Generative AI with LangChain (Dr. Priyanka Singh Hariom Singh) (Z-Library)

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Generative AI with LangChain Build smart AI apps using LangChain and Python tools Dr. Priyanka Singh Hariom Singh www.bpbonline.com

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First Edition 2025 Copyright © BPB Publications, India eISBN: 978-93-65892-307 All Rights Reserved. No part of this publication may be reproduced, distributed or transmitted in any form or by any means or stored in a database or retrieval system, without the prior written permission of the publisher with the exception to the program listings which may be entered, stored and executed in a computer system, but they can not be reproduced by the means of publication, photocopy, recording, or by any electronic and mechanical means. LIMITS OF LIABILITY AND DISCLAIMER OF WARRANTY The information contained in this book is true and correct to the best of author’s and publisher’s knowledge. The author has made every effort to ensure the accuracy of these publications, but the publisher cannot be held responsible for any loss or damage arising from any information in this book. All trademarks referred to in the book are acknowledged as properties of their respective owners but BPB Publications cannot guarantee the accuracy of this information. www.bpbonline.com

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Dedicated to To the brilliant minds exploring LangChain and shaping the future of AI—your bold questions and boundary-pushing ideas inspire innovation. To the curious students, passionate professionals, and relentless creators—your drive for knowledge lights the path forward. To our families—your unwavering support fuels our journey. And to ourselves—for daring to start, for growing through each challenge, and for believing in the power of intelligent transformation.

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About the Authors Dr. Priyanka Singh is a dynamic leader in artificial intelligence, cloud computing, and technical education, with over a decade of experience bridging the gap between industry and academia. A Ph.D. in cloud computing and an engineering manager (AI) at Universal AI, she has led innovative AI-driven projects across transportation, logistics, healthcare, and manufacturing. Her work emphasizes ethical AI development, governance, and impact-driven solutions. Dr. Singh is also a published author and technical reviewer in AI and NLP and a passionate mentor shaping the next generation of technologists. She was recognized as one of the Top 100 Women in Tech (AI) and is the creator of the #AIforLife movement, promoting the use of AI for societal good. Currently, she is transitioning into K- 12 education in Arkansas, empowering students as an AP Computer Science educator. Hariom Singh, the co-author, brings over 15 years of strategic leadership experience in business transformation, technology integration, and AI adoption. With an MBA and a certified PMP/RMP, he excels in bridging business needs with technical execution across various domains. Known for his strategic mindset and data-driven leadership, Hariom champions the responsible use of AI to drive enterprise innovation. He is also a mentor, blogger, and advocate for clear communication and the ethical adoption of AI. His writing and thought leadership can be explored on Medium and LinkedIn. Together, they bring a powerful blend of technical depth, business insight, and educational impact—committed to shaping a future where AI empowers, educates, and elevates society.

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About the Reviewers Sai Chaitanya is a data scientist with 7 years of experience (8.5 years overall) in building machine learning models and solving real-world problems. He is currently pursuing a masters in data science at Scaler Academy, focusing on data structures and algorithms (DSA), machine learning, deep learning and generative AI. Sai is an aspiring technical author with plans to write a machine learning book that simplifies complex algorithms using a unique visual approach. His aim is to make learning intuitive and long-lasting for readers. This exciting project is set to begin in 2025, reflecting his passion for demystifying data science concepts. In addition to his professional pursuits, Sai has recently embarked on a journey to enhance his overall well-being, balancing physical, mental, and spiritual health while expanding his knowledge in personal finance. He is also exploring the stock market and is dedicated to personal growth. Sai envisions a future where his journey inspires others to pursue excellence in the data science field and personal growth. Nabil Tadili is an accomplished lead developer at SPVIE Assurances, with extensive experience in software architecture and full-stack development. Over the years, he has contributed to a variety of impactful projects, including the integration of AI solutions to streamline business operations. Notably, he led initiatives to deploy machine learning models for defect detection and utilized large language models (LLMs) to automate document creation processes, enhancing operational efficiency. Currently pursuing a master of science in computer science with a specialization in machine learning at Georgia Tech through the OMSCS program, Nabil is deeply invested in advancing his expertise in artificial intelligence. His work reflects a passion for harnessing cutting-edge

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technology to solve complex challenges and deliver innovative solutions. Beyond his technical endeavors, Nabil remains committed to fostering collaboration and staying at the forefront of emerging trends in AI, software engineering, and digital transformation. Vineet Jaiswal, vice president of generative AI at India’s largest bank, is a distinguished leader with over 17 years of experience in AI, machine learning, and software development. Recognized for his deep expertise across all major cloud platforms, he has made significant contributions to generative AI, deep learning, computer vision, MLOps, and backend technologies. His work spans collaborations with multiple Fortune 500 clients, driving innovation and scalable solutions. In addition to his professional achievements, he holds a masters degree and has completed various certifications, further solidifying his expertise in the field.

