Mastering LLM Applications with LangChain and Hugging Face


Book Description

DESCRIPTION The book is all about the basics of NLP, generative AI, and their specific component LLM. In this book, we have provided conceptual knowledge about different terminologies and concepts of NLP and NLG with practical hands-on. This comprehensive book offers a deep dive into the world of NLP and LLMs. Starting with the fundamentals of Python programming and code editors, the book gradually introduces NLP concepts, including text preprocessing, word embeddings, and transformer architectures. You will explore the architecture and capabilities of popular models like GPT-3 and BERT. The book also covers practical aspects of LLM usage for RAG applications using frameworks like LangChain and Hugging Face and deploying them in real world applications. With a focus on both theoretical knowledge and hands-on experience, this book is ideal for anyone looking to master the art of NLP and LLMs. The book also contains AWS Cloud deployment, which will help readers step into the world of cloud computing. As the book contains both theoretical and practical approaches, it will help the readers to gain confidence in the deployment of LLMs for any use cases, as well as get acquainted with the required generative AI knowledge to crack the interviews. KEY FEATURES ● Covers Python basics, NLP concepts, and terminologies, including LLM and RAG concepts. ● Provides exposure to LangChain, Hugging Face ecosystem, and chatbot creation using custom data. ● Guides on integrating chatbots with real-time applications and deploying them on AWS Cloud. WHAT YOU WILL LEARN ● Basics of Python, which contains Python concepts, installation, and code editors. ● Foundation of NLP and generative AI concepts and different terminologies being used in NLP and generative AI domain. ● LLMs and their importance in the cutting edge of AI. ● Creating chatbots using custom data using open source LLMs without spending a single penny. ● Integration of chatbots with real-world applications like Telegram. WHO THIS BOOK IS FOR This book is ideal for beginners and freshers entering the AI or ML field, as well as those at an intermediate level looking to deepen their understanding of generative AI, LLMs, and cloud deployment. TABLE OF CONTENTS 1. Introduction to Python and Code Editors 2. Installation of Python, Required Packages, and Code Editors 3. Ways to Run Python Scripts 4. Introduction to NLP and its Concepts 5. Introduction to Large Language Models 6. Introduction of LangChain, Usage and Importance 7. Introduction of Hugging Face, its Usage and Importance 8. Creating Chatbots Using Custom Data with LangChain and Hugging Face Hub 9. Hyperparameter Tuning and Fine Tuning Pre-Trained Models 10. Integrating LLMs into Real-World Applications–Case Studies 11. Deploying LLMs in Cloud Environments for Scalability 12. Future Directions: Advances in LLMs and Beyond Appendix A: Useful Tips for Efficient LLM Experimentation Appendix B: Resources and References




LangChain & LlamaIndex: A Practical Guide


Book Description

"LangChain & LlamaIndex: A Practical Guide" is an insightful exploration into the world of blockchain technology and its applications within the emerging cryptocurrency market. Authored by leading experts in the field, this book offers a comprehensive overview of LangChain, a cutting-edge blockchain platform, and LlamaIndex, a unique cryptocurrency index. Readers are taken on a journey through the intricacies of LangChain, learning about its architecture, functionality, and potential uses in various industries. From its secure decentralized network to its smart contract capabilities, the book provides clear explanations and practical examples to help readers grasp the fundamentals of this innovative technology. In parallel, the book delves into the fascinating realm of the LlamaIndex, a benchmark for tracking the performance of cryptocurrencies. Through detailed analysis and case studies, readers gain valuable insights into how the LlamaIndex is constructed, its methodology for selecting and weighting cryptocurrencies, and its significance in the broader financial landscape. More than just a theoretical exploration, "LangChain & LlamaIndex: A Practical Guide" equips readers with the knowledge and tools they need to navigate the rapidly evolving world of blockchain and cryptocurrencies. Whether you're a novice looking to understand the basics or a seasoned investor seeking to stay ahead of the curve, this book offers invaluable guidance for leveraging LangChain and interpreting the LlamaIndex to make informed decisions in the digital asset space. With its accessible language, real-world examples, and actionable advice, this book is a must-read for anyone interested in unlocking the potential of blockchain technology and cryptocurrency investing.




