--- product_id: 604032537 title: "Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs" price: "£48.09" currency: GBP in_stock: true reviews_count: 13 url: https://www.desertcart.co.uk/products/604032537-generative-ai-with-langchain-build-large-language-model-llm-apps store_origin: GB region: United Kingdom --- # Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs **Price:** £48.09 **Availability:** ✅ In Stock ## Quick Answers - **What is this?** Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs - **How much does it cost?** £48.09 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.co.uk](https://www.desertcart.co.uk/products/604032537-generative-ai-with-langchain-build-large-language-model-llm-apps) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Description 2024 Edition – Get to grips with the LangChain framework to develop production-ready applications, including agents and personal assistants. The 2024 edition features updated code examples and an improved GitHub repository. Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free Key Features 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 Book 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. 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 Who this book is 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. Table of Contents What Is Generative AI? LangChain for LLM Apps Getting Started with LangChain Building Capable Assistants Building a Chatbot like ChatGPT Developing Software with Generative AI LLMs for Data Science Customizing LLMs and Their Output Generative AI in Production The Future of Generative Models Review: Great for getting started w/LLM apps - 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. Review: A good book, be prepared for extra research. - This is one of the best books on Generative AI (at least developing with/against) that I've seen thus far. That said, it's not perfect - in fact, kind of far from it. The Good: The first 3-4 chapters (especially the first 3) are a goldmine when it comes to a good landscape view of AI currently. What I greatly appreciate about this book is that while it still has some focus on OpenAI (through use), it does talk about other models out there. Way too many books focus on integrating with OpenAI instead of illustrating that you can (and sometimes should) be running your own models. I found the first 3 chapters to be, by far, the best in the book. In my opinion, the first three chapters are worth the cost of the book. The Bad: You'll need to be prepared for a lot of extra research. Starting around Chapter 4, you'll see new syntax and classes used that don't explain _WHY_ they're being suggested. There's also a good deal of hand waiving in terms of the details for the implementation choices. Below is an example: On page 158, you'll read about the ConversationalRetrievalChain - which is intended to, given vector store, search against documents loaded in it. Then, on page 161 you're presented with "ConversationChain". It mentions that it's to remember past interactions with the LLM, then gives a bunch of code. It doesn't do anything to contrast the two. In other words, you'll be finding something, noting it down on paper, and finding the distinction yourself (I found myself on a LangChain ticket that explained the difference). The other "bad" about the book is also part "good" too (somewhat). The Github repo is important to find and pull from. The code in the book in some cases works poorly, or is missing crucial points that if you type what's presented in the book, you may not get a fully running thing. So the github repo, grepping through repo looking for the code that best matches what you're reading then copying/pasting is important. It doesn't help that the Github repo doesn't denote the projects by chapter so it's easier to find the code. You'll also want to do this anyways, because even in earlier revisions of the code, there were bugs present. E.g. with the chatbot, multiple "AI" outputs because of the way it was called/instantiated. To the author's credit, he is making changes and most of them are good (not all, I find myself disagreeing with some of the changes and have multiple versions of his code up since some elements in the older versions are better for learning than the newest code checked on yesterday). The theme in the "bad" is be prepared to search, take notes, and write/highlight in the book. Summary: Despite the negatives, this book is absolutely fantastic and the best that I've found so far. O'Reilly had the preview version, which I was reading til the full release came out and bought the book too. I do recommend the book if you want to develop against LLMs - and I also recommend it for the first 3 chapters for more summary information. Just a few tips: 1. Make sure you do as many of your own code examples using what's presented in the book at the Github profile to solidify knowledge. If you plan to read the book and not implement right away, you'll gain little after Chapter 3. 