---
title: cosine_similarity
url: https://nexyron.tech/docs/cypher/functions/vector-and-similarity/cosine-similarity
description: Measures directional similarity between equal length vectors. It is documented separately because similarity orientation, vector dimensions, and set semantics directly affect ranki
---

# cosine_similarity

## Generic Description

Measures directional similarity between equal-length vectors. It is documented separately because similarity orientation, vector dimensions, and set semantics directly affect ranking and interpretation.

Simple example:

```cypher
RETURN cosine_similarity(vector([1.0, 0.0]), vector([1.0, 0.0])) AS value
```

## Consumer-Level Explanation

Use it for semantic ranking where vector angle matters more than vector magnitude. Prefer this function when symbolic graph filters and similarity evidence should remain in one auditable Cypher pipeline.

## More Detailed Explanation

`cosine_similarity` keeps similarity scoring in the same Cypher pipeline as symbolic graph filters and document metadata checks. That lets a query combine labels, relationships, time filters, and embedding or set overlap scores without asking a client to reconcile separate result sets.

## Advanced Example

This example applies `cosine_similarity` after symbolic graph filtering, which keeps hybrid retrieval constraints and similarity scoring in one visible pipeline.

```cypher
MATCH (u:User)-[e:VIEWED]->(d:Document)
TIME e.ts BETWEEN datetime('2025-01-01T00:00:00Z') AND datetime('2025-02-01T00:00:00Z')
WITH d, cosine_similarity(vector(properties(d).embedding), vector([0.22, 0.18, 0.44])) AS semantic_score
RETURN d.title AS document, semantic_score
ORDER BY semantic_score DESC
LIMIT 10
```

## Real Use Cases

-   semantic ranking over embedded documents after graph/time filtering
-   link-prediction features based on shared neighbors or metadata overlap
-   hybrid retrieval where symbolic constraints and vector scores are both visible

## Real Limitations And Tradeoffs

-   vector functions require compatible dimensions and meaningful embedding spaces
-   set-similarity functions treat lists as sets and may ignore duplicate frequency
-   a high similarity score is evidence, not proof of domain equivalence