omnigraph/docs/user/search/index.md

Search

OmniGraph runs vector, full-text, and hybrid search in the same runtime as graph traversal — a single query can combine a vector nearest, a bm25 text score, and an Expand traversal. Search functions are used inside match (to filter), or as expressions inside return / order (to score and rank).

Functions

Function	Purpose	Backing index
nearest($x.vec, $q)	k-NN vector search (cosine)	vector index (IVF / HNSW)
search(field, q)	Generic full-text search	inverted (FTS) index
fuzzy(field, q [, max_edits])	Levenshtein-tolerant text search	inverted index
match_text(field, q)	Pattern match	inverted index
bm25(field, q)	BM25 relevance scoring	inverted index
rrf(rank_a, rank_b [, k])	Reciprocal Rank Fusion of two rankings (default k=60)	fuses scored rankings

• nearest() requires a limit. The query vector is resolved from the param map, or embedded from a text input at runtime via the configured embedding client.
• Match filters apply before the search: combining a match predicate with nearest() (or bm25()) returns the top-limit of the matching rows — never a post-filtered remainder of the global top-k. A selective filter narrows the candidate set; it cannot starve the result count.
• Scores and ranks propagate as ordinary columns, so you can return a score and order by it.

Hybrid ranking with rrf

Reciprocal Rank Fusion combines two independent rankings (typically one vector and one text) into a single fused ranking, without needing the two score scales to be comparable. Rank each retrieval separately, then fuse:

query hybrid($q: String) {
  match { $d: Document { } }
  return {
    $d,
    rrf( nearest($d.embedding, $q), bm25($d.body, $q) ) as score
  }
  order { score desc }
  limit 10
}

Indexes and embeddings

Search functions only work when the backing index exists — see indexes for building vector and inverted indexes, and embeddings for generating the vectors nearest searches over.