fix context in _answer_correctness.py

src/ragas/metrics/_answer_correctness.py

@@ -65,12 +65,10 @@ class AnswerCorrectness(MetricWithLLM):
     batch_size: int
         batch size for evaluation
     weights:
-        a list of two weights corresponding to semantic similarity and factuality
-        Defaults [0.5, 0.5]
+        a list of two weights corresponding to factuality and semantic similarity
+        Defaults [0.75, 0.25]
     answer_similarity:
         The AnswerSimilarity object
-    faithfulness
-        The faithfulness object
     """
 
     name: str = "answer_correctness"  # type: ignore[reportIncompatibleMethodOverride]
@@ -108,41 +106,41 @@ def _score_batch(
                 )
                 prompts.append(ChatPromptTemplate.from_messages([human_prompt]))
 
-        result = self.llm.generate(prompts, callbacks=batch_group)
-        outputs = result.generations
-        key_map = {
-            "TP": "statements that are present in both the answer and the ground truth",
-            "FP": "statements present in the answer but not found in the ground truth",
-            "FN": "relevant statements found in the ground truth but omitted in the answer",  # noqa: E501
-        }
-
-        f1_score = []
-        for prediction in outputs:
-            prediction = json_loader.safe_load(prediction[0].text, self.llm)
-            prediction = prediction if isinstance(prediction, list) else []
-            if prediction:
-                prediction = [
-                    item.get(key_map[k], np.nan)
-                    for item in prediction
-                    for k in key_map.keys()
-                ]
-                tp, fp, fn = [
-                    len(item) if isinstance(item, list) else np.nan
-                    for item in prediction
-                ]
-                score = tp / (tp + 0.5 * (fp + fn))
-            else:
-                score = np.nan
-
-            f1_score.append(score)
-
-        similarity_scores = self.answer_similarity._score_batch(dataset)  # type: ignore
-        scores_stacked = np.vstack([f1_score, similarity_scores])
-        scores = np.average(
-            scores_stacked,
-            axis=0,
-            weights=self.weights,
-        )
+            result = self.llm.generate(prompts, callbacks=batch_group)
+            outputs = result.generations
+            key_map = {
+                "TP": "statements that are present in both the answer and the ground truth",
+                "FP": "statements present in the answer but not found in the ground truth",
+                "FN": "relevant statements found in the ground truth but omitted in the answer",  # noqa: E501
+            }
+    
+            f1_score = []
+            for prediction in outputs:
+                prediction = json_loader.safe_load(prediction[0].text, self.llm)
+                prediction = prediction if isinstance(prediction, list) else []
+                if prediction:
+                    prediction = [
+                        item.get(key_map[k], np.nan)
+                        for item in prediction
+                        for k in key_map.keys()
+                    ]
+                    tp, fp, fn = [
+                        len(item) if isinstance(item, list) else np.nan
+                        for item in prediction
+                    ]
+                    score = tp / (tp + 0.5 * (fp + fn))
+                else:
+                    score = np.nan
+    
+                f1_score.append(score)
+    
+            similarity_scores = self.answer_similarity._score_batch(dataset, callbacks=batch_group)  # type: ignore
+            scores_stacked = np.vstack([f1_score, similarity_scores])
+            scores = np.average(
+                scores_stacked,
+                axis=0,
+                weights=self.weights,
+            )
 
         return scores.tolist()