The Knowledge Engine: Building RAG Systems: Retrieval-Augmented Generation with Python and Vector Databases

Name: The Knowledge Engine: Building RAG Systems: Retrieval-Augmented Generation with Python and Vector Databases
Brand: Independently Published
SKU: 9798258793430
Price: 2694.0 INR
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by Richard Boozman

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ISBN13: 9798258793430
Binding: Paperback
Subject: N/A
Publisher: Independently Published
Publisher Imprint: Independently Published
Publication Date: May 4, 2026
Pages: 316
Original Price: USD 24.99
Language: English
Edition: N/A
Item Weight: 422 grams
BISAC Subject(s): Languages / Python

About The Book

LLMs are powerful.
But without the right data, they are limited.

Retrieval Augmented Generation, RAG, transforms AI systems by combining language models with external knowledge sources, enabling accurate, context aware, and up to date responses.

"The Knowledge Engine" is a practical, hands on guide to building RAG systems using Python and modern vector database technologies.

This book shows you how to design intelligent systems that retrieve, reason, and generate with precision.

Why RAG is essential for modern AI

Standalone models struggle with:

outdated knowledge
hallucinations
lack of domain specific context
limited accuracy in complex queries

RAG solves these problems by integrating retrieval systems with generation models.

With RAG, you can:

connect AI to real data sources
improve accuracy and relevance
reduce hallucinations
build domain specific AI systems
create scalable knowledge driven applications

What you will learn

fundamentals of retrieval augmented generation
how vector databases work
embeddings and similarity search
building retrieval pipelines
integrating LLMs with external data
chunking and indexing strategies
optimizing retrieval performance
evaluation and improvement of RAG systems
scaling and deploying RAG applications
monitoring and maintaining knowledge systems

From documents to intelligent systems

Throughout the book, you will learn how to:

convert raw data into searchable embeddings
design efficient retrieval systems
connect retrieval pipelines with generation models
build reliable AI applications
optimize performance and cost
deploy scalable RAG systems

Each chapter is focused on practical implementation.

Practical applications

enterprise knowledge assistants
document search and analysis systems
customer support automation
internal company knowledge bases
AI powered research tools

These examples reflect real world use cases.

Who this book is for

AI engineers
machine learning engineers
data scientists
backend developers working with AI
professionals building knowledge systems

If you want to build AI systems that are accurate, context aware, and connected to real data, this book provides the roadmap.

Retrieve with precision.
Generate with intelligence.
Build knowledge driven AI systems.

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