Hybrid retrieval just got practical with SQLite-RAG, a semantic search engine built on top of SQLite. It blends vector similarity search with the FTS5 extension and uses Reciprocal Rank Fusion (RRF) to lift document retrieval. The result is a single engine that can search both structured text and semantic content [1].
What it is — A hybrid search stack sits on SQLite, mixing vector similarity with the FTS5 full-text engine. The trick is Reciprocal Rank Fusion (RRF), a way to blend results from both worlds for sharper recall [1].
Where it shines — In practical terms, this setup is tailor-made for several local-first workflows:
• Offline knowledge bases — index once and search locally, keeping sensitive data on-device [1]. • Embedded document search in mobile apps — fast local lookup without cloud round-trips [1]. • Local knowledge management — personal or team docs become easily searchable with hybrid ranking [1].
This is a neat proof of concept for SQLite extensions, showing how vector search and FTS can live side by side in a local data store. Expect more SQLite extensions to explore hybrid retrieval in the coming months [1].
Watch how developers blend Reciprocal Rank Fusion (RRF) with the FTS5 engine in on-device apps to keep data fast and private.
References
Hybrid search on SQLite using vector similarity and FTS5, via RRF, built on top of SQLite for enhanced document retrieval.
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