examples: add RAIL Score responsible AI evaluation example#2591
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SumitVermakgp wants to merge 1 commit intoconfident-ai:mainfrom
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examples: add RAIL Score responsible AI evaluation example#2591SumitVermakgp wants to merge 1 commit intoconfident-ai:mainfrom
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Add a custom RAILScoreMetric(BaseMetric) that evaluates LLM outputs across 8 responsible AI dimensions (fairness, safety, reliability, transparency, privacy, accountability, inclusivity, user_impact) using the RAIL Score API. Includes a complete example script showing basic, batch, deep mode, and selective dimension evaluation patterns.
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Summary
Add a complete example showing how to use RAIL Score as a custom DeepEval metric for responsible AI evaluation across 8 dimensions (fairness, safety, reliability, transparency, privacy, accountability, inclusivity, user_impact).
Changes
examples/rail-score-evaluation/rail_score_metric.py--RAILScoreMetric(BaseMetric)class with sync/async support,score_breakdownfor per-dimension scoresexamples/rail-score-evaluation/example_evaluation.py-- Complete example showing basic, batch, deep mode, and selective dimension evaluationexamples/rail-score-evaluation/requirements.txt-- Dependenciesexamples/rail-score-evaluation/README.md-- Setup and usage documentationHow it works
The metric calls the RAIL Score API via rail-score-sdk, normalizes scores from 0-10 to 0-1, and populates
score_breakdownwith all 8 dimension scores. Supports bothbasic(fast) anddeep(with per-dimension explanations) modes, plus domain-specific evaluation contexts.Testing
evaluate(),measure(), anda_measure()against the live RAIL Score APIscore_breakdownblackRelated