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Key benefits

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

What do you get with Print?

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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

€37.99

Total € 117.97

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Josep Oriol Oct 13, 2023

This book is as up-to-date as it can be, with lots of helpful code examples, and covering all aspects of LLM the development pipeline. It's my work companion, way more useful than official LangChain documentation. A must for everyone involved in LLMOps

Subscriber review

Kam F Siu Jan 30, 2024

Feefo Verified review

Andrew McVeigh May 01, 2024

i'm only 100 pages into this book, but boy is it well phrased and extremely clear. i write apps around LLMs, including RAG architectures. perhaps it's just the current state of my learning, but i've found this book to be extremely helpful and very logically organized. I'll revisit this review once i'm through the entire book, but so far 10/10. it's easily the best and most self-contained book i have on the subject.

Amazon Verified review

F. P. Dec 22, 2023

During my learning journey into large language model (LLM) development, I encountered several challenges:- The difficulty of providing precise instructions within specific contexts, which I found to be the most challenging and crucial aspect.- Switching between different LLM models with minimal programming effort.- Selectively saving chat history in memory.- Handling data efficiently, including managing input data of various modalities and making output data accessible.In overcoming these obstacles, I came across LangChain, a robust toolkit designed for LLM application development. The book "Generative AI with LangChain" by Ben Auffarth provides a comprehensive overview, covering the basics of LLM, LangChain, and its key components (chains, agents, memory, tools). The book also explores sample applications such as chatbots, customization of LLM models (conditioning, fine-tuning), and the deployment of LLM apps into production. Unlike theoretical research materials, this book serves as a practical, one-stop resource for understanding the current landscape of LLM applications.Some of the interesting points:- LangChain helps standardize prompts by providing prompt templates (LangChain Expression Language).- LangChain provides extensive integrations to other model APIs including Fake LLM, OpenAI, Hugging Face, GCP, Jina AI, Replicate, etc.- LangChain has "memory" which allows the model to be context-aware.- LangChain supports advanced data facilities such as map-reduce approach and output parser.This book has significantly saved me time, providing consolidated information without the need for extensive online searches or inquiries to ChatGPT. For those unsure about its content, I recommend checking out the free sample on Amazon – it's undoubtedly worth every penny.

hawkinflight Jan 05, 2024

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

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