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:

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.

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