Retrieval Augmented Generation Vector Databases by Newton Gus (12 results)

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Paperback. Condition: new. Paperback. Retrieval-Augmented Generation (RAG) and Vector Databases: A Practical Guide for AI DevelopersAI is only as powerful as the information it can access. Retrieval-Augmented Generation (RAG) bridges the gap between static language models and real-time knowledge retrieval, enabling AI to generate more accurate, context-aware responses. This book provides a hands-on, practical guide to building RAG-powered AI systems using vector databases for efficient and intelligent information retrieval.From understanding how RAG improves AI responses to implementing scalable retrieval systems, this book walks you through step-by-step tutorials, real-world applications, and best practices for optimization. Whether you're developing AI-powered search engines, chatbots, or enterprise knowledge systems, this guide equips you with the skills to build robust retrieval-enhanced AI models.This book takes a deep dive into RAG and vector search, covering: How RAG enhances AI models by retrieving relevant context before generating responses.Vector databases (FAISS, Pinecone, Weaviate, and more) and their role in AI-powered search.Step-by-step implementation of RAG pipelines using Python and modern frameworks like LangChain and Haystack.Optimizing retrieval performance to improve accuracy, reduce latency, and scale AI systems.Real-world use cases in enterprise search, personalized recommendations, chatbots, healthcare, finance, and cybersecurity.By the end of this book, you'll have the practical knowledge to build, deploy, and scale RAG-based applications.Key Features of This BookComprehensive coverage of RAG, vector databases, and retrieval techniques.Practical tutorials and hands-on code examples using Python.Real-world applications for enterprise AI, e-commerce, and search engines.Step-by-step guidance on optimizing retrieval and reducing AI hallucinations.Deployment strategies for scaling RAG pipelines in cloud and on-premise environments.This book is perfect for: AI developers and data scientists building retrieval-augmented AI systems.Machine learning engineers optimizing search and retrieval models.Software developers working on chatbots, virtual assistants, and AI-powered search.AI researchers and enthusiasts interested in RAG, vector search, and LLMs.Unlock the full potential of AI-powered retrieval with Retrieval-Augmented Generation and Vector Databases! Whether you're a beginner or an experienced AI professional, this book will equip you with the skills to build advanced, intelligent systems. Get your copy today and start building RAG-powered AI applications that deliver smarter, more accurate responses. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Condition: New.

Paperback. Condition: new. Paperback. Retrieval-Augmented Generation (RAG) and Vector Databases: A Practical Guide for AI DevelopersAI is only as powerful as the information it can access. Retrieval-Augmented Generation (RAG) bridges the gap between static language models and real-time knowledge retrieval, enabling AI to generate more accurate, context-aware responses. This book provides a hands-on, practical guide to building RAG-powered AI systems using vector databases for efficient and intelligent information retrieval.From understanding how RAG improves AI responses to implementing scalable retrieval systems, this book walks you through step-by-step tutorials, real-world applications, and best practices for optimization. Whether you're developing AI-powered search engines, chatbots, or enterprise knowledge systems, this guide equips you with the skills to build robust retrieval-enhanced AI models.This book takes a deep dive into RAG and vector search, covering: How RAG enhances AI models by retrieving relevant context before generating responses.Vector databases (FAISS, Pinecone, Weaviate, and more) and their role in AI-powered search.Step-by-step implementation of RAG pipelines using Python and modern frameworks like LangChain and Haystack.Optimizing retrieval performance to improve accuracy, reduce latency, and scale AI systems.Real-world use cases in enterprise search, personalized recommendations, chatbots, healthcare, finance, and cybersecurity.By the end of this book, you'll have the practical knowledge to build, deploy, and scale RAG-based applications.Key Features of This BookComprehensive coverage of RAG, vector databases, and retrieval techniques.Practical tutorials and hands-on code examples using Python.Real-world applications for enterprise AI, e-commerce, and search engines.Step-by-step guidance on optimizing retrieval and reducing AI hallucinations.Deployment strategies for scaling RAG pipelines in cloud and on-premise environments.This book is perfect for: AI developers and data scientists building retrieval-augmented AI systems.Machine learning engineers optimizing search and retrieval models.Software developers working on chatbots, virtual assistants, and AI-powered search.AI researchers and enthusiasts interested in RAG, vector search, and LLMs.Unlock the full potential of AI-powered retrieval with Retrieval-Augmented Generation and Vector Databases! Whether you're a beginner or an experienced AI professional, this book will equip you with the skills to build advanced, intelligent systems. Get your copy today and start building RAG-powered AI applications that deliver smarter, more accurate responses. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.