SKU/Artículo: AMZ-B0FSZCLTYB
Vector Databases and Retrieval-Augmented Generation(RAG): How to Build Semantic Search and RAG Pipelines for Real-World AI Applications
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Paperback
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En stock
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0.37 kg
0.37 kg
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Sí
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Nuevo
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Amazon
Amazon
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USA
USA
Sobre este producto
- Large Language Models (LLMs) like GPT, PaLM, and LLaMA are powerful, but they face a critical challenge: their knowledge is frozen at the point of training. This limitation makes them prone to outdated information and hallucinations. The solution is Retrieval-Augmented Generation (RAG)—a revolutionary approach that connects LLMs to vector databases and external knowledge sources, ensuring responses are accurate, up-to-date, and verifiable.This comprehensive guide is designed for both professionals and aspiring practitioners who want to master the design and implementation of RAG pipelines. You’ll learn how to transform LLMs from general-purpose generators into production AI search systems capable of solving enterprise-level problems.Inside, you’ll discover:Core concepts of vector embeddings (text embeddings) and similarity search, the backbone of modern semantic retrieval and search.Step-by-step methods for building semantic search for developers using state-of-the-art databases like Pinecone, Weaviate, and FAISS.Practical workflows to build RAG systems, from data ingestion and embedding generation to vector storage, retrieval, and orchestration.How to integrate with LLMs using LangChain for RAG and extend pipelines with LangChain tutorial–style examples and LangGraph workflows.Advanced techniques including hybrid search, multi-hop retrieval, multimodal vector search, and prompt engineering for RAG optimization.Real-world applications spanning healthcare, finance, law, education, and customer support—showing how organizations deploy production AI search systems that scale.By the end of this book, you will not only understand how RAG works but also gain the skills to design, deploy, and optimize intelligent retrieval pipelines for real-world use. Whether you’re a machine learning engineer, data scientist, software developer, or knowledge worker, this guide will help you bridge the gap between large language models and the practical systems that make them reliable.
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AR$43.553
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