docs: Add docs for new entity based metric

docs/concepts/metrics/context_entities_recall.md

@@ -0,0 +1,35 @@
+# Context entities recall
+
+This metric gives the measure of recall of the retrieved context, based on the number of entities present in both `ground_truths` and `contexts` relative to the number of entities present in the `ground_truths` alone. Simply put, it is a measure of what fraction of entities are recalled from `ground_truths`. This metric is useful in fact-based use cases like tourism help desk, historical QA, etc. This metric can help evaluate the retrieval mechanism for entities, based on comparison with entities present in `ground_truths`, because in cases where entities matter, we need the `contexts` which cover them.
+
+To compute this metric, we use two sets, $GE$ and $CE$, as set of entities present in `ground_truths` and set of entities present in `contexts` respectively. We then take the number of elements in intersection of these sets and divide it by the number of elements present in the $GE$, given by the formula:
+
+```{math}
+:label: context_entity_recall
+\text{context entity recall} = \frac{| CE \cap GE |}{| GE |}
+````
+
+```{hint}
+**Ground truth**: The Taj Mahal is an ivory-white marble mausoleum on the right bank of the river Yamuna in the Indian city of Agra. It was commissioned in 1631 by the Mughal emperor Shah Jahan to house the tomb of his favorite wife, Mumtaz Mahal.
+
+**High entity recall context**: The Taj Mahal is a symbol of love and architectural marvel located in Agra, India. It was built by the Mughal emperor Shah Jahan in memory of his beloved wife, Mumtaz Mahal. The structure is renowned for its intricate marble work and beautiful gardens surrounding it.
+
+**Low entity recall context**: The Taj Mahal is an iconic monument in India. It is a UNESCO World Heritage Site and attracts millions of visitors annually. The intricate carvings and stunning architecture make it a must-visit destination.
+
+````
+
+
+## Example
+
+```{code-block} python
+from ragas.metrics import ContextEntityRecall
+context_entity_recall = ContextEntityRecall()
+
+# Dataset({
+#     features: ['ground_truths','contexts'],
+#     num_rows: 25
+# })
+dataset: Dataset
+
+results = context_entity_recall.score(dataset)
+```

docs/concepts/metrics/index.md

@@ -14,6 +14,7 @@ Just like in any machine learning system, the performance of individual componen
 - [Context recall](context_recall.md)
 - [Context precision](context_precision.md)
 - [Context relevancy](context_relevancy.md)
+- [Context entity recall](context_entities_recall.md)
 
 ## End-to-End Evaluation
 
@@ -31,6 +32,7 @@ answer_relevance
 context_precision
 context_relevancy
 context_recall
+context_entities_recall
 semantic_similarity
 answer_correctness
 critique

docs/references/metrics.rst

@@ -8,6 +8,7 @@ Metrics
    ragas.metrics.answer_correctness
    ragas.metrics.context_precision
    ragas.metrics.context_recall
+   ragas.metrics.context_entity_recall
 
 .. automodule:: ragas.metrics
    :members: