Product guide

Unlocking Data with Generative AI and RAG, 2nd Edition

Unlocking Data with Generative AI and RAG is a second-edition guide to building retrieval-augmented AI agents with memory and graph-based context. Its 606-page coverage includes vector search, semantic caches, LangMem, Neo4j, LangChain, and practical RAG implementations, with basic Python and LLM knowledge recommended.

At a glance

Covers RAG fundamentals, graph-based retrieval, and agentic memory in one guide.
Includes code-oriented examples and pipelines spanning vectors, prompt strategies, and feedback loops.
The described material includes both conceptual foundations and practical implementation patterns.
A DRM-free PDF and access to Packt's Reader are claimed as free with the book, subject to email sign-up and proof of purchase.

Price

$37.99Save 24%

Reference price: ~~$49.99~~

Price checked: Nov 14, 2026

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Pros and cons

Pros

Broad coverage connects conventional RAG pipelines with graph-based knowledge and persistent agent memory.
The listed topics include practical tools such as Neo4j, LangChain, LangGraph, and LangMem.
The publisher describes hands-on code and production-oriented patterns, rather than a purely conceptual overview.

Cons

The book is aimed at readers already familiar with basic Python and large language models, so it may not suit a complete beginner.

Technical specifications

10 specifications

Book details
Publisher	Packt Publishing
Edition	2nd ed.
Publication date	December 30, 2025
Language	English
Print length	606 pages
ISBN-13	978-1806381654

Dimensions and installation
Weight listed	2.26 pounds
Dimensions	7.5 x 1.37 x 9.25 inches

Topics and tools
Memory topics	Working, episodic, semantic, and procedural memory
RAG topics	Graph-based retrieval, ontologies, vector search, and semantic caches

What to know before buying

The free digital materials require email sign-up and proof of purchase. The listing gives no detailed information about code repository access or which versions of the named tools the examples use.

Who is it for?

A good fit for

AI engineers, data scientists, and developers building RAG or agent-based systems.
Readers with basic Python and LLM knowledge who want to explore GraphRAG, vector stores, or agent memory.

Less suited to

Readers looking for a no-code introduction or a first course in Python and large language models.
Buyers seeking a narrowly focused reference on just one framework or tool.

Customer reviews on Amazon

5 / 5 · 9 ratings

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The available aggregate rating is entirely five-star, but the sample is small and no written customer feedback is supplied. That offers limited reassurance about reader satisfaction, but cannot establish which parts readers valued or how well the code and explanations work in practice.

Frequently asked questions

What does this book cover?

The described topics include RAG pipelines, graph-based retrieval, vector search, semantic caches, agent memory, and tools such as Neo4j, LangChain, and LangMem.

Who is the book intended for?

The listing identifies AI engineers, data scientists, and developers as its audience, and says basic knowledge of Python and LLMs will help readers.

Is a digital edition included?

The listing claims a DRM-free PDF and Packt Reader access with the book. Email sign-up and proof of purchase are required.

Does it include practical coding material?

The description says the book includes hands-on code and implementations covering RAG, memory types, vector stores, prompting strategies, and feedback loops.