[rag evals][2/n] add more braintrust scoring fns for RAG eval (#666)

# What does this PR do?

- add more braintrust scoring functions for RAG eval
- add tests for evaluating against context

## Test Plan

```
pytest -v -s -m braintrust_scoring_together_inference scoring/test_scoring.py
```
<img width="850" alt="image"
src="https://github.com/user-attachments/assets/2f8f0693-ea13-422c-a183-f798faf86433"
/>


**Example Output**
- https://gist.github.com/yanxi0830/2acf3b8b3e8132fda2a48b1f0a49711b

<img width="827" alt="image"
src="https://github.com/user-attachments/assets/9014b957-107c-4c23-bbc0-812cbd0b16da"
/>

<img width="436" alt="image"
src="https://github.com/user-attachments/assets/21e9da17-f426-49b2-9113-855cab7b3d40"
/>




## Sources

Please link relevant resources if necessary.


## 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/tests/datasetio/test_datasetio.py

@@ -38,9 +38,15 @@ def data_url_from_file(file_path: str) -> str:
 
 
 async def register_dataset(
-    datasets_impl: Datasets, for_generation=False, dataset_id="test_dataset"
+    datasets_impl: Datasets,
+    for_generation=False,
+    for_rag=False,
+    dataset_id="test_dataset",
 ):
-    test_file = Path(os.path.abspath(__file__)).parent / "test_dataset.csv"
+    if for_rag:
+        test_file = Path(os.path.abspath(__file__)).parent / "test_rag_dataset.csv"
+    else:
+        test_file = Path(os.path.abspath(__file__)).parent / "test_dataset.csv"
     test_url = data_url_from_file(str(test_file))
 
     if for_generation:
@@ -49,6 +55,13 @@ async def register_dataset(
             "input_query": StringType(),
             "chat_completion_input": ChatCompletionInputType(),
         }
+    elif for_rag:
+        dataset_schema = {
+            "expected_answer": StringType(),
+            "input_query": StringType(),
+            "generated_answer": StringType(),
+            "context": StringType(),
+        }
     else:
         dataset_schema = {
             "expected_answer": StringType(),