he Oracle AI project introduces a sophisticated, multi-modal interaction framework designed to bridge the gap between static document repositories and dynamic user inquiry. By leveraging a Hybrid Retrieval-Augmented Generation (RAG) architecture, Oracle combines edge-based embedding generation with cloud-scale large language models (LLMs) to provide real-time, context-aware responses via both text and low-latency voice interfaces. The system utilizes local vector indexing to ensure data privacy and performance, while the integration of Gemini 2.0/2.5 models enables nuanced reasoning over complex document structures. This documentation outlines the technical foundations, the underlying analogy of the system as a digital sage, and the transformative impact of voice-driven document intelligence on professional workflows.
Kasturiwala et al. (Fri,) studied this question.