On-device information retrieval is a technique that enables search, context-awareness and retrieval-augmented generation (RAG) to run entirely on user devices — mobile, desktop or edge devices — prioritizing privacy and computational efficiency. It combines a lightweight local database with a model optimized for on-device inference. A promising implementation pairs sqlite-vec, a SQLite extension that supports vector search within the embedded database, with EmbeddingGemma, a 300 million parameter embedding model built on the Gemma 3 architecture. Optimized for efficiency and resource-constrained environments, this combination keeps data close to the edge, reducing dependence on cloud APIs and improving latency and privacy. We recommend teams assess this technique for local-first applications and other use cases where data sovereignty, low-latency and privacy are critical.
