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Artículo: AMZ-B0G8D953R1
Mastering Chroma: Vector Databases for Semantic Search, LLM Memory, and Real-Time Retrieval
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Paperback
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En stock
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0.59 kg
0.59 kg
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Sí
Sí
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Nuevo
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Amazon
Amazon
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USA
USA
Sobre este producto
- As modern AI systems grow more sophisticated, the need for efficient storage, retrieval, and organization of semantic information becomes essential. Vector databases solve this challenge by enabling fast similarity search across embeddings generated by Large Language Models and multimodal AI systems. Chroma, an advanced open-source vector database, delivers a powerful and developer-friendly platform for semantic search, long-term LLM memory, real-time retrieval, and production-grade RAG pipelines. This technology forms the backbone of intelligent assistants, autonomous agents, recommendation engines, chatbots, and AI knowledge systems used across industries today. Book summary Mastering Chroma: Vector Databases for Semantic Search, LLM Memory, and Real-Time Retrieval is a practical and comprehensive guide for developers, engineers, and AI practitioners who want to build retrieval-powered applications. This book provides a deep understanding of embeddings, vector search principles, Chroma internals, and modern RAG workflows. Through real examples and production insights, you learn how to create high-performance semantic search systems, design effective memory architectures, implement scalable retrieval pipelines, and integrate Chroma seamlessly with LLM frameworks. What’s Inside -Clear explanations of embeddings, similarity metrics, and vector search fundamentals -Step-by-step guidance on installing, configuring, and deploying Chroma -Practical techniques for semantic search across text, code, images, audio, and multimodal data -Detailed chapters on building RAG systems, managing short-term and long-term LLM memory, and designing high-accuracy retrieval pipelines -Production-ready strategies for scaling Chroma using Docker, Kubernetes, AWS, GCP, and Azure -Best practices for indexing, chunking, metadata management, caching, optimization, and observability -Real-world case studies and end-to-end code examples for developers building AI-powered applications To the Audience This book is written for software engineers, AI/ML developers, data architects, automation specialists, and technical founders who want to leverage vector databases in real applications. Readers should have basic familiarity with Python and modern AI concepts, but no prior experience with Chroma or vector indexing is required. Whether you are building chatbots, AI search engines, agentic systems, or enterprise knowledge platforms, this book provides everything you need to build retrieval-enhanced solutions with confidence. Transform the way your AI applications search, reason, and remember. Unlock the full potential of semantic retrieval with the definitive guide to Chroma. Start your journey today and build intelligent systems that deliver faster responses, deeper understanding, and smarter decisions—powered by the future of vector database technology.
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