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This article provides a step-by-step guide to setting up a Retrieval-Augmented Generation (RAG) service using Alibaba Cloud Model Studio, Compute Nest, and AnalyticDB for PostgreSQL. It covers the architecture, deployment, and integration of vector databases with large language models for enterprise knowledge retrieval.
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This article explores the dramatic history of AI training, dissects the critical bottlenecks that threaten to stall progress, and peers into the future of the infrastructure being engineered to overcome these monumental challenges. From the AlexNet moment in 2012 to the Transformer revolution and the current foundation model era, the piece traces how exponential growth in model scale has pushed underlying infrastructure to its limits — and what innovations are being developed to address communication overhead, memory walls, and energy constraints.
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This tutorial demonstrates how to use Alibaba Cloud’s Qwen model to generate SQL queries from natural language, covering the Gartner Generative AI Landscape and practical implementation strategies for enterprise data querying.
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This article introduces Qwen3’s advanced text embedding and reranking models, highlighting their versatility and multilingual support. It provides a comprehensive guide for developers on leveraging Qwen3’s embedding and reranker capabilities for building high-quality retrieval and semantic search systems.
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This article introduces Alibaba Cloud’s Qwen2.5 Omni, an advanced multimodal AI model that integrates text, images, audio, and video processing for enhanced AI capabilities. In the Generative AI era, Large Language Models are no longer confined to text — multimodal models like Qwen2.5 Omni bridge the gap between different data modalities, enabling richer and more capable AI applications.
