DeepSeek R1 is a large language model that has sparked debates about AI control, market disruption, and national security. It was trained on 14.8 trillion tokens using datasets like CodeCorpus-30M, arXiv math papers, and multilingual web text, making it suitable for tasks requiring precise coding, mathematical reasoning, and structured problem-solving. The model has been released as an open-source model under the MIT license, allowing anyone to use, modify, and deploy it without restrictions. Its performance is evident across multiple benchmarks and applications, with strong capabilities in mathematical reasoning, coding and debugging, and structured logical reasoning. DeepSeek R1's technical performance and cost efficiency make it a good candidate for real-world Retrieval-Augmented Generation applications when paired with a capable vector database like Milvus. The model's open availability and low operational costs open new opportunities for innovation and customization, making it a serious alternative to expensive, proprietary models. Its integration with Milvus proves its worth in real-world applications, from customer support to knowledge management, and raises important questions about data security, regulation, and the balance of technological power on a global scale.