Rm not implemented async in eval methods

libs/langchain/langchain/evaluation/schema.py

@@ -1,5 +1,6 @@
 """Interfaces to be implemented by general evaluators."""
 from __future__ import annotations
+import asyncio
 
 import logging
 from abc import ABC, abstractmethod
@@ -168,9 +169,13 @@ class StringEvaluator(_EvalArgsMixin, ABC):
                      - value: the string value of the evaluation, if applicable.
                      - reasoning: the reasoning for the evaluation, if applicable.
         """  # noqa: E501
-        raise NotImplementedError(
-            f"{self.__class__.__name__} hasn't implemented an async "
-            "aevaluate_strings method."
+        return await asyncio.get_running_loop().run_in_executor(
+            None,
+            self._evaluate_strings,
+            prediction=prediction,
+            reference=reference,
+            input=input,
+            **kwargs,
         )
 
     def evaluate_strings(
@@ -265,9 +270,14 @@ class PairwiseStringEvaluator(_EvalArgsMixin, ABC):
         Returns:
             dict: A dictionary containing the preference, scores, and/or other information.
         """  # noqa: E501
-        raise NotImplementedError(
-            f"{self.__class__.__name__} hasn't implemented an async "
-            "aevaluate_string_pairs method."
+        return await asyncio.get_running_loop().run_in_executor(
+            None,
+            self._evaluate_string_pairs,
+            prediction=prediction,
+            prediction_b=prediction_b,
+            reference=reference,
+            input=input,
+            **kwargs,
         )
 
     def evaluate_string_pairs(
@@ -381,9 +391,14 @@ class AgentTrajectoryEvaluator(_EvalArgsMixin, ABC):
         Returns:
             dict: The evaluation result.
         """
-        raise NotImplementedError(
-            f"{self.__class__.__name__} hasn't implemented an async "
-            "aevaluate_agent_trajectory method."
+        raise asyncio.get_running_loop().run_in_executor(
+            None,
+            self._evaluate_agent_trajectory,
+            prediction=prediction,
+            agent_trajectory=agent_trajectory,
+            input=input,
+            reference=reference,
+            **kwargs,
         )
 
     def evaluate_agent_trajectory(