Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs Paperback – 22 Dec. 2023

Today, Generative AI and Large Language Models (LLMs) are reshaping the world. LangChain is a framework for developing applications powered by language models. This book has, therefore, arrived at exactly the right time, is insightful, and delves into the critical role of LangChain in builing LLM-powered applications.The book comprises of ten distinct chapters. The author starts by introducing generative models, explaining transformers, the theory behind them, and the evolution of AI. The author then moves into more complex, LangChain-orientatated, discussions exploring a range of topics including setting up LangChain, building chatbots, automation in data science, and the complexities of deploying real-world generative AI applications. There is a wealth of valuable content contained within, much of which comprises crucial information, particularly considering contemporary issues and challenges.The author is adept at articulating intricate ideas in a clear manner. For example, the author offers a beginner-level explanation of getting started with LangChain, including the code for doing so. This approach of providing the code and describing it allows readers to gain hands-on experience and a deeper understanding of the concepts being discussed. If there is a minor gripe, it is that much of the code examples rely on OpenAI.In summary, Generative AI with LangChain is an informative read. The author has managed provide a practical guide for one of the key tools of today. Whether you are a developer, or someone who is just interested in understanding LangChain, this book is a valuable resource.

All global industries, irrespective of any domain, are now looking at significant automation aspects to reduce the substantial manual intervention and cost-effective solutions using the buzzword “Gen AI” Solutions. Yes! This Gen AI addresses multiple use cases and problem statements using cutting-edge technologies.The author mainly focused on building large language model (LLM) apps with Python, ChatGPT, and other LLMs.The author gets started with the definition of Generative AI and distinguishes more clearly between the terms AIML family. The author clearly outlined types of generative models and data handling in different data modalities across various domains. Why now Generative AI? From this point of view, the author comes up with the cost of computer storage since the 1950s in dollars (unadjusted) per terabyte comparison, which is a meaningful and critical point to understand. The author's notes on PaLM 2, LLaMa 2, and Claude 2 are really informative for the readers.The author’s introduction is about LangChain and how we can overcome LLM limitations and build innovative language-based applications, which is a significant theme in this book and set the goal to illustrate how LangChain enables the building of dynamic, data-aware applications by accessing LLMs via API calls. The author calls on the limitations of LLMs, how they can mitigate them, and how LLM applications are essential for several important reasons. Readers must read and digest them before they go into detail.When discussing LangChain, the author discusses how the LangChain ecosystem works, the critical components of LangChain – Chains, agents, memory, and tools, followed by how LangChain works. Not sure why the author compared the other leading frameworks.As we know, hallucinations are a big challenge and lead to unfaithful or nonsensical compared to the input and expected output; for this, the author came up with the Building Capable Assistants chapters with all possible solutions to improve the capabilities of the model with designing the Prompt templates, Chain of density, Map-Reduce pipelines for multiple documents and Exploring reasoning strategies.Building a Chatbot-like ChatGPT Chapter is a breakthrough for learners to understand the Retrieval-Augmented Language Models (RALMs), Embeddings, Vector databases, storage, indexing, Memory - Conversation buffers and storing knowledge graphs are key concepts while building robust LLMs-based AI applications.LLMs for Data Science – Here, the author discusses the impact of generative models on data science, how they can process automated data science, and how agents answer data science questions. Data exploration with LLMs is a significant milestone, even if it can be elaborated more on in real-time scenarios.Customising LLMs and Their Output – This chapter must be needed for Gen AI developers. The author covers conditioning techniques that enable LLMs to comprehend and execute complex instructions, deliver content that closely matches our expectations, and steer generative AI outputs with steps such as RLHF, LoRA, inference-time conditioning, and prompt engineering.The author gives a clear route for readers on how to move generative AI in production. Helped with how the LangSmith framework is used for debugging, testing, evaluating, and monitoring LLM applications developed and maintained by LangChain AI and completed the chapter on how the future of generative modelsOverall … I can give 4.5/5.0 for this. Indeed, an extraordinary effort from the author is really much appreciated.-Shanthababu PandianArtificial Intelligence and Analytics | Cloud Data and ML Architect | Scrum Master | National and International Speaker | Blogger |Author

