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Mastering LangChaina comprehensive g...

Agarwal, Nitin.

Mastering LangChaina comprehensive guide to building generative AI applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Mastering LangChainby Sanath Raj B Narayan, Nitin Agarwal.
其他題名:	a comprehensive guide to building generative AI applications /
作者:	Narayan, Sanath Raj B.
其他作者:	Agarwal, Nitin.
出版者:	Berkeley, CA :Apress :2025.
面頁冊數:	xiii, 243 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence.
電子資源:	https://doi.org/10.1007/979-8-8688-1718-2
ISBN:	9798868817182$q(electronic bk.)

Mastering LangChaina comprehensive guide to building generative AI applications /
Narayan, Sanath Raj B.

Mastering LangChaina comprehensive guide to building generative AI applications /[electronic resource] :by Sanath Raj B Narayan, Nitin Agarwal. - Berkeley, CA :Apress :2025. - xiii, 243 p. :ill., digital ;24 cm.

Chapter 1: Introduction to LangChain -- Chapter 2: Core Components of LangChain -- Chapter 3: Advanced Components and Integrations -- Chapter 4: Building Chatbots -- Chapter 5: Building Retrieval-Augmented Generation (RAG) Systems -- Chapter 6: LangServe, LangSmith, and LangGraph: Deploying, Optimizing, and Designing Language Model Workflows -- Chapter 7: LangChain and NLP -- Chapter 8: Building AI Agents with LangGraph -- Chapter 9: LangChain Framework Integration -- Chapter 10: Deploying LangChain Applications -- Chapter 11: Best Practices and Practical Aspects.

This book provides a comprehensive exploration of LangChain, empowering you to effectively harness large language models (LLMs) for Gen AI applications. It focuses on practical implementation and techniques, making it a valuable resource for learning LangChain. The book starts with foundational topics such as environment setup and building basic chains, then delves into key components such as prompt templates, tool integration, and memory management. You will also explore practical topics such as output parsing, embedding models, and developing chatbots and retrieval-augmented generation (RAG) systems. Additional chapters focus on integrating LangChain with other AI tools and deploying applications while emphasizing best practices for AI ethics and performance. By the time you finish this book, you'll have the know-how to confidently build Generative AI solutions using LangChain. Whether you're exploring practical applications or curious about the latest trends, this guide gives you the tools and insights to solve real-world AI problems. You'll be ready to design smart, data-driven applications-and rethink how you approach Generative AI. What You Will Learn Understand the core ideas, architecture, and essential features of the LangChain framework Create advanced LLM-driven workflows and applications that address real-world challenges Develop robust Retrieval-Augmented Generation (RAG) systems using LangChain, vector databases, and proven best practices for retrieving and generating high-quality responses.

ISBN: 9798868817182$q(electronic bk.)

Standard No.: 10.1007/979-8-8688-1718-2doiSubjects--Topical Terms:

194058
Artificial intelligence.

LC Class. No.: TK5105.5 / .N37 2025

Dewey Class. No.: 004.6

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520 $a This book provides a comprehensive exploration of LangChain, empowering you to effectively harness large language models (LLMs) for Gen AI applications. It focuses on practical implementation and techniques, making it a valuable resource for learning LangChain. The book starts with foundational topics such as environment setup and building basic chains, then delves into key components such as prompt templates, tool integration, and memory management. You will also explore practical topics such as output parsing, embedding models, and developing chatbots and retrieval-augmented generation (RAG) systems. Additional chapters focus on integrating LangChain with other AI tools and deploying applications while emphasizing best practices for AI ethics and performance. By the time you finish this book, you'll have the know-how to confidently build Generative AI solutions using LangChain. Whether you're exploring practical applications or curious about the latest trends, this guide gives you the tools and insights to solve real-world AI problems. You'll be ready to design smart, data-driven applications-and rethink how you approach Generative AI. What You Will Learn Understand the core ideas, architecture, and essential features of the LangChain framework Create advanced LLM-driven workflows and applications that address real-world challenges Develop robust Retrieval-Augmented Generation (RAG) systems using LangChain, vector databases, and proven best practices for retrieving and generating high-quality responses.
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