Isbn: 9798258793430 - the knowledge engine: building rag systems: retrieval-augmented generation with python and vector databases (5 resultados)

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condición: new. Paperback. LLMs are powerful.But without the right data, they are limited.Retrieval Augmented Generation, RAG, transforms AI systems by combining language models with external knowledge sources, enabling accurate, context aware, and up to date responses."The Knowledge Engine" is a practical, hands on guide to building RAG systems using Python and modern vector database technologies.This book shows you how to design intelligent systems that retrieve, reason, and generate with precision.Why RAG is essential for modern AIStandalone models struggle with: outdated knowledgehallucinationslack of domain specific contextlimited accuracy in complex queriesRAG solves these problems by integrating retrieval systems with generation models.With RAG, you can: connect AI to real data sourcesimprove accuracy and relevancereduce hallucinationsbuild domain specific AI systemscreate scalable knowledge driven applicationsWhat you will learnfundamentals of retrieval augmented generationhow vector databases workembeddings and similarity searchbuilding retrieval pipelinesintegrating LLMs with external datachunking and indexing strategiesoptimizing retrieval performanceevaluation and improvement of RAG systemsscaling and deploying RAG applicationsmonitoring and maintaining knowledge systemsFrom documents to intelligent systemsThroughout the book, you will learn how to: convert raw data into searchable embeddingsdesign efficient retrieval systemsconnect retrieval pipelines with generation modelsbuild reliable AI applicationsoptimize performance and costdeploy scalable RAG systemsEach chapter is focused on practical implementation.Practical applicationsenterprise knowledge assistantsdocument search and analysis systemscustomer support automationinternal company knowledge basesAI powered research toolsThese examples reflect real world use cases.Who this book is forAI engineersmachine learning engineersdata scientistsbackend developers working with AIprofessionals building knowledge systemsIf you want to build AI systems that are accurate, context aware, and connected to real data, this book provides the roadmap.Retrieve with precision.Generate with intelligence.Build knowledge driven AI systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New. Print on Demand.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condición: new. Paperback. LLMs are powerful.But without the right data, they are limited.Retrieval Augmented Generation, RAG, transforms AI systems by combining language models with external knowledge sources, enabling accurate, context aware, and up to date responses."The Knowledge Engine" is a practical, hands on guide to building RAG systems using Python and modern vector database technologies.This book shows you how to design intelligent systems that retrieve, reason, and generate with precision.Why RAG is essential for modern AIStandalone models struggle with: outdated knowledgehallucinationslack of domain specific contextlimited accuracy in complex queriesRAG solves these problems by integrating retrieval systems with generation models.With RAG, you can: connect AI to real data sourcesimprove accuracy and relevancereduce hallucinationsbuild domain specific AI systemscreate scalable knowledge driven applicationsWhat you will learnfundamentals of retrieval augmented generationhow vector databases workembeddings and similarity searchbuilding retrieval pipelinesintegrating LLMs with external datachunking and indexing strategiesoptimizing retrieval performanceevaluation and improvement of RAG systemsscaling and deploying RAG applicationsmonitoring and maintaining knowledge systemsFrom documents to intelligent systemsThroughout the book, you will learn how to: convert raw data into searchable embeddingsdesign efficient retrieval systemsconnect retrieval pipelines with generation modelsbuild reliable AI applicationsoptimize performance and costdeploy scalable RAG systemsEach chapter is focused on practical implementation.Practical applicationsenterprise knowledge assistantsdocument search and analysis systemscustomer support automationinternal company knowledge basesAI powered research toolsThese examples reflect real world use cases.Who this book is forAI engineersmachine learning engineersdata scientistsbackend developers working with AIprofessionals building knowledge systemsIf you want to build AI systems that are accurate, context aware, and connected to real data, this book provides the roadmap.Retrieve with precision.Generate with intelligence.Build knowledge driven AI systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…