[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/inline/eval/meta_reference/eval.py

@@ -7,7 +7,7 @@ from typing import Any, Dict, List, Optional
 
 from tqdm import tqdm
 
-from llama_stack.apis.agents import Agents
+from llama_stack.apis.agents import Agents, StepType
 from llama_stack.apis.datasetio import DatasetIO
 from llama_stack.apis.datasets import Datasets
 from llama_stack.apis.eval_tasks import EvalTask
@@ -139,11 +139,21 @@ class MetaReferenceEvalImpl(Eval, EvalTasksProtocolPrivate, DataSchemaValidatorM
                 )
             ]
             final_event = turn_response[-1].event.payload
-            generations.append(
-                {
-                    ColumnName.generated_answer.value: final_event.turn.output_message.content
-                }
+
+            # check if there's a memory retrieval step and extract the context
+            memory_rag_context = None
+            for step in final_event.turn.steps:
+                if step.step_type == StepType.memory_retrieval.value:
+                    memory_rag_context = " ".join(x.text for x in step.inserted_context)
+
+            agent_generation = {}
+            agent_generation[ColumnName.generated_answer.value] = (
+                final_event.turn.output_message.content
             )
+            if memory_rag_context:
+                agent_generation[ColumnName.context.value] = memory_rag_context
+
+            generations.append(agent_generation)
 
         return generations