Generative AI with LangChain


Book 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. Purchase of the print or Kindle book includes a free PDF eBook. 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 DescriptionChatGPT 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.




Building LLM Powered Applications


Book Description

Get hands-on with GPT 3.5, GPT 4, LangChain, Llama 2, Falcon LLM and more, to build LLM-powered sophisticated AI applications Key Features Embed LLMs into real-world applications Use LangChain to orchestrate LLMs and their components within applications Grasp basic and advanced techniques of prompt engineering Book DescriptionBuilding LLM Powered Applications delves into the fundamental concepts, cutting-edge technologies, and practical applications that LLMs offer, ultimately paving the way for the emergence of large foundation models (LFMs) that extend the boundaries of AI capabilities. The book begins with an in-depth introduction to LLMs. We then explore various mainstream architectural frameworks, including both proprietary models (GPT 3.5/4) and open-source models (Falcon LLM), and analyze their unique strengths and differences. Moving ahead, with a focus on the Python-based, lightweight framework called LangChain, we guide you through the process of creating intelligent agents capable of retrieving information from unstructured data and engaging with structured data using LLMs and powerful toolkits. Furthermore, the book ventures into the realm of LFMs, which transcend language modeling to encompass various AI tasks and modalities, such as vision and audio. Whether you are a seasoned AI expert or a newcomer to the field, this book is your roadmap to unlock the full potential of LLMs and forge a new era of intelligent machines.What you will learn Explore the core components of LLM architecture, including encoder-decoder blocks and embeddings Understand the unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLM Use AI orchestrators like LangChain, with Streamlit for the frontend Get familiar with LLM components such as memory, prompts, and tools Learn how to use non-parametric knowledge and vector databases Understand the implications of LFMs for AI research and industry applications Customize your LLMs with fine tuning Learn about the ethical implications of LLM-powered applications Who this book is for Software engineers and data scientists who want hands-on guidance for applying LLMs to build applications. The book will also appeal to technical leaders, students, and researchers interested in applied LLM topics. We don’t assume previous experience with LLM specifically. But readers should have core ML/software engineering fundamentals to understand and apply the content.




Mastering Large Language Models


Book Description

Do not just talk AI, build it: Your guide to LLM application development KEY FEATURES ● Explore NLP basics and LLM fundamentals, including essentials, challenges, and model types. ● Learn data handling and pre-processing techniques for efficient data management. ● Understand neural networks overview, including NN basics, RNNs, CNNs, and transformers. ● Strategies and examples for harnessing LLMs. DESCRIPTION Transform your business landscape with the formidable prowess of large language models (LLMs). The book provides you with practical insights, guiding you through conceiving, designing, and implementing impactful LLM-driven applications. This book explores NLP fundamentals like applications, evolution, components and language models. It teaches data pre-processing, neural networks , and specific architectures like RNNs, CNNs, and transformers. It tackles training challenges, advanced techniques such as GANs, meta-learning, and introduces top LLM models like GPT-3 and BERT. It also covers prompt engineering. Finally, it showcases LLM applications and emphasizes responsible development and deployment. With this book as your compass, you will navigate the ever-evolving landscape of LLM technology, staying ahead of the curve with the latest advancements and industry best practices. WHAT YOU WILL LEARN ● Grasp fundamentals of natural language processing (NLP) applications. ● Explore advanced architectures like transformers and their applications. ● Master techniques for training large language models effectively. ● Implement advanced strategies, such as meta-learning and self-supervised learning. ● Learn practical steps to build custom language model applications. WHO THIS BOOK IS FOR This book is tailored for those aiming to master large language models, including seasoned researchers, data scientists, developers, and practitioners in natural language processing (NLP). TABLE OF CONTENTS 1. Fundamentals of Natural Language Processing 2. Introduction to Language Models 3. Data Collection and Pre-processing for Language Modeling 4. Neural Networks in Language Modeling 5. Neural Network Architectures for Language Modeling 6. Transformer-based Models for Language Modeling 7. Training Large Language Models 8. Advanced Techniques for Language Modeling 9. Top Large Language Models 10. Building First LLM App 11. Applications of LLMs 12. Ethical Considerations 13. Prompt Engineering 14. Future of LLMs and Its Impact







