Learn how to leverage LangChain to work around LLMs’ inherent weaknesses
Delve into LLMs with LangChain and explore their fundamentals, ethical dimensions, and application challenges
Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality

Description

ChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Gemini. It demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis – illustrating the expansive utility of LLMs in real-world applications. Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.

Who is this book for?

The book is for developers, researchers, and anyone interested in learning more about LangChain. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs using LangChain. Basic knowledge of Python is a prerequisite, while prior exposure to machine learning will help you follow along more easily.

What you will learn

Create LLM apps with LangChain, like question-answering systems and chatbots
Understand transformer models and attention mechanisms
Automate data analysis and visualization using pandas and Python
Grasp prompt engineering to improve performance
Fine-tune LLMs and get to know the tools to unleash their power
Deploy LLMs as a service with LangChain and apply evaluation strategies
Privately interact with documents using open-source LLMs to prevent data leaks

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Frequently bought together

Machine Learning with PyTorch and Scikit-Learn

€41.99

Modern Generative AI with ChatGPT and OpenAI Models

€37.99

Total € 117.97

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Sarabjit Oct 27, 2024

Product is good but paper cutting is not so smootBook binding issue

Amazon Verified review

Sohrab Oct 17, 2024

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.

Jesper Wulff Jul 16, 2024

I feel like I've given this book a fair chance. The examples are highly relevant, but it's just too big of a downside that the code doesn't work. It's not just a matter of tweaking a couple of things. And this book is bascially all about the examples because it contains little to no background on the technical details. I recommend just going straight to the langchain website for tutorials instead.

Errol Kutan Jun 13, 2024

Bought this to ramp up on langchain. First two chapters were a decent amount of non-technical fluff, which is probably fine if this is your first book on modern trends in AI/LLMs.Subsequent chapters actually dive into technical examples of using langchain APIs and integrations with hugging face, open ai, etc. The problem is, very few of the examples work without significant amount of troubleshooting and rejiggering. I'm not talking about "you didnt follow the instructions and forgot to procure an API key". Im talking about "the model in the example just completely doesnt work anymore, even if you have a pro subscription to hugging face hub", or weird versioning issues among the various libraries. Moreover, the github repo doesnt actually have clear chapter specific code, so you cannot just clone the repo and rerun those examples verbatim to rule out any issues local to your setup, or get an updated version of the examples should the models in them have to actually change due to deprecation. The book is fairly new, so it's confusing on why so many things already don't work. IMO, you may be better just going through the langchain walkthroughs.I've gone through other AI books from other publishers and had some issues due to API changes, but at least in those cases, much of it was addressable by reverting to library versions specified or by using the accompanying jupyter notebooks. Also, those issues only popped up 10% of the time.

SURYADIPTA ROY Jun 05, 2024

This book could potentially have been of great value given the widespread usage of the LangChain platform. The book itself attempts to cover all the important LLM architectures. Unfortunately, there is little association between the book materials and the code, which makes the book to be of limited value. I have found it very frustrating whenever I have tried to implement the materials covered in the text. The code on GitHub have also been posted in a very haphazard manner and do not have any clear relationship with the text chapters. I sincerely hope that the author will modify the text in the next iteration so that the book is better organized.

Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs

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