LangChain in Action, MEAP, Infante R.

LangChain in Action, MEAP, Infante R.
     
   "LangChain in Action" is structured to cater both to beginners and seasoned professionals. It offers a comprehensive look into the foundational technologies and advanced techniques in the realm of LLMs. Whether you are new to programming or an experienced developer, you will find the content approachable yet enriching, especially with practical code examples to enhance your engagement.
The insights in this book are drawn from my real-world experiences and continuous learning in a field that evolves almost daily. I cover a range of topics from running open source LLMs locally to advanced RAG techniques and fine-tuning methods. My goal is to provide a resource that I wish had been available to me—an accessible, practical guide that empowers you to navigate and excel in this emerging domain.




Introducing LangChain.
Since late 2022, a number of open-source frameworks have emerged to support the development of applications powered by large language models (LLMs). These frameworks offer ready-made components for common tasks —such as data retrieval, prompt management, and response parsing—helping developers save time and build more reliable systems. Driven by community contributions, they evolve quickly to keep pace with advances in LLM technology, data sources, and vector store integrations. As they mature, they help establish and reinforce best practices across the industry.

Among the most widely adopted of these frameworks are LangChain, LlamaIndex, and Semantic Kernel. Though each originated from a different perspective, they have converged toward offering similar sets of core components. These components map closely to the typical architecture of LLM applications—a modular, interface-driven design that emphasizes flexibility and reusability.

CONTENTS.
Welcome.
1 Developing LLM applications with LangChain.
2 Executing prompts programmatically.
3 Summarizing text using LangChain.
4 Building a research summarization engine.
5 Introducing AI Agents with LangGraph.
6 RAG fundamentals with Chroma DB.
7 Q&A chatbots with LangChain and LangSmith.
8 Advanced indexing.
9 Question transformations.
10 Query generation, routing and retrieval post-processing.
11 Using open-source LLMs.
Appendix A. The Transformer architecture.
Appendix B. Setting up a Jupyter Notebook environment.
Appendix C. Choosing an LLM.
Appendix D. Installing SQLite on Windows.
Appendix E. Further inference engines.
Index.



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