Mastering NLP from Foundations to LLMs


Book Description

Enhance your NLP proficiency with modern frameworks like LangChain, explore mathematical foundations and code samples, and gain expert insights into current and future trends Key Features Learn how to build Python-driven solutions with a focus on NLP, LLMs, RAGs, and GPT Master embedding techniques and machine learning principles for real-world applications Understand the mathematical foundations of NLP and deep learning designs Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDo you want to master Natural Language Processing (NLP) but don’t know where to begin? This book will give you the right head start. Written by leaders in machine learning and NLP, Mastering NLP from Foundations to LLMs provides an in-depth introduction to techniques. Starting with the mathematical foundations of machine learning (ML), you’ll gradually progress to advanced NLP applications such as large language models (LLMs) and AI applications. You’ll get to grips with linear algebra, optimization, probability, and statistics, which are essential for understanding and implementing machine learning and NLP algorithms. You’ll also explore general machine learning techniques and find out how they relate to NLP. Next, you’ll learn how to preprocess text data, explore methods for cleaning and preparing text for analysis, and understand how to do text classification. You’ll get all of this and more along with complete Python code samples. By the end of the book, the advanced topics of LLMs’ theory, design, and applications will be discussed along with the future trends in NLP, which will feature expert opinions. You’ll also get to strengthen your practical skills by working on sample real-world NLP business problems and solutions.What you will learn Master the mathematical foundations of machine learning and NLP Implement advanced techniques for preprocessing text data and analysis Design ML-NLP systems in Python Model and classify text using traditional machine learning and deep learning methods Understand the theory and design of LLMs and their implementation for various applications in AI Explore NLP insights, trends, and expert opinions on its future direction and potential Who this book is for This book is for deep learning and machine learning researchers, NLP practitioners, ML/NLP educators, and STEM students. Professionals working with text data as part of their projects will also find plenty of useful information in this book. Beginner-level familiarity with machine learning and a basic working knowledge of Python will help you get the best out of this book.




LangChain for RAG Beginners - Build Your First Powerful AI GPT Agent


Book Description

Dive into the world of advanced AI with "Python LangChain for RAG Beginners" ✔ Learn how to code Agentic RAG Powered Chatbot Systems. ✔ Empower your Agents with Tools ✔ Learn how to Create your Own Agents This comprehensive guide takes you on a journey through LangChain, an innovative framework designed to harness the power of Generative Pre-trained Transformers (GPTs) and other large language models (LLMs) for creating sophisticated AI-driven applications. Starting from the basics, this book provides a detailed understanding of how to effectively use LangChain to build, customize, and deploy AI applications that can think, learn, and interact seamlessly. You will explore the core concepts of LangChain, including prompt engineering, memory management, and Retrieval Augmented Generation (RAG). Each chapter is packed with practical examples and code snippets that demonstrate real-world applications and use cases. Key highlights include: Getting Started with LangChain: Learn the foundational principles and set up your environment. Advanced Prompt Engineering: Craft effective prompts to enhance AI interactions. Memory Management: Implement various memory types to maintain context and continuity in conversations. Retrieval Augmented Generation (RAG): Integrate external knowledge bases to expand your AI's capabilities. Building Intelligent Agents: Create agents that can autonomously perform tasks and make decisions. Practical Use Cases: Explore building a chat agent with web UI that allows you chatting with documents, web retrieval, vector databases for long term memory and much more ! Whether you are an AI enthusiast, a developer looking to integrate AI into your projects, or a professional aiming to stay ahead in the AI-driven world, " Python LangChain for RAG Beginners" provides the tools and knowledge to elevate your AI skills. Embrace the future of AI and transform your ideas into powerful, intelligent applications with LangChain.