2. Take notes - ample notes, search and contrast. The name differences between the classes are important, and there's little explanation in the book. I find myself noting the class names on paper, drawing relationships, and explaining them based off searches (and what little may be in the book that helps). That all said, I do recommend the book. ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #1,317,446 in Books ( See Top 100 in Books ) #455 in Computer Neural Networks #465 in Natural Language Processing (Books) #1,093 in Python Programming | | Customer Reviews | 4.1 out of 5 stars 95 Reviews | ## Images ![Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs - Image 1](https://m.media-amazon.com/images/I/71TwKPAVEQL.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ Great for getting started w/LLM apps *by H***T on January 5, 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. ### ⭐⭐⭐⭐ A good book, be prepared for extra research. *by D***T on January 13, 2024* This is one of the best books on Generative AI (at least developing with/against) that I've seen thus far. That said, it's not perfect - in fact, kind of far from it. The Good: The first 3-4 chapters (especially the first 3) are a goldmine when it comes to a good landscape view of AI currently. What I greatly appreciate about this book is that while it still has some focus on OpenAI (through use), it does talk about other models out there. Way too many books focus on integrating with OpenAI instead of illustrating that you can (and sometimes should) be running your own models. I found the first 3 chapters to be, by far, the best in the book. In my opinion, the first three chapters are worth the cost of the book. The Bad: You'll need to be prepared for a lot of extra research. Starting around Chapter 4, you'll see new syntax and classes used that don't explain _WHY_ they're being suggested. There's also a good deal of hand waiving in terms of the details for the implementation choices. Below is an example: On page 158, you'll read about the ConversationalRetrievalChain - which is intended to, given vector store, search against documents loaded in it. Then, on page 161 you're presented with "ConversationChain". It mentions that it's to remember past interactions with the LLM, then gives a bunch of code. It doesn't do anything to contrast the two. In other words, you'll be finding something, noting it down on paper, and finding the distinction yourself (I found myself on a LangChain ticket that explained the difference). The other "bad" about the book is also part "good" too (somewhat). The Github repo is important to find and pull from. The code in the book in some cases works poorly, or is missing crucial points that if you type what's presented in the book, you may not get a fully running thing. So the github repo, grepping through repo looking for the code that best matches what you're reading then copying/pasting is important. It doesn't help that the Github repo doesn't denote the projects by chapter so it's easier to find the code. You'll also want to do this anyways, because even in earlier revisions of the code, there were bugs present. E.g. with the chatbot, multiple "AI" outputs because of the way it was called/instantiated. To the author's credit, he is making changes and most of them are good (not all, I find myself disagreeing with some of the changes and have multiple versions of his code up since some elements in the older versions are better for learning than the newest code checked on yesterday). The theme in the "bad" is be prepared to search, take notes, and write/highlight in the book. Summary: Despite the negatives, this book is absolutely fantastic and the best that I've found so far. O'Reilly had the preview version, which I was reading til the full release came out and bought the book too. I do recommend the book if you want to develop against LLMs - and I also recommend it for the first 3 chapters for more summary information. Just a few tips: 1. Make sure you do as many of your own code examples using what's presented in the book at the Github profile to solidify knowledge. If you plan to read the book and not implement right away, you'll gain little after Chapter 3. 2. Take notes - ample notes, search and contrast. The name differences between the classes are important, and there's little explanation in the book. I find myself noting the class names on paper, drawing relationships, and explaining them based off searches (and what little may be in the book that helps). That all said, I do recommend the book. ### ⭐⭐⭐⭐⭐ Must have book for LLM and Generative AI *by H***N on March 24, 2024* "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. ## Frequently Bought Together - Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs - LangChain Crash Course: Build OpenAI LLM powered Apps: Fast track to building OpenAI LLM powered Apps using Python - Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs (Addison-Wesley Data & Analytics Series) --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.co.uk/products/604032537-generative-ai-with-langchain-build-large-language-model-llm-apps](https://www.desertcart.co.uk/products/604032537-generative-ai-with-langchain-build-large-language-model-llm-apps) --- *Product available on Desertcart United Kingdom* *Store origin: GB* *Last updated: 2026-05-22*