S Scour

Embeddable hybrid-search primitives in zero-dependency Rust — BM25 keyword search, an HNSW vector index, and reciprocal-rank fusion. Type a query and watch all three lanes rank the same corpus, so you can see why hybrid wins.

Live, in-process, no model server: the vector lane uses a deterministic feature-hash embedding computed in pure Rust. · Source on GitHub

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BM25 keyword exact term match

Inverted index + Okapi BM25. Precise when your words match the document's words — brittle when they don't.

Run a search to see results.

Vector (HNSW) meaning match

Approximate nearest neighbors over embeddings, cosine distance. Recovers paraphrases and synonyms the keyword lane misses.

Run a search to see results.

Hybrid (RRF) best of both

Reciprocal-rank fusion merges the two rankings with no score calibration. Badges show which legs found each hit.

Run a search to see results.

How to read this: the same query runs through three independent retrievers. BM25 ranks by keyword overlap; the vector lane ranks by embedding similarity; the hybrid lane fuses both with RRF (1 / (k + rank), k=60). Watch a query like “light from the early universe” — the keyword lane struggles, the vector lane finds the astronomy docs, and the hybrid lane keeps the win. · All retrieval is pure std Rust, indexed in-process at startup.