{"product_id":"langgraph-guide-for-knowledge-driven-llms-carter-zhang-9798265783325","title":"LangGraph guide for Knowledge-Driven LLMs: Designing graph-first LLM applications with hybrid retrieval, entity linking, graph and vector pipelines","description":"LangGraph for Knowledge-Driven LLMs shows how to combine graph-structured knowledge with large language models to produce more accurate, explainable, and maintainable AI systems. The book introduces LangGraph concepts, data models, and connectors, and walks through full ingestion pipelines that convert raw documents into triples, entities, and canonical nodes. Learn entity resolution and linking techniques that reduce ambiguity, maintain provenance, and make knowledge updates straightforward.\u003cbr\u003eA major focus is on converting graph structure into vector representations and building hybrid retrieval flows that combine graph queries with vector similarity search. You'll learn how to craft graph-aware context assembly and prompting strategies so LLMs can reason with structured knowledge and return traceable answers. The book also covers graph embeddings, graph neural nets, explainability patterns, and operational best practices for indexing, monitoring, and schema evolution. Real-world case studies demonstrate customer-support assistants, domain expert systems, and product catalogs that use LangGraph for domain grounding and faster iteration.\u003cbr\u003eWhat's inside: \u003cul\u003e\n\u003cli\u003eLangGraph architecture explained with connector and transform examples.\u003c\/li\u003e\n\u003cli\u003ePipelines from documents to triples, to graph stores, to vector indexes.\u003c\/li\u003e\n\u003cli\u003eEntity linking, canonicalization, deduplication, and schema evolution patterns.\u003c\/li\u003e\n\u003cli\u003eGraph vector conversion: embedding strategies, batching, and incremental updates.\u003c\/li\u003e\n\u003cli\u003eHybrid retrieval recipes: combining SPARQL\/Cypher-like graph constraints with vector similarity.\u003c\/li\u003e\n\u003cli\u003ePrompting patterns that leverage graph provenance and traceability.\u003c\/li\u003e\n\u003cli\u003eAgents that consult LangGraph for planning, grounding, and action execution.\u003c\/li\u003e\n\u003cli\u003eMonitoring, explainability, and provenance tooling for regulated domains.\u003c\/li\u003e\n\u003cli\u003eIntegration examples with Neo4j, ArangoDB, and common vector DBs.\u003c\/li\u003e\n\u003cli\u003ePerformance tuning, consistency approaches, and operational checklists.\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for: \u003cul\u003e\n\u003cli\u003eData engineers, knowledge engineers, and ML engineers building knowledge-first LLM applications.\u003c\/li\u003e\n\u003cli\u003eTeams seeking explainability, auditability, and updatability in AI systems.\u003c\/li\u003e\n\u003cli\u003eProduct managers and architects planning hybrid retrieval or graph-backed assistants.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Carter Zhang\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798265783325\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 09\/16\/2025\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 228\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 0.89lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 10.00h x 7.00w x 0.48d","brand":"Carter Zhang","offers":[{"title":"Paperback","offer_id":48014264533247,"sku":"9798265783325","price":19.25,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0662\/2982\/9887\/files\/img_8c167762-6060-4f38-9160-041e716de267.jpg?v=1767747695","url":"https:\/\/www.whiterainbookhouse.com\/products\/langgraph-guide-for-knowledge-driven-llms-carter-zhang-9798265783325","provider":"WR Book House","version":"1.0","type":"link"}