LangChain in your Pocket


Book Description

Unlock the full potential of Generative AI with "LangChain in your Pocket", a hands-on guide that takes you through the robust LangChain framework. This book provides a step-by-step journey into creating powerful applications, from Auto-SQL and NER to custom Agents and Chains, integrating Memory, OutputParsers, RAG for Q&A, Few-Shot Classification, Evaluators, Autonomous AI agents, Advanced Prompt Engineering and many more. NOTE: Drop an email to [email protected] with the transaction receipt for a free PDF version. Key Features: Step-by-step code explanations with expected outputs for each solution. No prerequisites: If you know Python, you're ready to dive in. Practical, hands-on guide with minimal mathematical explanations. Book Description: Since the arrival of ChatGPT in late 2022, the AI landscape has evolved dramatically. "LangChain in your Pocket" invites you to move beyond ChatGPT and explore the versatility of LangChain, a Python/JavaScript framework at the forefront of Large Language Models (LLMs). Whether you're building Classification models, Storyteller, or Internet-enabled GPT, LangChain empowers you to do more. This beginner-friendly introduction covers: Basics of Large Language Models (LLMs) and why LangChain is pivotal. Hello World tutorial for setting up LangChain and creating baseline applications. In-depth chapters on each LangChain module. Advanced problem-solving, including Multi-Document RAG, Hallucinations, NLP chains, and Evaluation for LLMs for supervised and unsupervised ML problems. Dedicated sections for Few-Shot Learning, Advanced Prompt Engineering using ReAct, Autonomous AI agents, and deployment using LangServe. Who should read it? This book is for anyone keen on exploring AI, especially Generative AI. Whether you're a Software Developer, Data Scientist, Student or Content Writer, the focus on diverse use cases in LangChain and GenAI makes it equally valuable to all. Table of Contents Introduction Hello World Different LangChain Modules Models & Prompts Chains Agents OutputParsers & Memory Callbacks RAG Framework & Vector Databases LangChain for NLP problems Handling LLM Hallucinations Evaluating LLMs Advanced Prompt Engineering Autonomous AI agents LangSmith & LangServe Additional Features




Large Language Models


Book Description

Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs -- their intricate architecture, underlying algorithms, and ethical considerations -- require thorough exploration, creating a need for a comprehensive book on this subject. This book provides an authoritative exploration of the design, training, evolution, and application of LLMs. It begins with an overview of pre-trained language models and Transformer architectures, laying the groundwork for understanding prompt-based learning techniques. Next, it dives into methods for fine-tuning LLMs, integrating reinforcement learning for value alignment, and the convergence of LLMs with computer vision, robotics, and speech processing. The book strongly emphasizes practical applications, detailing real-world use cases such as conversational chatbots, retrieval-augmented generation (RAG), and code generation. These examples are carefully chosen to illustrate the diverse and impactful ways LLMs are being applied in various industries and scenarios. Readers will gain insights into operationalizing and deploying LLMs, from implementing modern tools and libraries to addressing challenges like bias and ethical implications. The book also introduces the cutting-edge realm of multimodal LLMs that can process audio, images, video, and robotic inputs. With hands-on tutorials for applying LLMs to natural language tasks, this thorough guide equips readers with both theoretical knowledge and practical skills for leveraging the full potential of large language models. This comprehensive resource is appropriate for a wide audience: students, researchers and academics in AI or NLP, practicing data scientists, and anyone looking to grasp the essence and intricacies of LLMs.