, ML engineers, and data scientists with the expertise to design scalable, low-latency retrieval systems that power modern AI workflows.

Inside, you’ll discover:

• How vector databases underpin retrieval-augmented generation (RAG) and real-time AI reasoning.

• Step-by-step architectural patterns for building high-performance, production-ready retrieval systems.

• Expert insights into embedding strategies, indexing, graph orchestration, and memory management.

• Hands-on, production-aware Python examples with LangChain and LangGraph, taking you from minimal prototypes to enterprise-ready deployments.

• Techniques for optimization, testing, debugging, and secure AI deployment, including considerations for GDPR and data governance.

Whether you’re designing intelligent assistants, multimodal search engines, or agentic automation pipelines, this book bridges theory and practice, giving you actionable frameworks and expert best practices that go far beyond tutorials.

Transform your AI projects with systems that are robust, efficient, and ready for real-world scale—all guided by a seasoned AI engineer and technical author with 10+ years of applied experience.