Articoli correlati a Building RAG Applications: A Hands-On Guide to Retrieval-Aug...
Building RAG Applications: A Hands-On Guide to Retrieval-Augmented Generation with Python - Brossura
Libro 5 di 15: The Practical Tech Guide Series
Sinossi
Build practical RAG applications without getting lost in vendor noise.
Retrieval-Augmented Generation, or RAG, is one of the most useful patterns for building AI applications that answer from real documents instead of unsupported guesses. But many explanations jump too quickly into tools, vector databases, and frameworks before explaining how the system actually works.
This book takes a practical, project-first approach.
You will build docs_rag_app, a small local RAG application in Python that answers questions over a folder of documents. Step by step, you will learn how to load documents, split them into chunks, retrieve relevant evidence, generate grounded answers, add citations, evaluate quality, and debug bad results.
Along the way, the book explains the ideas behind the code:
- why LLMs need retrieval
- how RAG differs from fine-tuning, long context, memory, and tool calls
- how chunking affects answer quality
- how lexical and vector retrieval work
- why reranking improves difficult cases
- how citations and abstention reduce hallucination risk
- how to evaluate and refresh a RAG system
- why retrieved text should be treated as evidence, not trusted instructions
The goal is not to build the largest possible AI system. The goal is to build a small RAG application that is easy to inspect, easy to debug, and strong enough to improve.
If you can read basic Python and want to understand how retrieval changes AI application design, this guide gives you a clear path from first working prototype to practical local RAG app.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Risultati della ricerca per Building RAG Applications: A Hands-On Guide to Retrieval-Aug...
Foto dell'editore
Building RAG Applications
Print on DemandDa: PBShop.store US, Wood Dale, IL, U.S.A.
Valutazione del venditore 5 su 5 stelle
PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Codice articolo L0-9798197521361
Compra nuovo
EUR 12,93
Spedizione gratuita
Spedito in U.S.A.
Spedito in U.S.A.
Quantità: Più di 20 disponibili
Foto dell'editore
Building RAG Applications
Print on DemandDa: PBShop.store UK, Fairford, GLOS, Regno Unito
Valutazione del venditore 5 su 5 stelle
PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Codice articolo L0-9798197521361
Compra nuovo
EUR 11,62
Spedizione EUR 3,89
Spedito da Regno Unito a U.S.A.
Spedito da Regno Unito a U.S.A.
Quantità: Più di 20 disponibili
Foto dell'editore
Building RAG Applications (Paperback)
Print on DemandDa: CitiRetail, Stevenage, Regno Unito
Valutazione del venditore 5 su 5 stelle
Paperback. Condizione: new. Paperback. Build practical RAG applications without getting lost in vendor noise.Retrieval-Augmented Generation, or RAG, is one of the most useful patterns for building AI applications that answer from real documents instead of unsupported guesses. But many explanations jump too quickly into tools, vector databases, and frameworks before explaining how the system actually works.This book takes a practical, project-first approach.You will build docs_rag_app, a small local RAG application in Python that answers questions over a folder of documents. Step by step, you will learn how to load documents, split them into chunks, retrieve relevant evidence, generate grounded answers, add citations, evaluate quality, and debug bad results.Along the way, the book explains the ideas behind the code: why LLMs need retrievalhow RAG differs from fine-tuning, long context, memory, and tool callshow chunking affects answer qualityhow lexical and vector retrieval workwhy reranking improves difficult caseshow citations and abstention reduce hallucination riskhow to evaluate and refresh a RAG systemwhy retrieved text should be treated as evidence, not trusted instructionsThe goal is not to build the largest possible AI system. The goal is to build a small RAG application that is easy to inspect, easy to debug, and strong enough to improve.If you can read basic Python and want to understand how retrieval changes AI application design, this guide gives you a clear path from first working prototype to practical local RAG app. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9798197521361
Compra nuovo
EUR 15,20
Spedizione EUR 43,73
Spedito da Regno Unito a U.S.A.
Spedito da Regno Unito a U.S.A.
Quantità: 1 disponibili