Generative AI (GenAI) is transforming industries by enabling intelligent content generation, automation, and decision-making. However, the effectiveness of GenAI applications depends significantly on efficient data storage, retrieval, and contextual augmentation. This paper explores the critical role of databases in GenAI workflows, emphasizing the importance of choosing the right database architecture to optimize performance, accuracy, and scalability. It categorizes database roles into conversational context (key-value/document databases), situational context (relational databases/data lakehouses), and semantic context (vector databases) each serving a distinct function in enriching AI-generated responses. Additionally, the paper highlights real-time query processing, vector search for semantic retrieval, and the impact of database selection on model efficiency and scalability. By leveraging a multi-database approach, GenAI applications can achieve more context-aware, personalized, and high-performing AI-driven solutions.

翻译：生成式人工智能（GenAI）正通过实现智能内容生成、自动化与决策制定，变革各行各业。然而，GenAI应用的有效性在很大程度上依赖于高效的数据存储、检索与上下文增强。本文探讨了数据库在GenAI工作流中的关键作用，强调了选择合适的数据库架构对于优化性能、准确性与可扩展性的重要性。文章将数据库的角色分为对话上下文（键值/文档数据库）、情境上下文（关系数据库/数据湖仓）和语义上下文（向量数据库），每种类型在丰富AI生成响应的过程中发挥着独特功能。此外，本文重点讨论了实时查询处理、用于语义检索的向量搜索，以及数据库选择对模型效率与可扩展性的影响。通过采用多数据库策略，GenAI应用能够实现更具上下文感知能力、个性化且高性能的AI驱动解决方案。