Vector Search

Uni provides native vector search over embedding properties with ANN indexes (HNSW, IVF_PQ, Flat). Use it for semantic search, RAG, and similarity-based retrieval.

What It Provides

• Vector properties stored alongside graph data.
• ANN indexes with cosine, L2, or dot distance — scores are automatically converted to [0, 1] similarity regardless of metric.
• CALL uni.vector.query(...) for KNN retrieval.
• similar_to() expression function for point-scoring bound nodes in WHERE, RETURN, and Locy rules.
• Auto-embedding: Pass text directly and let Uni embed it using the index's configured embedding model.

Example

RustPython

use uni_db::{DataType, IndexType, Uni, VectorAlgo, VectorIndexCfg, VectorMetric};

# async fn demo() -> Result<(), uni_db::UniError> {
let db = Uni::open("./my_db").build().await?;

db.schema()
    .label("Document")
        .property("title", DataType::String)
        .property("embedding", DataType::Vector { dimensions: 384 })
        .index("embedding", IndexType::Vector(VectorIndexCfg {
            algorithm: VectorAlgo::Hnsw { m: 16, ef_construction: 200 },
            metric: VectorMetric::Cosine,
            embedding: None,  // Or configure auto-embed
        }))
    .apply()
    .await?;

let rows = db.query_with(
    "CALL uni.vector.query('Document', 'embedding', $q, 10) YIELD node, score RETURN node, score"
)
.param("q", vec![0.1_f32, 0.2, 0.3])
.fetch_all()
.await?;

println!("{:?}", rows);
# Ok(())
# }

import uni_db

db = uni_db.Database("./my_db")

db.schema() \
    .label("Document") \
        .property("title", "string") \
        .vector("embedding", 384) \
        .index("embedding", "vector") \
        .done() \
    .apply()

rows = db.query(
    "CALL uni.vector.query('Document', 'embedding', $q, 10) YIELD node, score RETURN node, score",
    {"q": [0.1, 0.2, 0.3]}
)
print(rows)

Auto-Embedding Queries

With an embedding configuration on your index, you can query with text directly:

-- Create index with embedding config
CREATE VECTOR INDEX doc_embed FOR (d:Document) ON (d.embedding)
OPTIONS {
    metric: 'cosine',
    embedding: {
        provider: 'Candle',
        model: 'all-MiniLM-L6-v2',
        source: ['content']
    }
}

-- Query with text - Uni auto-embeds it
CALL uni.vector.query('Document', 'embedding', 'machine learning tutorial', 10)
YIELD node, score
RETURN node.title, score

Expression-Based Scoring: similar_to

For scoring already-bound nodes rather than top-K retrieval, use similar_to():

MATCH (a:Paper)-[:CITES]->(b:Paper)
WHERE similar_to(b.embedding, 'attention mechanisms') > 0.7
RETURN b.title, similar_to(b.embedding, 'attention mechanisms') AS score

similar_to supports vector similarity, FTS scoring, and multi-source hybrid fusion. It works in WHERE, RETURN, ORDER BY, and Locy rule bodies. See the Vector Search guide for full details.

Use Cases

• Semantic search for documents or products.
• RAG retrieval over knowledge graphs.
• Similarity search over embeddings generated in-app.
• Scoring graph-traversed nodes with similar_to() in WHERE and Locy rules.

When To Use

Choose vector search when you need semantic similarity rather than exact matching. Pair it with graph traversal for contextual results.

• Use CALL uni.vector.query(...) to find top-K candidates from a full label.
• Use similar_to() to score nodes already bound by MATCH.

See also: Full-Text Search | Hybrid Search