Retrieval-augmented text generation (RAG) addresses the common limitations of large language models (LLMs), such as hallucination, by retrieving information from an updatable external knowledge base. However, existing approaches often require dedicated backend servers for data storage and retrieval, thereby limiting their applicability in use cases that require strict data privacy, such as personal finance, education, and medicine. To address the pressing need for client-side dense retrieval, we introduce MeMemo, the first open-source JavaScript toolkit that adapts the state-of-the-art approximate nearest neighbor search technique HNSW to browser environments. Developed with modern and native Web technologies, such as IndexedDB and Web Workers, our toolkit leverages client-side hardware capabilities to enable researchers and developers to efficiently search through millions of high-dimensional vectors in the browser. MeMemo enables exciting new design and research opportunities, such as private and personalized content creation and interactive prototyping, as demonstrated in our example application RAG Playground. Reflecting on our work, we discuss the opportunities and challenges for on-device dense retrieval. MeMemo is available at https://github.com/poloclub/mememo.

通过在浏览器环境中适应最先进的近似最近邻搜索技术，我们介绍了MeMemo这个JavaScript工具包，使得研究人员和开发者能够在浏览器中高效地搜索数百万个高维向量。这种工具包提供了新的设计和研究机会，如个人和个性化内容创建以及交互式原型设计，并且具有数据隐私保护的能力。

MeMemo: 设备上的检索增强 用于私密和个性化文本生成

MeMemo: 设备上的检索增强用于私密和个性化文本生成