Mastering Retrieval-Augmented Generation (RAG): Build and Deploy RAG Systems with Vector Databases, Hybrid Search, and LLM Pipelines (Mastering LLMs Series, Band 3) - Softcover
Buch 3 von 3: Mastering LLMs Series
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Unlock the full power of large language models with Mastering Retrieval-Augmented Generation (RAG). This hands-on guide walks you through building production-grade RAG systems that fuse transformer-based LLMs with cutting-edge vector databases, keyword-and-semantic hybrid search, and custom pipelines. You’ll start by mastering the core concepts of retrieval vs. generation, then dive into real-world tools—LlamaIndex, LangChain, Pinecone, Weaviate, and ChromaDB—to index, retrieve, and refine context at scale.
Each chapter is packed with proven recipes and end-to-end projects: from a legal-document QA assistant to a personalized news summarizer and a compliant healthcare chatbot. Learn how to optimize context injection, implement PEFT/LoRA fine-tuning, safeguard data privacy (GDPR/HIPAA), and deploy auto-scaling microservices with CI/CD. Detailed performance-tuning, monitoring strategies, and cost-management best practices ensure you deliver low-latency, high-accuracy applications.
Whether you’re an ML engineer, data scientist, or technical lead, this book arms you with the architecture patterns, code examples, and operational know-how to launch RAG-powered AI applications that users love—and trust. Dominate the RAG niche on Amazon and in production.
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Mastering Retrieval-Augmented Generation (RAG): Build and Deploy RAG Systems with Vector Databases, Hybrid Search, and LLM Pipelines
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Mastering Retrieval-Augmented Generation (RAG) (Paperback)
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Paperback. Zustand: new. Paperback. Unlock the full power of large language models with Mastering Retrieval-Augmented Generation (RAG). This hands-on guide walks you through building production-grade RAG systems that fuse transformer-based LLMs with cutting-edge vector databases, keyword-and-semantic hybrid search, and custom pipelines. You'll start by mastering the core concepts of retrieval vs. generation, then dive into real-world tools-LlamaIndex, LangChain, Pinecone, Weaviate, and ChromaDB-to index, retrieve, and refine context at scale.Each chapter is packed with proven recipes and end-to-end projects: from a legal-document QA assistant to a personalized news summarizer and a compliant healthcare chatbot. Learn how to optimize context injection, implement PEFT/LoRA fine-tuning, safeguard data privacy (GDPR/HIPAA), and deploy auto-scaling microservices with CI/CD. Detailed performance-tuning, monitoring strategies, and cost-management best practices ensure you deliver low-latency, high-accuracy applications.Whether you're an ML engineer, data scientist, or technical lead, this book arms you with the architecture patterns, code examples, and operational know-how to launch RAG-powered AI applications that users love-and trust. Dominate the RAG niche on Amazon and in production. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9798296901736
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Mastering Retrieval-Augmented Generation (RAG): Build and Deploy RAG Systems with Vector Databases, Hybrid Search, and LLM Pipelines (Mastering LLMs Series)
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Mastering Retrieval-Augmented Generation (RAG): Build and Deploy RAG Systems with Vector Databases, Hybrid Search, and LLM Pipelines
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Mastering Retrieval-Augmented Generation (RAG) (Paperback)
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Paperback. Zustand: new. Paperback. Unlock the full power of large language models with Mastering Retrieval-Augmented Generation (RAG). This hands-on guide walks you through building production-grade RAG systems that fuse transformer-based LLMs with cutting-edge vector databases, keyword-and-semantic hybrid search, and custom pipelines. You'll start by mastering the core concepts of retrieval vs. generation, then dive into real-world tools-LlamaIndex, LangChain, Pinecone, Weaviate, and ChromaDB-to index, retrieve, and refine context at scale.Each chapter is packed with proven recipes and end-to-end projects: from a legal-document QA assistant to a personalized news summarizer and a compliant healthcare chatbot. Learn how to optimize context injection, implement PEFT/LoRA fine-tuning, safeguard data privacy (GDPR/HIPAA), and deploy auto-scaling microservices with CI/CD. Detailed performance-tuning, monitoring strategies, and cost-management best practices ensure you deliver low-latency, high-accuracy applications.Whether you're an ML engineer, data scientist, or technical lead, this book arms you with the architecture patterns, code examples, and operational know-how to launch RAG-powered AI applications that users love-and trust. Dominate the RAG niche on Amazon and in production. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798296901736
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