Returns 0.0-1.0 relevance score for text vs criterion (cross-encoder)
Per-row — runs once for each row.
{{ text }} ABOUT {{ criterion }}{{ text }} RELEVANCE TO {{ criterion }}| name | type | description |
|---|---|---|
| text | VARCHAR | — |
| criterion | VARCHAR | — |
Function: High quality score
SELECT
semantic_score (
'The food was excellent, service was great',
'quality'
)Function: unrelated text gets a low quality relevance score
SELECT
semantic_score (
'The meeting starts at noon in conference room B',
'quality'
)Function: High service relevance score
SELECT
semantic_score (
'Great customer support and fast delivery',
'service'
)Function: tech relevance score
SELECT
semantic_score (
'Machine learning and AI developments',
'technology'
)LLM-backed 0.0-1.0 relevance score (escape hatch for ABOUT/RELEVANCE TO)
Pick the single best value from a group by a plain-English quality criterion
Find unusual or atypical items via embeddings (+ optional criteria)
LLM-backed outlier detection (escape hatch for OUTLIERS)
PageRank centrality on an ad-hoc edge list (NetworkX)
Rank a group of items by a subjective multi-factor criterion
Returns 0.0-1.0 relevance score for text vs criterion (cross-encoder)
Per-row — runs once for each row.
{{ text }} ABOUT {{ criterion }}{{ text }} RELEVANCE TO {{ criterion }}| name | type | description |
|---|---|---|
| text | VARCHAR | — |
| criterion | VARCHAR | — |
Function: High quality score
SELECT
semantic_score (
'The food was excellent, service was great',
'quality'
)Function: unrelated text gets a low quality relevance score
SELECT
semantic_score (
'The meeting starts at noon in conference room B',
'quality'
)Function: High service relevance score
SELECT
semantic_score (
'Great customer support and fast delivery',
'service'
)Function: tech relevance score
SELECT
semantic_score (
'Machine learning and AI developments',
'technology'
)LLM-backed 0.0-1.0 relevance score (escape hatch for ABOUT/RELEVANCE TO)
Pick the single best value from a group by a plain-English quality criterion
Find unusual or atypical items via embeddings (+ optional criteria)
LLM-backed outlier detection (escape hatch for OUTLIERS)
PageRank centrality on an ad-hoc edge list (NetworkX)
Rank a group of items by a subjective multi-factor criterion