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Acknowledgements This book would not have been possible without the unwavering support of my family. I am deeply grateful to my husband and co-author, Hariom Singh, whose expertise in cloud architecture and AI brought invaluable depth to this work. His partnership and vision were instrumental throughout this journey. As an author, I, Dr. Priyanka Singh, would like to thank my sons, Gopi and Aaditya, and my brother, Sushil, for their constant encouragement, patience, and love. We extend our heartfelt thanks to the students, professionals, and innovators whose passion for learning and technology inspired the creation of this book. To our mentors, colleagues, and collaborators—your insights challenged and enriched our thinking. To the publishers, editors, and everyone who helped bring this book to life, your dedication has turned our vision into reality. This book is for the pioneers of today and the leaders of tomorrow. May it inspire, guide, and empower you as you shape the future of AI.

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Preface This book is a practical guide to building intelligent language applications using artificial intelligence and the LangChain framework. From foundational AI concepts and Python essentials to deploying real-world solutions on AWS, Azure, Snowflake, and Athena, each chapter is crafted to equip readers with both the knowledge and tools to create scalable, ethical, and impactful AI systems. You will explore neural networks, LangChain workflows, DevOps/MLOps integration, and enterprise use cases, all through hands-on examples and strategic insights. Whether you are a beginner or a professional, this book is your roadmap to mastering AI-powered applications in today’s fast- evolving tech landscape. Chapter 1: Introduction to Artificial Intelligence and LangChain - This chapter introduces the core concepts of artificial intelligence (AI) and the LangChain framework. It explains AI fundamentals, its real-world applications, and its impact across industries. The chapter also presents LangChain as a powerful tool for building AI-driven language applications, emphasizing its flexibility, ease of use, and community support. Ethical considerations and responsible AI usage are also discussed to set the foundation for future learning. Chapter 2: Getting Started with Python - This chapter lays the groundwork for using Python in AI development. It covers Python basics such as variables, data types, control structures, functions, and modules. The chapter also explains why Python is the preferred language in the AI community and walks through hands-on coding examples to help readers build confidence. It prepares learners to write clean, readable code for AI tasks and introduces Python's role in building intelligent systems. Chapter 3: Understanding LangChain Basics - This chapter provides a comprehensive overview of the LangChain framework. It covers its key

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components—LangChain libraries, templates, LangServe, and LangSmith —and explains how developers can use these tools to create, debug, and deploy language applications. The chapter highlights LangChain’s workflow, including building chains, integrating models, and serving applications via APIs. Hands-on examples and installation guidance make it easy for readers to get started. Chapter 4: Neural Network with LangChain - This chapter explores the integration of neural networks with LangChain to build advanced language applications. It explains how neural networks work—covering layers, activation functions, and training processes—and demonstrates their role in NLP. The chapter introduces modular neural networks and showcases real- world implementations like sentiment analysis using LSTM. Readers also learn how LangChain supports scalable, customizable, and ethical AI development. Chapter 5: LangChain and AWS Integration - This chapter explores how to deploy LangChain applications on Amazon Web Services (AWS). It covers the cloud setup process, explains integration with services like EC2, Lambda, and S3, and guides readers through infrastructure management, deployment strategies, and cost-effective scaling of AI applications using AWS. Chapter 6: LangChain and Azure Integration - This chapter covers the step-by-step integration of LangChain applications into Microsoft Azure’s cloud ecosystem. It explains how to deploy, scale, monitor, and secure AI services in Azure. Real-world examples show how to use LangChain alongside Azure services such as Azure Functions, Blob Storage, and Application Insights. Chapter 7: Real-world Data Science with Snowflake and Athena - This chapter demonstrates how LangChain can be combined with modern data warehousing tools like Snowflake and Amazon Athena. It explains how to use LangChain to perform scalable data retrieval, intelligent querying, and downstream AI processing for enterprise-scale datasets. Chapter 8: AI in DevOps and MLOps - This chapter explains the role of LangChain in the CI/CD and MLOps landscape. It covers topics like automated testing, continuous integration of AI models, versioning, model

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monitoring, deployment automation, and how to build resilient AI pipelines for production environments. Chapter 9: Future Trends in AI and LangChain - This chapter explores the emerging trends and innovations in AI and LangChain—from advancements in machine learning and generative models to enterprise use cases in healthcare, agriculture, finance, and education. It emphasizes the importance of ethical and responsible AI, offering insights into what lies ahead in the evolving AI landscape.

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Code Bundle and Coloured Images Please follow the link to download the Code Bundle and the Coloured Images of the book: https://rebrand.ly/e5bad2 The code bundle for the book is also hosted on GitHub at https://github.com/bpbpublications/Generative-AI-with-LangChain. In case there’s an update to the code, it will be updated on the existing GitHub repository. We have code bundles from our rich catalogue of books and videos available at https://github.com/bpbpublications. Check them out! Errata We take immense pride in our work at BPB Publications and follow best practices to ensure the accuracy of our content to provide with an indulging reading experience to our subscribers. Our readers are our mirrors, and we use their inputs to reflect and improve upon human errors, if any, that may have occurred during the publishing processes involved. To let us maintain the quality and help us reach out to any readers who might be having difficulties due to any unforeseen errors, please write to us at : errata@bpbonline.com Your support, suggestions and feedbacks are highly appreciated by the BPB Publications’ Family. Did you know that BPB offers eBook versions of every book published, with PDF and ePub files available? You can upgrade to the eBook version at www.bpbonline.com and as a print book customer, you are entitled to a discount on the eBook copy. Get in touch with us at : business@bpbonline.com for more details.

