Search

Full-text, fuzzy, vector, and hybrid search are first-class query expressions in Omnigraph.

Omnigraph has six built-in search modes. They are not separate services — they are expressions you use directly inside .gq queries, alongside traversal, filtering, and projection.

The six search modes

Expression	Mode	Where it appears	What it does
`search(field, term)`	Full-text	`match`	Filters to nodes whose field matches the search term via an inverted index
`fuzzy(field, term, distance)`	Fuzzy	`match`	Filters with edit-distance tolerance for typos and near-matches
`match_text(field, phrase)`	Text match	`match`	Filters with the current full-text backend; do not treat it as strict phrase adjacency
`bm25(field, term)`	BM25 ranking	`order`	Ranks results by BM25 relevance score
`nearest(field, vector)`	Vector KNN	`order`	Ranks results by nearest-neighbor distance over an indexed vector field; requires `limit`
`rrf(nearest(...), bm25(...))`	Hybrid	`order`	Combines two ranking signals with Reciprocal Rank Fusion; requires `limit`

Where search expressions appear

Search expressions are used in two places inside a query:

match block — search(), fuzzy(), and match_text() act as filters. They restrict which nodes are returned.
order block — bm25(), nearest(), and rrf() act as ranking functions. They sort results by relevance.

A query can combine both: filter in match, then rank in order.

query find_experts($term: String, $vec: Vector(1536)) {
    match {
        $p: Person
        search($p.bio, $term)
    }
    return { $p.name, $p.bio }
    order { rrf(nearest($p.embedding, $vec), bm25($p.bio, $term)) desc }
    limit 10
}

Index requirements

Search expressions require indexes on the fields they operate on:

Search type	Schema annotation	Index created
Full-text, fuzzy, text match, BM25	`@index` on a `String` field	Inverted index
Vector KNN	`@index` on a `Vector(N)` field	`ivf_flat` vector index with `L2` metric

Without the right index, the query compiler rejects the query at compile time.

node Person {
    name: String @key
    bio: String @index
    embedding: Vector(1536) @index
}

Combining search with traversal

Search expressions compose with the rest of the query language. You can filter by search, traverse edges, and project properties all in one query:

query experts_at_company($term: String) {
    match {
        $p: Person
        search($p.bio, $term)
        $p worksAt $c: Company { name: "Acme" }
    }
    return { $p.name, $c.name }
    order { bm25($p.bio, $term) desc }
}

Next steps

Full-text Search — keyword search, fuzzy matching, text matching, and BM25 ranking.
Vector Search — nearest-neighbor search over embeddings.
Hybrid Search — combine BM25 and vector ranking with Reciprocal Rank Fusion.