Vector Search (<=>)

The <=> operator computes the distance between a stored vector embedding and a query vector. It integrates HNSW vector search into Cypher queries.

Basic Usage

MATCH (chunk:Chunk)
WHERE chunk.embedding <=> $query < 0.5
RETURN chunk.text
ORDER BY chunk.embedding <=> $query
LIMIT 10

This finds chunks whose embedding is within distance 0.5 of the query vector, sorted by proximity.

How It Works

When the query planner encounters <=> in a WHERE clause, it converts the pattern into a specialized HNSW search operator rather than scanning all nodes. The distance threshold (e.g., < 0.5) filters results after the search.

Distance Metric

LatticeDB uses cosine distance (1 - cosine_similarity). Values range from 0 (identical) to 2 (opposite).

Ordering by Distance

Use ORDER BY to sort results by similarity:

MATCH (n:Document)
WHERE n.embedding <=> $query < 1.0
RETURN n.title
ORDER BY n.embedding <=> $query
LIMIT 10

Combining with Graph Traversal

The real power is combining vector search with graph patterns:

MATCH (chunk:Chunk)-[:PART_OF]->(doc:Document)-[:AUTHORED_BY]->(author:Person)
WHERE chunk.embedding <=> $query < 0.5
RETURN doc.title, chunk.text, author.name
ORDER BY chunk.embedding <=> $query
LIMIT 10

This finds similar chunks, then traverses to their documents and authors — all in one query.

Combining with Full-Text Search

MATCH (chunk:Chunk)-[:PART_OF]->(doc:Document)
WHERE chunk.embedding <=> $query < 0.5
  AND doc.content @@ "neural networks"
RETURN doc.title, chunk.text
ORDER BY chunk.embedding <=> $query

Parameter Binding

Pass the query vector as a parameter:

Python:

results = db.query(
    "MATCH (n) WHERE n.embedding <=> $q < 0.5 RETURN n",
    parameters={"q": query_vector}
)

TypeScript:

const results = await db.query(
  "MATCH (n) WHERE n.embedding <=> $q < 0.5 RETURN n",
  { q: new Float32Array([0.1, 0.2, ...]) }
);