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At www.bpbonline.com, you can also read a collection of free technical articles, sign up for a range of free newsletters, and receive exclusive discounts and offers on BPB books and eBooks. Piracy If you come across any illegal copies of our works in any form on the internet, we would be grateful if you would provide us with the location address or website name. Please contact us at business@bpbonline.com with a link to the material. If you are interested in becoming an author If there is a topic that you have expertise in, and you are interested in either writing or contributing to a book, please visit www.bpbonline.com. We have worked with thousands of developers and tech professionals, just like you, to help them share their insights with the global tech community. You can make a general application, apply for a specific hot topic that we are recruiting an author for, or submit your own idea. Reviews Please leave a review. Once you have read and used this book, why not leave a review on the site that you purchased it from? Potential readers can then see and use your unbiased opinion to make purchase decisions. We at BPB can understand what you think about our products, and our authors can see your feedback on their book. Thank you! For more information about BPB, please visit www.bpbonline.com. Join our book’s Discord space Join the book’s Discord Workspace for Latest updates, Offers, Tech happenings around the world, New Release and Sessions with the Authors: https://discord.bpbonline.com

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Table of Contents 1. Introduction to Artificial Intelligence and LangChain Introduction Structure Objectives Introduction to artificial intelligence Historical context Overview of LangChain Distinctive attributes of LangChain The allure of LangChain: A user's perspective Applications of AI in the modern world Healthcare Implementing a healthcare AI application with LangChain Finance Implementing a finance-related AI application with LangChain Retail Implementing a retail-based AI application Manufacturing Implementing an AI application Transportation Implementing a transportation-related AI application Impact on jobs and industries Enhancement and efficiency Job creation Ethical considerations in AI

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Transparency Fairness and bias Privacy and security Accountability Ethical considerations are crucial Establishing your environment Development environment Integrated development environment Getting started with Python Conclusion Points to remember Multiple choice questions Answers Key terms 2. Getting Started with Python Introduction Structure Objectives Introduction to Python Discovering Python's appeal in AI Exploring simplicity and readability Python basics Essential concepts and basic operations Variables Casting Getting the type Single or double quotes Case-sensitive Unpack a collection Data types

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Built-in data types in Python Control structures in Python Python if-else Logical conditions If statement Indentation Elif Else Logical operators Exploring loops for repetitive tasks While loop For loops Functions and modules in Python Understanding function arguments in Python Uncovering the power of Python modules Python for AI Understanding Python's role in AI development Exploring Python libraries and frameworks in AI NumPy Pandas TensorFlow and PyTorch Conclusion Points to remember Multiple choice questions Answers Key terms 3. Understanding LangChain Basics Introduction Structure Objectives

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Introduction to LangChain Key components of LangChain LangChain libraries Advantages Limitations LangChain workflow Getting started LangChain in action Installation Hands-on examples Demo Iterative improvement Demo Real-world examples Healthcare: Patient data analysis Demo with a dummy dataset Conclusion Points to remember Multiple choice questions Answers Key terms 4. Neural Network with LangChain Introduction Structure Objectives Introduction to neural networks and LangChain Neural networks Introduction to LangChain Bridging AI and language technology

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The purpose of LangChain with neural networks Capabilities of LangChain with neural networks Integrating neural networks with LangChain Neural networks are incorporated into LangChain Benefits of integrating neural networks with LangChain Mechanics of neural networks in language processing Getting data ready Teaching the model Learning and adaptation in LangChain LangChain learns Continuous improvement Model refinement in action Logic behind continuous learning Applications and use cases of LangChain and neural networks Challenges and ethical considerations Future directions and innovations Advancements in neural network technologies Emerging trends and new applications Conclusion Points to remember Multiple choice questions Answers Key terms 5. LangChain and AWS Integration Introduction Structure Objectives Getting started with LangChain and AWS Prerequisites for integration

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Beginner steps for AWS account setup AWS CLI and SDKs Single-file Python script for expert Advantages of cloud computing for AI Setting up your AWS environment Deploying LangChain applications on AWS Step-by-step guide for serverless deployment Data storage and management Step-by-step guide Using AWS databases Managing and scaling the LangChain application Advanced integration techniques LangChain integration Expected workflow Key benefits Benefits Case studies and real-world examples Healthcare: Patient data analysis and interaction Expected output Finance: Automated customer service Sample output Insights from real-world applications Challenges and solutions Troubleshooting tips Future directions Conclusion Points to remember Multiple choice questions Answers Key terms