forked from phoenix-oss/llama-stack-mirror
[rag evals] refactor & add ability to eval retrieval + generation in agentic eval pipeline (#664)
# What does this PR do? - See https://github.com/meta-llama/llama-stack/pull/666 & https://github.com/meta-llama/llama-stack/pull/668 - Refactor BaseScoringFn to be just a minimal interface, add new RegistrableBaseScoring - Refactor data schema check - To separately evaluate retrieval component in RAG, we will have scoring functions needing "context" column additionally. - Refactor braintrust eval (more scoring fn added & tested in following PR) ## Test Plan ``` pytest -v -s -m llm_as_judge_scoring_together_inference scoring/test_scoring.py --judge-model meta-llama/Llama-3.2-3B-Instruct pytest -v -s -m basic_scoring_together_inference scoring/test_scoring.py pytest -v -s -m braintrust_scoring_together_inference scoring/test_scoring.py ``` <img width="847" alt="image" src="https://github.com/user-attachments/assets/d099cb2d-6f9c-4bdf-9d0d-f388cf758c0f" /> ``` pytest -v -s -m meta_reference_eval_together_inference eval/test_eval.py pytest -v -s -m meta_reference_eval_together_inference_huggingface_datasetio eval/test_eval.py ``` <img width="850" alt="image" src="https://github.com/user-attachments/assets/dce28fc3-0493-4d34-820a-567260873cc8" /> ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Ran pre-commit to handle lint / formatting issues. - [ ] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md), Pull Request section? - [ ] Updated relevant documentation. - [ ] Wrote necessary unit or integration tests.
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24 changed files with 544 additions and 139 deletions
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@ -47,7 +47,7 @@ class Scoring(Protocol):
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async def score_batch(
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self,
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dataset_id: str,
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scoring_functions: Dict[str, Optional[ScoringFnParams]] = None,
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scoring_functions: Dict[str, Optional[ScoringFnParams]],
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save_results_dataset: bool = False,
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) -> ScoreBatchResponse: ...
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@ -55,5 +55,5 @@ class Scoring(Protocol):
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async def score(
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self,
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input_rows: List[Dict[str, Any]],
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scoring_functions: Dict[str, Optional[ScoringFnParams]] = None,
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scoring_functions: Dict[str, Optional[ScoringFnParams]],
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) -> ScoreResponse: ...
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