LangGraph guide for Knowledge-Driven LLMs
Book 1
Book 2
Book 3
Book 1
Book 2
Book 3
Book 1
Book 2
Book 3
Book 1
Book 2
Book 3
Home > Computing and Information Technology > Computer science > Artificial intelligence > Natural language and machine translation > LangGraph guide for Knowledge-Driven LLMs: Designing graph-first LLM applications with hybrid retrieval, entity linking, graph and vector pipelines(2 Applied LLM Systems: Production Patterns for Agents, Context, and Knowledge Graphs)
LangGraph guide for Knowledge-Driven LLMs: Designing graph-first LLM applications with hybrid retrieval, entity linking, graph and vector pipelines(2 Applied LLM Systems: Production Patterns for Agents, Context, and Knowledge Graphs)

LangGraph guide for Knowledge-Driven LLMs: Designing graph-first LLM applications with hybrid retrieval, entity linking, graph and vector pipelines(2 Applied LLM Systems: Production Patterns for Agents, Context, and Knowledge Graphs)


     0     
5
4
3
2
1



International Edition


X
About the Book

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. Who this book is for: 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.


Best Sellers


Product Details
  • ISBN-13: 9798265783325
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Height: 254 mm
  • No of Pages: 228
  • Returnable: N
  • Spine Width: 12 mm
  • Weight: 453 gr
  • ISBN-10: 8265783329
  • Publisher Date: 16 Sep 2025
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Series Title: 2 Applied LLM Systems: Production Patterns for Agents, Context, and Knowledge Graphs
  • Sub Title: Designing graph-first LLM applications with hybrid retrieval, entity linking, graph and vector pipelines
  • Width: 178 mm


Similar Products

Add Photo
Add Photo

Customer Reviews

REVIEWS      0     
Click Here To Be The First to Review this Product
LangGraph guide for Knowledge-Driven LLMs: Designing graph-first LLM applications with hybrid retrieval, entity linking, graph and vector pipelines(2 Applied LLM Systems: Production Patterns for Agents, Context, and Knowledge Graphs)
Independently Published -
LangGraph guide for Knowledge-Driven LLMs: Designing graph-first LLM applications with hybrid retrieval, entity linking, graph and vector pipelines(2 Applied LLM Systems: Production Patterns for Agents, Context, and Knowledge Graphs)
Writing guidlines
We want to publish your review, so please:
  • keep your review on the product. Review's that defame author's character will be rejected.
  • Keep your review focused on the product.
  • Avoid writing about customer service. contact us instead if you have issue requiring immediate attention.
  • Refrain from mentioning competitors or the specific price you paid for the product.
  • Do not include any personally identifiable information, such as full names.

LangGraph guide for Knowledge-Driven LLMs: Designing graph-first LLM applications with hybrid retrieval, entity linking, graph and vector pipelines(2 Applied LLM Systems: Production Patterns for Agents, Context, and Knowledge Graphs)

Required fields are marked with *

Review Title*
Review
    Add Photo Add up to 6 photos
    Would you recommend this product to a friend?
    Tag this Book Read more
    Does your review contain spoilers?
    What type of reader best describes you?
    I agree to the terms & conditions
    You may receive emails regarding this submission. Any emails will include the ability to opt-out of future communications.

    CUSTOMER RATINGS AND REVIEWS AND QUESTIONS AND ANSWERS TERMS OF USE

    These Terms of Use govern your conduct associated with the Customer Ratings and Reviews and/or Questions and Answers service offered by Bookswagon (the "CRR Service").


    By submitting any content to Bookswagon, you guarantee that:
    • You are the sole author and owner of the intellectual property rights in the content;
    • All "moral rights" that you may have in such content have been voluntarily waived by you;
    • All content that you post is accurate;
    • You are at least 13 years old;
    • Use of the content you supply does not violate these Terms of Use and will not cause injury to any person or entity.
    You further agree that you may not submit any content:
    • That is known by you to be false, inaccurate or misleading;
    • That infringes any third party's copyright, patent, trademark, trade secret or other proprietary rights or rights of publicity or privacy;
    • That violates any law, statute, ordinance or regulation (including, but not limited to, those governing, consumer protection, unfair competition, anti-discrimination or false advertising);
    • That is, or may reasonably be considered to be, defamatory, libelous, hateful, racially or religiously biased or offensive, unlawfully threatening or unlawfully harassing to any individual, partnership or corporation;
    • For which you were compensated or granted any consideration by any unapproved third party;
    • That includes any information that references other websites, addresses, email addresses, contact information or phone numbers;
    • That contains any computer viruses, worms or other potentially damaging computer programs or files.
    You agree to indemnify and hold Bookswagon (and its officers, directors, agents, subsidiaries, joint ventures, employees and third-party service providers, including but not limited to Bazaarvoice, Inc.), harmless from all claims, demands, and damages (actual and consequential) of every kind and nature, known and unknown including reasonable attorneys' fees, arising out of a breach of your representations and warranties set forth above, or your violation of any law or the rights of a third party.


    For any content that you submit, you grant Bookswagon a perpetual, irrevocable, royalty-free, transferable right and license to use, copy, modify, delete in its entirety, adapt, publish, translate, create derivative works from and/or sell, transfer, and/or distribute such content and/or incorporate such content into any form, medium or technology throughout the world without compensation to you. Additionally,  Bookswagon may transfer or share any personal information that you submit with its third-party service providers, including but not limited to Bazaarvoice, Inc. in accordance with  Privacy Policy


    All content that you submit may be used at Bookswagon's sole discretion. Bookswagon reserves the right to change, condense, withhold publication, remove or delete any content on Bookswagon's website that Bookswagon deems, in its sole discretion, to violate the content guidelines or any other provision of these Terms of Use.  Bookswagon does not guarantee that you will have any recourse through Bookswagon to edit or delete any content you have submitted. Ratings and written comments are generally posted within two to four business days. However, Bookswagon reserves the right to remove or to refuse to post any submission to the extent authorized by law. You acknowledge that you, not Bookswagon, are responsible for the contents of your submission. None of the content that you submit shall be subject to any obligation of confidence on the part of Bookswagon, its agents, subsidiaries, affiliates, partners or third party service providers (including but not limited to Bazaarvoice, Inc.)and their respective directors, officers and employees.

    Accept

    Fresh on the Shelf


    Inspired by your browsing history


    Your review has been submitted!

    You've already reviewed this product!