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

"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.

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.

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.

"Generative AI with Langchain" comprehensively explores building Generative AI software and individual components using Langchain. From understanding the fundamentals of generative models to practical applications in various sectors, the book navigates through topics such as enhancing language models with external knowledge, developing AI-driven assistants, and deploying generative AI in real-world scenarios. It addresses critical concerns like misinformation, ethical deployment, and the future societal impacts of AI technologies.With insights into LLM customization techniques, and deployment strategies, the book offers a roadmap for harnessing Langchain's potential in the Gen AI landscape.

Explanation is good. Need a basic knowledge of python and gen ai to start this book. Worth for buying. Modules are well organized.

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

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.

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.

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