LangGraph guide for Knowledge-Driven LLMs
Language: English
Published by Amazon Digital Services LLC - Kdp, 2025
Series: Book 2 of 4 - Applied LLM Systems: Production Patterns for Agents, Context, and Knowledge Graphs
- Softcover
- New

Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
AbeBooks seller since June 11, 1999
Condition: New
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New Book. Shipped from UK. Established seller since 2000.
Seller Inventory # L2-9798265783325
- Title
- LangGraph guide for Knowledge-Driven LLMs
- Author
- Carter Zhang
- Publisher
- Amazon Digital Services LLC - Kdp
- Publication year
- 2025
- Condition
- New
- Binding
- PAP
- Language
- English
- ISBN 13
- 9798265783325
- Item weight
- 404 grams
- Series
- Book 2 of 4: Applied LLM Systems: Production Patterns for Agents, Context, and Knowledge Graphs
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.
"Synopsis" may belong to another edition of this title.
PBShop.store UK
Fairford, GLOS, United Kingdom
AbeBooks seller since June 11, 1999
Shipping rates from United Kingdom to U.S.A.
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