[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.

llama_stack/providers/utils/scoring/base_scoring_fn.py

@@ -13,12 +13,51 @@ from llama_stack.providers.utils.scoring.aggregation_utils import aggregate_metr
 
 class BaseScoringFn(ABC):
     """
-    Base interface class for all native scoring_fns.
-    Each scoring_fn needs to implement the following methods:
+    Base interface class for Scoring Functions.
+    Each scoring function needs to implement the following methods:
     - score_row(self, row)
     - aggregate(self, scoring_fn_results)
     """
 
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+    def __str__(self) -> str:
+        return self.__class__.__name__
+
+    @abstractmethod
+    async def score_row(
+        self,
+        input_row: Dict[str, Any],
+        scoring_fn_identifier: Optional[str] = None,
+        scoring_params: Optional[ScoringFnParams] = None,
+    ) -> ScoringResultRow:
+        raise NotImplementedError()
+
+    @abstractmethod
+    async def aggregate(
+        self,
+        scoring_results: List[ScoringResultRow],
+        scoring_fn_identifier: Optional[str] = None,
+        scoring_params: Optional[ScoringFnParams] = None,
+    ) -> Dict[str, Any]:
+        raise NotImplementedError()
+
+    @abstractmethod
+    async def score(
+        self,
+        input_rows: List[Dict[str, Any]],
+        scoring_fn_identifier: Optional[str] = None,
+        scoring_params: Optional[ScoringFnParams] = None,
+    ) -> List[ScoringResultRow]:
+        raise NotImplementedError()
+
+
+class RegisteredBaseScoringFn(BaseScoringFn):
+    """
+    Interface for native scoring functions that are registered in LlamaStack.
+    """
+
     def __init__(self, *args, **kwargs) -> None:
         super().__init__(*args, **kwargs)
         self.supported_fn_defs_registry = {}