Cross-Engine Fusion (RRF)

Reciprocal Rank Fusion merges ranked results from different engines — vector similarity, BM25 text ranking, graph hop distance — into a single result set. No application-level merging needed.

How RRF Works

For each result, compute: score = Σ 1 / (k + rank_i) where k is a constant (default 60) and rank_i is the result's rank from each source.

Vector + Full-Text

SELECT title, rrf_score(
    vector_distance(embedding, $query_vec),
    bm25_score(body, 'distributed database')
) AS score
FROM articles
LIMIT 10;

GraphRAG (Vector + Graph)

GRAPH RAG FUSION ON entities
  QUERY $embedding VECTOR_TOP_K 50 EXPANSION_DEPTH 2
  EDGE_LABEL 'related_to' FINAL_TOP_K 10 RRF_K (60.0, 35.0);

Vector search finds seed nodes, graph BFS expands context, RRF merges both rankings.

Cross-Model Queries

All engines share the same snapshot. A query that combines vector similarity, graph traversal, spatial filtering, and document field access sees a consistent point-in-time view.

-- Spatial filter → vector rank → document fields
SELECT name, vector_distance(embedding, $vec) AS sim
FROM restaurants
WHERE ST_DWithin(location, ST_Point(-73.99, 40.75), 2000)
  AND embedding <-> $vec
LIMIT 10;