Architecting Rag 2 0 For Ai Agent
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Building AI Agents with LLMs, RAG, and Knowledge Graphs
Author: Salvatore Raieli
language: en
Publisher: Packt Publishing Ltd
Release Date: 2025-07-11
Master LLM fundamentals to advanced techniques like RAG, reinforcement learning, and knowledge graphs to build, deploy, and scale intelligent AI agents that reason, retrieve, and act autonomously Key Features Implement RAG and knowledge graphs for advanced problem-solving Leverage innovative approaches like LangChain to create real-world intelligent systems Integrate large language models, graph databases, and tool use for next-gen AI solutions Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis AI agents book addresses the challenge of building AI that not only generates text but also grounds its responses in real data and takes action. Authored by AI specialists with deep expertise in drug discovery and systems optimization, this guide empowers you to leverage retrieval-augmented generation (RAG), knowledge graphs, and agent-based architectures to engineer truly intelligent behavior. By combining large language models (LLMs) with up-to-date information retrieval and structured knowledge, you'll create AI agents capable of deeper reasoning and more reliable problem-solving. Inside, you'll find a practical roadmap from concept to implementation. You’ll discover how to connect language models with external data via RAG pipelines for increasing factual accuracy and incorporate knowledge graphs for context-rich reasoning. The chapters will help you build and orchestrate autonomous agents that combine planning, tool use, and knowledge retrieval to achieve complex goals. Concrete Python examples built on popular libraries, along with real-world case studies, reinforce each concept and show you how these techniques come together. By the end of this book, you’ll be well-equipped to build intelligent AI agents that reason, retrieve, and interact dynamically, empowering you to deploy powerful AI solutions across industries.What you will learn Learn how LLMs work, their structure, uses, and limits, and design RAG pipelines to link them to external data Build and query knowledge graphs for structured context and factual grounding Develop AI agents that plan, reason, and use tools to complete tasks Integrate LLMs with external APIs and databases to incorporate live data Apply techniques to minimize hallucinations and ensure accurate outputs Orchestrate multiple agents to solve complex, multi-step problems Optimize prompts, memory, and context handling for long-running tasks Deploy and monitor AI agents in production environments Who this book is for If you are a data scientist or researcher who wants to learn how to create and deploy an AI agent to solve limitless tasks, this book is for you. To get the most out of this book, you should have basic knowledge of Python and Gen AI. This book is also excellent for experienced data scientists who want to explore state-of-the-art developments in LLM and LLM-based applications.
Mastering OpenAI for Enterprise: Unlock the Power of OpenAI to Build Intelligent Applications for Businesses with GPT, DALL-E, RAG, and AI Agents
Author: Sriram Subramanian
language: en
Publisher: Orange Education Pvt Limited
Release Date: 2025-03-11
Master OpenAI and Unlock the Future of AI-Powered Innovation Key Features● In-depth exploration of OpenAI tools, models, and enterprise use cases● Hands-on projects with extensive code samples for practical learning● Real-world case studies with ethical AI insights and best practices Book DescriptionOpenAI is transforming industries with cutting-edge AI models, redefining how businesses operate, innovate, and compete. Mastering OpenAI for Enterprise is your definitive guide to harnessing the power of OpenAI’s groundbreaking technologies, including GPT models, DALL·E, and more. Designed for AI engineers, developers, and business leaders, this book offers an in-depth understanding of OpenAI’s tools and their real-world applications in enterprise settings. This hands-on guide provides a structured learning path, featuring practical code samples, step-by-step implementations, and industry case studies that bridge theory with practice. Whether you're building intelligent chatbots, leveraging AI for automation, or exploring generative AI for creative solutions, this book equips you with the knowledge and skills to seamlessly integrate OpenAI into your workflows. Ethical AI development and responsible implementation are also key themes, ensuring that innovation is balanced with accountability. As AI continues to evolve at an unprecedented pace, mastering OpenAI is no longer optional—it’s essential. The future belongs to those who can effectively leverage these technologies. Don’t get left behind—equip yourself with the expertise needed to stay ahead in the AI revolution. What you will learn● Gain expertise in OpenAI’s models, APIs, and enterprise applications● Build intelligent chatbots and virtual assistants using ChatGPT● Implement ethical AI practices for responsible and fair deployment● Optimize and deploy OpenAI models for scalable business solutions● Analyze real-world case studies to drive AI-powered innovation● Leverage generative AI to automate, enhance, and transform workflows
Generative AI on AWS
Author: Chris Fregly
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2023-11-13
Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology. You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images. Apply generative AI to your business use cases Determine which generative AI models are best suited to your task Perform prompt engineering and in-context learning Fine-tune generative AI models on your datasets with low-rank adaptation (LoRA) Align generative AI models to human values with reinforcement learning from human feedback (RLHF) Augment your model with retrieval-augmented generation (RAG) Explore libraries such as LangChain and ReAct to develop agents and actions Build generative AI applications with Amazon Bedrock