Hybrid Search in SQLite: A Practical Blueprint Using Vector Similarity, FTS5, and RRF

Hybrid search is finally practical on SQLite. SQLite-RAG stitches vector similarity with FTS5 on SQLite, and uses Reciprocal Rank Fusion to rank results. ^[1]

What this is — A hybrid search engine built on top of SQLite that blends neural similarity with traditional text search. The approach lets you catch both conceptually relevant and textually exact documents, then harmonizes signals with Reciprocal Rank Fusion for a tighter result set. ^[1]

How the layers fit — A vector similarity layer complements FTS5 indexing to broaden recall beyond keyword matches. The fusion step combines vector and text results into a single ranking, improving document retrieval on the same database core. ^[1]

Step-by-step extension blueprint — (high level, as outlined in the discussion): • Add a vector similarity layer on top of the SQLite data using embeddings. ^[1] • Leverage FTS5 to index and search the textual content. ^[1] • Fuse rankings with Reciprocal Rank Fusion to produce a final ordering. ^[1] • Expose results through a single SQLite query path so apps stay on one platform. ^[1]

Why it matters — This on-database hybrid approach keeps data movement minimal while boosting retrieval quality. It signals a practical blueprint for embedding-vector + text search on the SQLite stack. ^[1]

Closing thought — Keep an eye on SQLite-RAG as vector math meets mature text search in 2025. ^[1]

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