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Generative AI with LangChain

You're reading from   Generative AI with LangChain Build production-ready LLM applications and advanced agents using Python, LangChain, and LangGraph

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Product type Paperback
Published in May 2025
Publisher Packt
ISBN-13 9781837022014
Length 476 pages
Edition 2nd Edition
Languages
Concepts
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Toc

Table of Contents (14) Chapters Close

Preface 1. The Rise of Generative AI: From Language Models to Agents 2. First Steps with LangChain FREE CHAPTER 3. Building Workflows with LangGraph 4. Building Intelligent RAG Systems 5. Building Intelligent Agents 6. Advanced Applications and Multi-Agent Systems 7. Software Development and Data Analysis Agents 8. Evaluation and Testing 9. Production-Ready LLM Deployment and Observability 10. The Future of Generative Models: Beyond Scaling 11. Other Books You May Enjoy 12. Index Appendix

Questions

  1. What is LangGraph, and how does LangGraph workflow differ from LangChain’s vanilla chains?
  2. What is a “state” in LangGraph, and what are its main functions?
  3. Explain the purpose of add_node and add_edge in LangGraph.
  4. What are “supersteps” in LangGraph, and how do they relate to parallel execution?
  5. How do conditional edges enhance LangGraph workflows compared to sequential chains?
  6. What is the purpose of the Literal type hint when defining conditional edges?
  7. What are reducers in LangGraph, and how do they allow modification of the state?
  8. Why is error handling crucial in LangChain workflows, and what are some strategies for achieving it?
  9. How can memory mechanisms be used to trim the history of a conversational bot?
  10. What is the use case of LangGraph checkpoints?
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