Browser-based language models often use retrieval-augmented generation (RAG) but typically rely on fixed, outdated indices that give users no control over which sources are consulted. This can lead to answers that mix trusted and untrusted content or draw on stale information. We present OwlerLite, a browser-based RAG system that makes user-defined scopes and data freshness central to retrieval. Users define reusable scopes-sets of web pages or sources-and select them when querying. A freshness-aware crawler monitors live pages, uses a semantic change detector to identify meaningful updates, and selectively re-indexes changed content. OwlerLite integrates text relevance, scope choice, and recency into a unified retrieval model. Implemented as a browser extension, it represents a step toward more controllable and trustworthy web assistants.

翻译：基于浏览器的语言模型常采用检索增强生成（RAG），但通常依赖固定且过时的索引，用户无法控制检索哪些来源。这可能导致答案混杂可信与不可信内容，或依赖陈旧信息。本文提出OwlerLite，一种基于浏览器的RAG系统，其检索核心为用户自定义的范围与数据新鲜度。用户可定义可复用的范围（即网页或来源集合），并在查询时进行选择。系统通过新鲜度感知爬虫实时监控动态页面，利用语义变化检测器识别有意义的更新，并选择性重新索引已变更内容。OwlerLite将文本相关性、范围选择与时效性整合为统一的检索模型。该系统以浏览器扩展形式实现，标志着向更具可控性与可信度的网络助手迈进一步。