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.
將知識圖譜與大語言模型真正落地的實踐指南 1. 核心價值 本書聚焦於“Retrieval-Augmented Generation (RAG)”這一當下最具前景的生成式 AI 落地路徑，系統化地講述瞭如何用 Neo4j 知識圖譜爲大語言模型注入結構化語義，從而提升回答準確度、可解釋性與安全性的全過程。 2. 內容深度與廣度 • 從知識圖譜建模、向量搜索到 RAG 框架，再到生產級部署與監控，覆蓋了構建 GenAI 應用的完整技術棧。 • 作者不僅比較了 LangChain、Haystack、Spring AI、LangChain4j 等主流框架，還給出選型建議和實際代碼示例，對開發者遷移和選型極具參考價值。 • 通過真實用例（聊天機器人、個性化推薦等）手把手演示項目從本地到雲端的全流程，既有理論也有可運行的代碼庫。 3. 作者與社區貢獻 作爲 Haystack 維護者之一，Julian Risch 的參與讓本書在技術落地層面更具權威性；他強調“Haystack pipelines are graphs”這一觀點，幫助讀者從圖計算角度重新理解 RAG 組件編排。 4. 適合讀者 • 想用 Neo4j 或其他圖數據庫爲 LLM 增強檢索結果的技術人員。 • 需要將 RAG 理論快速轉化爲可運行方案的開發者或架構師。 • 關注 AI 產品化、可擴展性與監控實踐的團隊。 5. 可能的侷限 • 對雲服務（如 Google Cloud Run）的依賴示例較多，若讀者偏好 AWS 或 Azure，可能需要自行對照調整。 • 偏向 Python 生態，雖然提及 Java/Spring，但章節深度不及 Python/Haystack 部分。 總評 如果你想在最短時間內掌握“知識圖譜 + 大語言模型”這一組合，並獲得可直接遷移到生產環境的代碼與架構思路，這本書幾乎是同類中文、英文資料中覆蓋面最完整、實踐性最強的一本。對於想深耕 RAG、GenAI 領域的個人與團隊，強烈推薦列入必讀清單。