000			01968nam a22002297a 4500
005			20260727150424.0
008			260727s2025 |||||||| |||| 00| 0 eng d
020			_a9781836206231
040			_cPK-LaUMT
082			_a005.74 _bANT-B
100	1		_aAnthapu, Ravindranatha _914262
245	1	0	_aBuilding Neo4j-powered applications with LLMs : _bcreate LLM-driven search and recommendations : applications with Haystack, LangChain4j, and Sprin AI / _cRavindranatha Anthapu
260			_aBirmingham : _bPackt Publishing Ltd., _c2025
300			_axxiv, 283 p.
490			_aExpert insight
500			_aIndex present
520			_aA comprehensive guide to building cutting-edge generative AI applications using Neo4j's knowledge graphs and vector search capabilities Key Features 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 Book 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.
546			_aEng
650			_aNeo4J (computer file) _913417
650			_aDatabase design _94777
942			_cBK
999			_c141675 _d141675