Benchmarking Hybrid Retrieval: How Vector Embeddings + FTS5 on SQLite Could Score vs Traditional FTS

Meet SQLite-RAG: a hybrid search engine built on top of SQLite that fuses vector similarity with the FTS5 extension, using Reciprocal Rank Fusion to boost document retrieval ^[1].

The hybrid core blends semantically rich vectors with exact-text matching, then reranks with RR F to surface the most relevant docs. This approach invites clear benchmarking against traditional FTS in SQLite to see when the vector side pays off ^[1].

Why run benchmarks? Because embedding-aware search can trade off latency for quality, and understanding that balance helps you pick the right setup for your app.

Experiment ideas: • Embedding dimensions - explore how different dims affect latency and recall ^[1] • RRF thresholds - adjust ranking fusion settings to see impact on results ^[1] • Workloads - simulate real-world data: dataset size, doc length, and query styles ^[1] • Metrics - track latency, retrieval quality, and overall payoff to decide when the hybrid wins ^[1]

Keep an eye on how hybrid retrieval evolves in the SQLite ecosystem; it could tilt the scales for when to lean on vectors versus traditional FTS in practice ^[1].

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