euclidean_similarity

Generic Description

Turns Euclidean distance into a higher-is-better similarity score. It is documented separately because similarity orientation, vector dimensions, and set semantics directly affect ranking and interpretation.

Simple example:

RETURN euclidean_similarity(vector([1.0, 0.0]), vector([1.0, 0.0])) AS value

Consumer-Level Explanation

Use it when downstream ranking expects similarity orientation but the geometry is distance-based. Prefer this function when symbolic graph filters and similarity evidence should remain in one auditable Cypher pipeline.

More Detailed Explanation

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

MATCH (d:Document)
WITH d, euclidean_similarity(vector(properties(d).embedding), vector([0.22, 0.18, 0.44])) AS embedding_similarity
RETURN d.title AS document, embedding_similarity
ORDER BY embedding_similarity DESC

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