Building Llm Applications With Python

BUILDING LLM APPLICATIONS WITH PYTHON

Building AI Intensive Python Applications

Master retrieval-augmented generation architecture and fine-tune your AI stack, along with discovering real-world use cases and best practices to create powerful AI apps Key Features Get to grips with the fundamentals of LLMs, vector databases, and Python frameworks Implement effective retrieval-augmented generation strategies with MongoDB Atlas Optimize AI models for performance and accuracy with model compression and deployment optimization Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe era of generative AI is upon us, and this book serves as a roadmap to harness its full potential. With its help, you’ll learn the core components of the AI stack: large language models (LLMs), vector databases, and Python frameworks, and see how these technologies work together to create intelligent applications. The chapters will help you discover best practices for data preparation, model selection, and fine-tuning, and teach you advanced techniques such as retrieval-augmented generation (RAG) to overcome common challenges, such as hallucinations and data leakage. You’ll get a solid understanding of vector databases, implement effective vector search strategies, refine models for accuracy, and optimize performance to achieve impactful results. You’ll also identify and address AI failures to ensure your applications deliver reliable and valuable results. By evaluating and improving the output of LLMs, you’ll be able to enhance their performance and relevance. By the end of this book, you’ll be well-equipped to build sophisticated AI applications that deliver real-world value.What you will learn Understand the architecture and components of the generative AI stack Explore the role of vector databases in enhancing AI applications Master Python frameworks for AI development Implement Vector Search in AI applications Find out how to effectively evaluate LLM output Overcome common failures and challenges in AI development Who this book is for This book is for software engineers and developers looking to build intelligent applications using generative AI. While the book is suitable for beginners, a basic understanding of Python programming is required to make the most of it.

Build LLM Applications with Python, Ollama, LangChain, and Gradio

Build LLM Applications with Python, Ollama, LangChain, and Gradio: A Hands-On Guide By Prabir Guha Unlock the power of Large Language Models (LLMs) through practical, real-world application! This hands-on guide demystifies how LLMs work, how to run them locally with Ollama, and how to build cutting-edge applications with Python, LangChain, and Gradio - no cloud dependency required. Starting with the evolution of Natural Language Processing (NLP) from early rule-based systems to today's transformer-based LLMs like GPT and BERT, the book provides a solid technical foundation. You'll learn how to install and configure the Ollama framework to run models like LLaMA 3.1 on your own workstation, ensuring privacy, low latency, and no API costs. Through step-by-step examples, you'll build your first Python LLM applications, master prompting techniques, and explore LangChain - a powerful framework for chaining prompts, tools, and memory. Practical use cases include text summarization, generation, QA systems, and structured data extraction. The book also introduces Agentic Technology, allowing your LLM applications to reason dynamically and use external tools autonomously. You'll build user-friendly chat interfaces with Gradio, mimicking popular conversational AIs like ChatGPT, and dive into Retrieval-Augmented Generation (RAG) systems that enrich LLMs with domain-specific knowledge, such as querying documents like a Medicare Guide. Finally, the book discusses the major challenges facing LLMs - bias, hallucination, environmental impact - and explores future trends such as multimodal AI, model optimization, and autonomous AI agents. Whether you're a developer, researcher, or enthusiast, this guide equips you with the skills and tools to build intelligent, efficient, and domain-adaptive LLM applications - all locally and hands-on. Key Topics Covered: How LLMs work (Transformer models, Encoders, Decoders) Setting up the Ollama framework for local LLM execution Building LLM applications with Python Crafting effective prompts for optimal model behavior Developing advanced LLM apps with LangChain Integrating agents for autonomous reasoning Creating conversational UIs using Gradio Implementing Retrieval-Augmented Generation (RAG) systems Future challenges and trends in LLM evolution If you want to build and deploy your own LLM-powered systems - without relying on expensive cloud services - this book is your practical, hands-on guide.