SKU/Artículo: AMZ-B0FJ2TKC2Z
The Vector Database Handbook: Building High-Performance Semantic Search and AI Retrieval Systems
Format:
Kindle
Detalles del producto
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Fuera de stock
Fuera de stock
Peso con empaque:
0.76 kg
0.76 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Are you ready to transform your search capabilities from keyword matching to understanding meaning and context? "The Vector Database Handbook" is your essential, hands-on guide to implementing and scaling high-performance semantic search and AI retrieval solutions. Designed for software engineers, data scientists, architects, and product leaders, this book provides the practical, actionable knowledge you need to build intelligent, AI-powered applications.In today's data-rich world, traditional search falls short when dealing with unstructured information like text, images, and audio. This handbook introduces you to the power of vector databases, which index and search numerical representations (vectors) that encode semantic meaning. Discover how to move beyond "what matches this exact word" to "what's most similar to this idea or image," opening new frontiers for:AI-Native Search: Revolutionize how users discover information.Retrieval-Augmented Generation (RAG): Enhance large language models with real-time, relevant data.Recommendation Engines: Deliver highly personalized suggestions.Multimodal Applications: Build systems that understand and connect different data types.What you will gain from this handbook:Core Concepts & Architecture: Grasp the fundamental principles and multi-layered architecture behind vector database systems, from data ingestion to user-facing applications.Practical Implementation: Dive into real-world code examples and deployment patterns. Learn to choose the right hardware, select optimal embedding models, and prepare your environment with Python, Docker, and Kubernetes.Performance Optimization: Master strategies for efficient indexing, continuous benchmarking (latency, recall, throughput), and fine-tuning your system for peak performance.Security & Governance: Implement robust data governance, encryption, audit logging, and compliance best practices to ensure secure and reliable operations.Advanced Use Cases: Explore diverse applications including semantic search, building recommendation engines, and multimodal similarity search, with detailed walkthroughs.Hands-On with Leading Technologies: Work with popular vector database engines like Pinecone, Milvus, Weaviate, Chroma, Qdrant, and pgvector. Integrate with frameworks such as LangChain, LlamaIndex, and OpenAI APIs.Every chapter is packed with working code and clear explanations, making it easy to replicate and adapt solutions in your own projects. Whether you're building cutting-edge AI applications, enhancing existing search systems, or just curious about the next wave of data management, "The Vector Database Handbook" equips you with the directly applicable knowledge to excel.Start building your high-performance vector search and AI retrieval systems today!
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