---
title: manhattan_distance
url: https://nexyron.tech/docs/cypher/functions/vector-and-similarity/manhattan-distance
description: Sums absolute per dimension differences between equal length vectors. It is documented separately because similarity orientation, vector dimensions, and set semantics directly affe
---

# manhattan_distance

## Generic Description

Sums absolute per-dimension differences 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 manhattan_distance(vector([1.0, 2.0]), vector([2.0, 4.0])) AS value
```

## Consumer-Level Explanation

Use it when each dimension contributes linearly to distance. Prefer this function when symbolic graph filters and similarity evidence should remain in one auditable Cypher pipeline.

## More Detailed Explanation

`manhattan_distance` 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 `manhattan_distance` after symbolic graph filtering, which keeps hybrid retrieval constraints and similarity scoring in one visible pipeline.

```cypher
MATCH (d:Document)
WITH d, manhattan_distance(vector(properties(d).embedding), vector([0.22, 0.18, 0.44])) AS embedding_distance
RETURN d.title AS document, embedding_distance
ORDER BY embedding_distance ASC
```

## 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