Hybrid Search
Keyword search nails exact terms; semantic search understands meaning. Neither wins alone. Hybrid search runs both and fuses the results — drag the weight slider and watch the ranking shift in real time.
⚖️ Why neither method alone is enough
Ask for “affordable sneakers for playing basketball” — keyword search misses the doc that says “basketball shoe” (no word overlap), while semantic search may fumble an exact code like ERR-4032. Hybrid runs both and merges them, so exact terms and meaning both count. Slide the weight to see each engine's strength.
Free uses this fixed corpus. Sign in for 3 demo searches/day; Pro beta members can search and compare their own documents.
Score fusion
Four ways to combine two rankings
Arithmetic mean
Equal weight to both scores. Simple, but sensitive to score-scale mismatch between engines.
Weighted sum
Tunable, e.g. 0.6 semantic + 0.4 keyword. Needs normalized scores to be meaningful.
Harmonic mean
Penalizes large discrepancies — a doc must do reasonably well on both to rank high.
Reciprocal Rank Fusion
Uses ranks, not raw scores, so score scales never clash. This lab uses RRF: Σ weight / (60 + rank + 1).
Why RRF here: keyword (BM25) and cosine scores live on totally different scales, so adding them directly lets one engine dominate. RRF throws away the raw scores and fuses the ranks instead — robust and parameter-light. (Also the approach Amazon OpenSearch Serverless recommends, since it doesn't support script_score.)
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