docs: update ResponseGroundedness metric documentation to Collections API

- Added new primary example using collections-based API with ResponseGroundedness
- Added synchronous usage note with .score() method
- Moved legacy SingleTurnSample example to Legacy Metrics API section
- Tested new example and verified it produces expected output (score: 1.0)

Author: sanjeed5

docs/concepts/metrics/available_metrics/nvidia_metrics.md

@@ -248,28 +248,47 @@ Output:
 - **1** → The response is partially grounded.
 - **2** → The response is fully grounded (every statement can be found or inferred from the retrieved context).
 
+### Example
 
 ```python
-from ragas.dataset_schema import SingleTurnSample
-from ragas.metrics import ResponseGroundedness
+from openai import AsyncOpenAI
+from ragas.llms import llm_factory
+from ragas.metrics.collections import ResponseGroundedness
 
-sample = SingleTurnSample(
+# Setup LLM
+client = AsyncOpenAI()
+llm = llm_factory("gpt-4o-mini", client=client)
+
+# Create metric
+scorer = ResponseGroundedness(llm=llm)
+
+# Evaluate
+result = await scorer.ascore(
     response="Albert Einstein was born in 1879.",
     retrieved_contexts=[
         "Albert Einstein was born March 14, 1879.",
         "Albert Einstein was born at Ulm, in Württemberg, Germany.",
     ]
 )
-
-scorer = ResponseGroundedness(llm=evaluator_llm)
-score = await scorer.single_turn_ascore(sample)
-print(score)
+print(f"Response Groundedness Score: {result.value}")
 ```
-Output
+
+Output:
+
 ```
-1.0
+Response Groundedness Score: 1.0
 ```
 
+!!! note "Synchronous Usage"
+    If you prefer synchronous code, you can use the `.score()` method instead of `.ascore()`:
+    
+    ```python
+    result = scorer.score(
+        response="Albert Einstein was born in 1879.",
+        retrieved_contexts=[...]
+    )
+    ```
+
 ### How It’s Calculated
 
 **Step 1:** The LLM is prompted with two distinct templates to evaluate the grounding of the response with respect to the retrieved contexts. Each prompt returns a grounding rating of **0**, **1**, or **2**.
@@ -299,3 +318,35 @@ In this example, the retrieved contexts provide both the birthdate and location
 - **Token Usage:** Faithfulness consumes more tokens, whereas Response Groundedness is more token-efficient.
 - **Explainability:** Faithfulness provides transparent, reasoning for each claim, while Response Groundedness provides a raw score.
 - **Robust Evaluation:** Faithfulness incorporates user input for a comprehensive assessment, whereas Response Groundedness ensures consistency through dual LLM evaluations.
+
+### Legacy Metrics API
+
+The following examples use the legacy metrics API pattern. For new projects, we recommend using the collections-based API shown above.
+
+!!! warning "Deprecation Timeline"
+    This API will be deprecated in version 0.4 and removed in version 1.0. Please migrate to the collections-based API shown above.
+
+#### Example with SingleTurnSample
+
+```python
+from ragas.dataset_schema import SingleTurnSample
+from ragas.metrics import ResponseGroundedness
+
+sample = SingleTurnSample(
+    response="Albert Einstein was born in 1879.",
+    retrieved_contexts=[
+        "Albert Einstein was born March 14, 1879.",
+        "Albert Einstein was born at Ulm, in Württemberg, Germany.",
+    ]
+)
+
+scorer = ResponseGroundedness(llm=evaluator_llm)
+score = await scorer.single_turn_ascore(sample)
+print(score)
+```
+
+Output:
+
+```
+1.0
+```