Building Neo4j-Powered Applications with LLMs: Create LLM-driven search and recommendations applications with Haystack, LangChain4j, and Spring AI

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Key benefits

Design vector search and recommendation systems with LLMs using Neo4j GenAI, Haystack, Spring AI, and LangChain4j

Apply best practices for graph exploration, modeling, reasoning, and performance optimization

Build and consume Neo4j knowledge graphs and deploy your GenAI apps to Google Cloud

Purchase of the print or Kindle book includes a free PDF eBook

Description

Embark on an expert-led journey into building LLM-powered applications using Retrieval-Augmented Generation (RAG) and Neo4j knowledge graphs. Written by Ravindranatha Anthapu, Principal Consultant at Neo4j, and Siddhant Agrawal, a Google Developer Expert in GenAI, this comprehensive guide is your starting point for exploring alternatives to LangChain, covering frameworks such as Haystack, Spring AI, and LangChain4j. As LLMs (large language models) reshape how businesses interact with customers, this book helps you develop intelligent applications using RAG architecture and knowledge graphs, with a strong focus on overcoming one of AI’s most persistent challenges—mitigating hallucinations. You'll learn how to model and construct Neo4j knowledge graphs with Cypher to enhance the accuracy and relevance of LLM responses. Through real-world use cases like vector-powered search and personalized recommendations, the authors help you build hands-on experience with Neo4j GenAI integrations across Haystack and Spring AI. With access to a companion GitHub repository, you’ll work through code-heavy examples to confidently build and deploy GenAI apps on Google Cloud. By the end of this book, you’ll have the skills to ground LLMs with RAG and Neo4j, optimize graph performance, and strategically select the right cloud platform for your GenAI applications.

Who is this book for?

This LLM book is for database developers and data scientists who want to leverage knowledge graphs with Neo4j and its vector search capabilities to build intelligent search and recommendation systems. Working knowledge of Python and Java is essential to follow along. Familiarity with Neo4j, the Cypher query language, and fundamental concepts of databases will come in handy.

What you will learn

Design, populate, and integrate a Neo4j knowledge graph with RAG

Model data for knowledge graphs

Integrate AI-powered search to enhance knowledge exploration

Maintain and monitor your AI search application with Haystack

Use LangChain4j and Spring AI for recommendations and personalization

Seamlessly deploy your applications to Google Cloud Platform

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Shipping Details

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Unfortunately, due to several restrictions, we are unable to ship to the following countries:

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Building Neo4j-Powered Applications with LLMs: Create LLM-driven search and recommendations applications with Haystack, LangChain4j, and Spring AI

What do you get with Print?