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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

Review questions

  1. What are the three main limitations of raw LLMs that LangChain addresses?
    • Memory limitations
    • Tool integration
    • Context constraints
    • Processing speed
    • Cost optimization
  2. Which of the following best describes the purpose of LCEL (LangChain Expression Language)?
    • A programming language for LLMs
    • A unified interface for composing LangChain components
    • A template system for prompts
    • A testing framework for LLMs
  3. Name three types of memory systems available in LangChain
  4. Compare and contrast LLMs and Chat Models in LangChain. How do their interfaces and use cases differ?
  5. Explain how chains in LangChain differ from simple sequential API calls to an LLM. Provide two specific advantages of using chains.
  6. What role do Runnables play in LCEL? How do they contribute to building modular LLM applications?
  7. When running models locally, which factors affect model performance? (Select all that apply)
    • Available RAM
    • CPU/GPU capabilities
    • Internet connection speed
    • Model quantization level
    • Operating...
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