The Hybrid Search post drops a tasty idea: run vector similarity and full-text search inside SQLite and fuse their results with Reciprocal Rank Fusion. It’s a clean path to smarter document retrieval on the same engine [1].
What to fuse - Use FTS5 results together with vector-derived scores. The post shows this hybrid approach as the core idea to lift relevance beyond either method alone [1]. - Treat each result as a candidate with two scores, then blend them for a single ranking that captures both textual signals and semantic similarity [1].
Where to insert ranking logic - Inside a SQLite extension, after you’ve computed both the FTS5 and vector scores. The natural spot is a ranking step that combines signals before the final sort [1]. - Keep the fusion logic close to the query planner so you don’t pay extra round trips to the app layer [1].
API surface in a SQLite extension - Expose a small surface (for example, a UDF or table-valued function) that returns RRF-ranked results for a given user query. This makes the hybrid rank reusable in plain SQL [1]. - Consider a virtual table or a dedicated ranking API that streams top-N docs ordered by the RRF score, so apps can page results efficiently [1]. - Tie the API to query parameters (k, weight for vector vs. text, etc.) to tune the blend at runtime [1].
Closing thought: if you’re building on SQLite today, this RRF-backed fusion is a concrete path to richer search without leaving the database boundary [1].
References
Hybrid search on SQLite using vector similarity and FTS5, via RRF, built on top of SQLite for enhanced document retrieval.
View source