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Mastering LangChain = a comprehensiv...

Narayan, Sanath Raj B.

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Mastering LangChain = a comprehensive guide to building generative AI applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mastering LangChain/ by Sanath Raj B Narayan, Nitin Agarwal.
Reminder of title:	a comprehensive guide to building generative AI applications /
Author:	Narayan, Sanath Raj B.
other author:	Agarwal, Nitin.
Published:	Berkeley, CA :Apress : : 2025.,
Description:	xiii, 243 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence. -
Online resource:	https://doi.org/10.1007/979-8-8688-1718-2
ISBN:	9798868817182

Mastering LangChain = a 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

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

516317
Artificial intelligence.

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

Dewey Class. No.: 004.6

Mastering LangChain = a comprehensive guide to building generative AI applications /
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245 1 0 $a Mastering LangChain $h [electronic resource] : $b a comprehensive guide to building generative AI applications / $c by Sanath Raj B Narayan, Nitin Agarwal.
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300 $a xiii, 243 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
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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