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portada Modern Vector Database Development: Designing AI Search Applications with Embeddings, Semantic Retrieval, RAG Pipelines, and Scalable Data Systems
Type
Physical Book
Publisher
Author
Language
English
Pages
256
Format
Paperback
ISBN13
9798172191428

Modern Vector Database Development: Designing AI Search Applications with Embeddings, Semantic Retrieval, RAG Pipelines, and Scalable Data Systems

Alden Dacre (Author) · Independently Published · Paperback

Modern Vector Database Development: Designing AI Search Applications with Embeddings, Semantic Retrieval, RAG Pipelines, and Scalable Data Systems - Alden Dacre

New Book Imported to Taiwan
Delivery: 09 Nov - 17 Nov Shipping: 6 to 7 business days.
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Synopsis "Modern Vector Database Development: Designing AI Search Applications with Embeddings, Semantic Retrieval, RAG Pipelines, and Scalable Data Systems"

Artificial intelligence applications are changing the way information is stored, processed, and retrieved. As organizations work with increasing amounts of unstructured data, traditional database systems alone are no longer enough to support intelligent search experiences.Modern Vector Database Development provides a practical guide to designing and understanding the technologies behind AI-powered search applications. From embeddings and semantic retrieval to Retrieval-Augmented Generation (RAG) pipelines, this book explains the concepts and architectures that enable modern intelligent systems.Readers will explore how vector databases help applications discover relationships, understand context, and retrieve information based on meaning rather than simple keyword matching.Inside this book, you will learn: The fundamentals of vector databases and their role in artificial intelligence applications How embeddings transform text, images, and other information into searchable vector representations The principles behind semantic retrieval and meaning-based search How vector indexing improves search performance and relevance Designing AI search applications using modern retrieval techniques Building Retrieval-Augmented Generation (RAG) pipelines with vector-based storage Connecting vector databases with large language models and AI workflows Understanding scalable data system architecture for AI applications Managing high-dimensional data efficiently Improving reliability, speed, and performance in intelligent retrieval systems Practical approaches for developing real-world AI solutionsModern software applications are moving beyond traditional search methods. Users expect systems that can understand questions, recognize relationships, and provide relevant information instantly.Vector database technology provides the foundation behind many emerging AI solutions, including intelligent assistants, recommendation engines, document search platforms, and knowledge-based applications.This book helps readers understand how these systems are designed, how different components work together, and how developers can apply these concepts when building next-generation AI applications.

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