spelling: response

Signed-off-by: Josh Soref <[email protected]>

Author: jsoref

src/google/adk/a2a/converters/part_converter.py

@@ -84,7 +84,7 @@ def convert_a2a_part_to_genai_part(
       return None
 
   if isinstance(part, a2a_types.DataPart):
-    # Convert the Data Part to funcall and function reponse.
+    # Convert the Data Part to funcall and function response.
     # This is mainly for converting human in the loop and auth request and
     # response.
     # TODO once A2A defined how to suervice such information, migrate below
@@ -179,7 +179,7 @@ def convert_genai_part_to_a2a_part(
 
     return a2a_types.Part(root=a2a_part)
 
-  # Convert the funcall and function reponse to A2A DataPart.
+  # Convert the funcall and function response to A2A DataPart.
   # This is mainly for converting human in the loop and auth request and
   # response.
   # TODO once A2A defined how to suervice such information, migrate below

src/google/adk/evaluation/local_eval_service.py

@@ -199,7 +199,7 @@ async def _evaluate_single_inference_result(
 
     # We also keep track of the overall score for a metric, derived from all
     # invocation. For example, if we were keeping track the metric that compares
-    # how well is the final resposne as compared to a golden answer, then each
+    # how well is the final response as compared to a golden answer, then each
     # invocation will have the value of this metric. We will also have an
     # overall score using aggregation strategy across all invocations. This
     # would be the score for the eval case.

src/google/adk/evaluation/response_evaluator.py

@@ -36,7 +36,7 @@ class ResponseEvaluator(Evaluator):
 
   This class supports two metrics:
   1) response_evaluation_score
-  This metric evaluates how coherent agent's resposne was.
+  This metric evaluates how coherent agent's response was.
 
   Value range of this metric is [1,5], with values closer to 5 more desirable.
 
@@ -81,7 +81,7 @@ def get_metric_info(metric_name: str) -> MetricInfo:
       return MetricInfo(
           metric_name=PrebuiltMetrics.RESPONSE_EVALUATION_SCORE.value,
           description=(
-              "This metric evaluates how coherent agent's resposne was. Value"
+              "This metric evaluates how coherent agent's response was. Value"
               " range of this metric is [1,5], with values closer to 5 more"
               " desirable."
           ),