* feat: return matching scores from jev
Replace the experimental three-argument Boolean function with jev(text, prompt) returning a Float64 probability in [0, 1]. Move threshold comparisons into SQL and update tests and migration examples.
Signed-off-by: Lei, HUANG <ratuthomm@gmail.com>
* feat: add Jev choice and score functions
Share asynchronous execution across Noul, Choice, and Score. Validate JSON criteria before requests and return typed scalar answers. Add SQL and HTTP mock coverage with usage examples.
Signed-off-by: Lei, HUANG <ratuthomm@gmail.com>
* refactor: use generic AI SQL function names
Expose ai_match, ai_choose, and ai_score and move their implementation, tests, and usage guide under generic AI names. Document the current unreleased interface without migration history.
Signed-off-by: Lei, HUANG <ratuthomm@gmail.com>
* fix: share constant AI criteria within each batch
Borrow scalar string arguments and lazily parse constant criteria once per batch. Share the parsed allocation across requests while preserving NULL propagation and batch validation before HTTP calls.
Signed-off-by: Lei, HUANG <ratuthomm@gmail.com>
* feat: preserve AI score uncertainty in JSONB results
Return score, confidence, and probabilities in criteria-level order from one evaluation. Validate the distribution and preserve provider precision. Add JSON extraction, uncertainty, and single-request regressions, and document confidence-aware ranking.
Signed-off-by: Lei, HUANG <ratuthomm@gmail.com>
* docs: explain reuse of volatile AI evaluations
Document repeated SELECT and WHERE evaluation costs as N + M requests, and show subquery aliases for reusing scalar or structured AI results without additional model calls.
Signed-off-by: Lei, HUANG <ratuthomm@gmail.com>
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Signed-off-by: Lei, HUANG <ratuthomm@gmail.com>
