Sanath Raj B Narayan, Nitin Agarwal | Mastering LangChain. A Comprehensive Guide to Building Generative AI Applications (2025) [PDF, EPUB]
Автор: Sanath Raj B Narayan, Nitin Agarwal
Издательство: Apress
ISBN: 979-8868817175
Жанр: Artificial Intelligence, Python Programming, Artificial Intelligence & Semantics
Язык: Английский
Формат: PDF, EPUB
Качество: Изначально электронное (ebook)
Иллюстрации: Цветные и черно-белые
Описание: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.
Читать фрагмент 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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