# 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.

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- Market: US
- Language: en-US
- Currency: USD
- Category: Books, Music & Hobbies > Books, Music & Movies > Books & Comics
- Price: 37.99 USD
- Previous price: 49.99 USD
- Discount: 24%
- Price checked: 2026-11-14T05:50:21.984Z
- Rating: 5 / 5
- Reviews: 9

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## 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.

## 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.

## 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

## Before you buy

- 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.

## 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 feedback

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.

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## Add-ons

- [Context Engineering for Multi-Agent Systems: Move beyond prompting to build a Context Engine, a transparent architecture of context and reasoning](https://best-deals.pro/visit/context-engineering-multi-agent-systems-book)
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## Images

![Unlocking Data with Generative AI and RAG, 2nd Edition — 1](https://best-deals.pro/i/-EENRJbu0qLCKPnMB83bPTknKJa_f46XhRjFzeLqeJ-QXJPabgjNi-4_g4FbzqCBetbRhNBZ2nT2V8RAgTyQRcHq2Sda3UjziQS-HUcpapKiHoCoUurT.jpg)