LangGraph guide for Knowledge-Driven LLMs: Designing graph-first LLM applications with hybrid retrieval, entity linking, graph and vector pipelines ... Context, and Knowledge Graphs, Band 2) - Softcover

Inhaltsangabe

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.
A 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.
What’s inside:

• LangGraph architecture explained with connector and transform examples.
• Pipelines from documents to triples, to graph stores, to vector indexes.
• Entity linking, canonicalization, deduplication, and schema evolution patterns.
• Graph vector conversion: embedding strategies, batching, and incremental updates.
• Hybrid retrieval recipes: combining SPARQL/Cypher-like graph constraints with vector similarity.
• Prompting patterns that leverage graph provenance and traceability.
• Agents that consult LangGraph for planning, grounding, and action execution.
• Monitoring, explainability, and provenance tooling for regulated domains.
• Integration examples with Neo4j, ArangoDB, and common vector DBs.
• Performance tuning, consistency approaches, and operational checklists.

• Data engineers, knowledge engineers, and ML engineers building knowledge-first LLM applications.
• Teams seeking explainability, auditability, and updatability in AI systems.
• Product managers and architects planning hybrid retrieval or graph-backed assistants.

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