Building Neo4j-Powered Applications with LLMs
Ravindranatha Anthapu Siddhant Agarwal Dr. Jim Webber Dr. Julian Risch
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Ravindranatha Anthapu Siddhant Agarwal Dr. Jim Webber Dr. Julian Risch
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.
将知识图谱与大语言模型真正落地的实践指南 1. 核心价值 本书聚焦于“Retrieval-Augmented Generation (RAG)”这一当下最具前景的生成式 AI 落地路径，系统化地讲述了如何用 Neo4j 知识图谱为大语言模型注入结构化语义，从而提升回答准确度、可解释性与安全性的全过程。 2. 内容深度与广度 • 从知识图谱建模、向量搜索到 RAG 框架，再到生产级部署与监控，覆盖了构建 GenAI 应用的完整技术栈。 • 作者不仅比较了 LangChain、Haystack、Spring AI、LangChain4j 等主流框架，还给出选型建议和实际代码示例，对开发者迁移和选型极具参考价值。 • 通过真实用例（聊天机器人、个性化推荐等）手把手演示项目从本地到云端的全流程，既有理论也有可运行的代码库。 3. 作者与社区贡献 作为 Haystack 维护者之一，Julian Risch 的参与让本书在技术落地层面更具权威性；他强调“Haystack pipelines are graphs”这一观点，帮助读者从图计算角度重新理解 RAG 组件编排。 4. 适合读者 • 想用 Neo4j 或其他图数据库为 LLM 增强检索结果的技术人员。 • 需要将 RAG 理论快速转化为可运行方案的开发者或架构师。 • 关注 AI 产品化、可扩展性与监控实践的团队。 5. 可能的局限 • 对云服务（如 Google Cloud Run）的依赖示例较多，若读者偏好 AWS 或 Azure，可能需要自行对照调整。 • 偏向 Python 生态，虽然提及 Java/Spring，但章节深度不及 Python/Haystack 部分。 总评 如果你想在最短时间内掌握“知识图谱 + 大语言模型”这一组合，并获得可直接迁移到生产环境的代码与架构思路，这本书几乎是同类中文、英文资料中覆盖面最完整、实践性最强的一本。对于想深耕 RAG、GenAI 领域的个人与团队，强烈推荐列入必读清单。