MongoDB
Database platform company. Relevant to search via MongoDB Atlas Search — a full-text and vector search capability built on Apache Lucene, embedded inside Atlas (MongoDB’s managed cloud database).
Search Contributions
Semantic Boosting — a two-phase hybrid retrieval technique named and described by Erik Hatcher: run vector search first, inject results as boost clauses into a final lexical $search query. Preserves native faceting, highlighting, and pagination.
Atlas Search — Lucene-powered full-text search integrated directly into the MongoDB document model. Supports $search (lexical), $vectorSearch (dense ANN), and $rankFusion / $scoreFusion fusion operators.
Fusion aggregation stages
| Stage | Purpose |
|---|---|
$rankFusion | RRF over named input pipelines, with per-pipeline combination.weights |
$scoreFusion | RSF with input.normalization (none / sigmoid / minMaxScaler) and combination.method (avg or a custom expression) |
$score | Places a computed value into $meta.score for pipelines that don’t already produce one; optionally normalizes |
$meta: 'scoreDetails' | Exposes the full per-pipeline computation — rank or raw score, weight, and contribution — for explainability |
Because the 60 in the RRF denominator is a fixed built-in, a weight of 30 on each of two pipelines
conveniently rescales the fused score into a ~0.0–1.0 range. $vectorSearch scores already arrive
in 0.0–1.0, while lexical $search BM25 scores are unbounded — so Score Normalization
matters far more on the lexical leg. Worked through in
Reciprocal Rank Fusion and Relative Score Fusion.
People
Articles
The MongoDB Hybrid Search series by Erik Hatcher:
- Survey of the Hybrid Search Landscape
- Reciprocal Rank Fusion and Relative Score Fusion
- Hybrid Search Blueprint Series Semantic Boosting