We benchmarked SQLite, ObjectBox, and ToStore for On-Device RAG in Flutter: 5-dimension results & architecture comparison
Flutter On-Device RAG and Vector Search Deep Dive: SQLite, ObjectBox, or ToStore? A 5-Dimension Benchmark and Selection Guide Executive Summary : With the explosive maturation of On-Device Small Language Models (SLMs), building offline private knowledge bases, intelligent code copilots, and local Retrieval-Augmented Generation (RAG) pipelines has become a primary architecture initiative for…
The article provides an in-depth comparison of three local database options (SQLite, ObjectBox, and ToStore) for implementing On-Device Retrieval-Augmented Generation (RAG) in Flutter applications. The analysis focuses on five dimensions: architectural model, dense vector ANN search, full-text search, neural sparse vector support, and multi-way hybrid fusion capabilities.
The evaluation reveals that SQLite lacks native support for dense vector ANN searches, requiring manual implementation that can lead to performance issues. ObjectBox offers a native HNSW index for ANN search but has limitations with full-text search under heavy concurrent loads. ToStore, on the other hand, provides native support for both dense vector ANN search and full-text search using BM25, along with a native multi-way hybrid fusion capability.
The authors emphasize that industrial-grade edge RAG requires a multi-way hybrid recall pipeline combining dense vectors, BM25 full-text search, and neural sparse vectors. They argue that while SQLite may be suitable for simple use cases, ObjectBox and ToStore emerge as better options for more complex on-device RAG applications due to their native support for advanced retrieval techniques.
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