Improve efficiency in factual correctness for precision mode (#1578)

### Description

This pull request optimizes the `_single_turn_ascore` function by
conditionally skipping unnecessary computations when the mode is set to
"precision." Previously, the function calculated `response_reference`
regardless of the scoring mode, which consumed resources and potentially
increased processing costs. By avoiding the `response_reference`
calculation in "precision" mode, this update aims to enhance speed and
reduce computational expenses.

### Changes Made

- Modified `_single_turn_ascore` function:
- **Conditionally calculated `response_reference`**: The
`response_reference` is now computed only when the mode is not
"precision."

---------

Co-authored-by: jeff yang <[email protected]>

src/ragas/metrics/_factual_correctness.py

@@ -244,13 +244,21 @@ async def _single_turn_ascore(
         reference_response = await self.verify_claims(
             premise=reference, hypothesis_list=response_claims, callbacks=callbacks
         )
-        response_reference = await self.verify_claims(
-            premise=response, hypothesis_list=reference_claims, callbacks=callbacks
-        )
+
+
+        if self.mode != "precision":
+            response_reference = await self.verify_claims(
+                premise=response, hypothesis_list=reference_claims, callbacks=callbacks
+            )
+        else:
+            response_reference = np.array([])
 
         true_positives = sum(reference_response)
         false_positives = sum(~reference_response)
-        false_negatives = sum(~response_reference)
+        if self.mode != "precision":
+            false_negatives = sum(~response_reference)
+        else:
+            false_negatives = 0
 
         if self.mode == "precision":
             score = true_positives / (true_positives + false_positives + 1e-8)