"Generative AI with LangChain" offers a timely exploration of the evolving landscape of language models, particularly in the context of LangChain's transformative potential. Auffarth adeptly navigates the complexities of LLM-powered applications, providing a comprehensive guide for both beginners and seasoned developers alike.The book demystifies key LangChain developments by abstracting LLM complexities while empowering readers with advanced customization options. From fundamental concepts to intricate techniques like agents and chains, Auffarth equips readers with the tools necessary to enhance applications and navigate production deployment effectively.What sets this book apart is its multifaceted approach, bridging theory with hands-on examples across diverse domains like information extraction and chatbots. By combining conceptual foundations with real-world implementations, Auffarth ensures readers gain not only a deep understanding of LangChain but also the skills to tailor it to their specific applications."Generative AI with LangChain" stands out among existing resources by offering a comprehensive, well-rounded exploration of LangChain's capabilities. Auffarth's expertise shines through in his intuitive explanations and applied case studies, making this book an invaluable resource for anyone looking to harness the power of language models in their projects.

This book covers all the model and terminology used in LLMs and options available. Looking forward to carry forward the knowledge and go in depth from here.

A very promising title. However not that much systematics, instead lots of Python source code.

I have not used LangChain before, and I am looking at this book to learn how to create an LLM app. I am really looking forward to trying it out for all three types of apps covered in the book - assistants/chatbot, code generation, and data science. The book is clear and straight to the point, so I expect to be able to try these out fairly quickly. I have gotten through the "setting up the dependencies" section. I cloned the book's github repo, and I tried three methods for variety's sake to create a python environment: pip, conda, and Docker, all on Windows, and I believe I have them all set up. I hit some bumps, but I was able to follow the onscreen error messages and get past them. For pip, I needed to install MSFT Build Tools to get C++. For the conda case, I had to modify the yaml file for two of the packages - ncurses and readline, which have different names for Windows. In Chapter 2 there is a comparison of LangChain with other frameworks, from which you get a feel that choosing LangChain at this moment is the best choice. I am happy to have found this book, and I can't wait to proceed w/the next steps. It's a lot of fun to be able to interact w/LLMs.

"Generative AI with Langchain" by Dr. Ben Auffarth is a convergence of artificial intelligence and Generative AI. Its a comprehensive guide for both beginners and experts in the data science field. Dr Ben Auffarth had meticulously crafted this book which serves as a valuable resource for those delving into the realms of Generative AI.One of the most commendable aspects of this book is its content structure and its readability. Despite the complexity of the subject, the author explained the intricate concepts in a clear and concise manner, making it suitable for readers with varying levels of technical expertise. Irrespective of whether you are an expert AI researcher or a beginner to the field, you'll find valuable insights to deepen your understanding of Generative AI and Langchain. This book also talks about the recipe for building a chatbot like ChatGPT for enterprise, leveraging the capabilities of external knowledge sources/domain specific data via Retrieval Augmented Generation(RAG). This also emphasise on customizing the LLMs via Supervised Finetuning(SFT), Prompt Engineering(PE).This book is a starter kit for those who intend to build LLM based applications by leveraging the Langchain as an orchestrator for their application. This book also outlines the limitations of the current LLM models and ways to mitigate them for our specific use cases. By showcasing how Langchain can be used to generate different modalities like text, images, videos and speech, the book inspires readers to push the boundaries of what's possible with AI-driven creativity. This book also uncovers the need for going beyond the stochastic parrots of LLM models by harnessing the Langchain framework.In summary, Generative AI with Langchain" is a must-read for anyone interested in exploring the fascinating world of Generative AI. Ben Auffarth alongside their lucid writing style and focus on fostering creativity, renders this book an invaluable asset for researchers, practitioners, and enthusiasts alike. Whether you seek to enhance your comprehension of AI or ignite your creative spark, this book is sure to make a lasting impact. I highly recommended this book.