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LangGraph guide for Knowledge-Driven LLMs: Designing graph-first LLM applications with hybrid retrieval, entity linking, graph and vector pipelines
Paperback
Series: Applied LLM Systems: Production Patterns for Agents, Context, and Knowledge Graphs, Book 2
Publisher: Independently Published
Published: Sep 16 2025
Pages: 228
Weight: 0.89
Height: 0.48 Width: 7.00 Depth: 10.00
Language: English
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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