Narayan S Mastering LangChain A Comprehensive Guide 2025
General:
Name: Narayan S Mastering LangChain A Comprehensive Guide 2025
Format: pdf
Size: 3.5 MB
Book:
Title: Mastering LangChain
Author: Sanath Raj B Narayan
Language: English
Year: 2025
Subjects: N/A
Publisher: Apress Okt 2025
ISBN: 9798868817175
Total pages: 251
Description:
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
Who This Book Is For
Data scientists and AI enthusiasts with basic Python skills who want to use LangChain for advanced development, and Python developers interested in building data-responsive applications with large language models (LLMs)
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