数据库是GenAI高效运行的关键，决定生成内容的准确与智能程度。
Role of Databases in GenAI Applications
- 按对话、场景、语义三类需求匹配不同数据库，提升上下文理解
- 向量数据库支持语义搜索，显著提升信息检索效率
- 多数据库协同可实现个性化、高并发的AI应用
生成式人工智能（GenAI）正推动各行业在内容生成、自动化和决策方面的变革。然而，GenAI应用的效果高度依赖于数据的存储、检索与上下文增强效率。本文探讨数据库在GenAI工作流中的核心作用，强调选择合适的数据库架构对性能、准确性和可扩展性的重要性。论文将数据库角色分为三类：用于对话上下文的键值/文档数据库、用于情境上下文的关系型数据库与数据湖仓，以及用于语义上下文的向量数据库，各自承担不同功能以丰富AI生成结果。此外，文章还强调实时查询处理、向量搜索在语义检索中的应用，以及数据库选型对模型效率和扩展性的关键影响。通过采用多数据库协同策略，GenAI应用可实现更具备上下文感知、个性化的高性能解决方案。
原文摘要 · Abstract (English)
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.
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