Stop the Low Memory Killer: Mastering Memory-Efficient RAG on Android with Gemini Nano

The dream of on-device Generative AI is finally a reality. With the release of Gemini Nano and Google’s AICore, Android developers can now build applications that summarize text, suggest smart replies, and answer complex queries without ever sending data to a cloud server. But as the saying goes, "With great power comes great memory pressure." When you move from a basic LLM implementation to a Retrieval-Augmented Generation (RAG) architecture, you aren't just running a model; you are managing a complex pipeline of embeddings, vector databases, and dynamic context windows.