Mastering LangChain

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 applicationsand 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)

  Covers LangChain, LangServe, LangSmith, & key frameworks, with best practices for testing, monitoring, & deployment Includes hands-on code examples from LangChain basics to advanced components Provides practical case studies on chatbots, RAG pipelines, and AI agents

  Autorentext

Sanath Raj B Narayan is a Senior Data Scientist with over a decade of experience in building AI and machine learning solutions, as well as scalable systems using AWS and Azure. He has previously held roles at Ericsson, Mindtree, KPMG India, and Cognizant, where he led data-driven projects across the retail, telecom, and consulting sectors. Sanathraj's expertise spans predictive modeling, recommender systems, and the deployment of end-to-end machine learning pipelines. He is also a regular speaker at conferences, where he presents on AI and related topics.

Nitin Agarwal is a Principal AI Scientist with over 14 years of experience in Artificial Intelligence and Data Science. Formerly a Senior Data Scientist at Microsoft, he specializes in Machine Learning, Deep Learning, Natural Language Processing, and Statistical Modeling. Nitin brings extensive expertise in crafting innovative AI Copilots and delivering cutting-edge Data Science solutions across diverse industries, including Healthcare, Technology, and Logistics. He holds a master's degree in Data Science and Engineering from Birla Institute of Technology and Sciences (BITS), Pilani and CORe from Harvard Business School (HBX). Passionate about Generative AI and Large Language Models (LLMs), he is also a published researcher and a dedicated mentor. Nitin frequently shares his expertise as a speaker at AI and technology conferences, where he engages with the community on the latest advancements in AI and their real-world applications.

Inhalt

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.