no logs

Author: nina-kollman

packages/sample-app/sample_app/guardrail_medical_chat_example.py

@@ -55,7 +55,6 @@ def handle_medical_evaluation(evaluator_result, original_result):
     Returns:
         Either the original result dict or a modified version
     """
-    print("NOMI - handle_medical_evaluation - evaluator_result:", evaluator_result)
     if not evaluator_result.success:
         # Return a modified dict with error message
         return {

packages/traceloop-sdk/traceloop/sdk/evaluator/evaluator.py

@@ -98,23 +98,17 @@ async def run_experiment_evaluator(
             task_id, experiment_id, experiment_run_id, input, evaluator_version, evaluator_config
         )
 
-        print("NOMI - run_experiment_evaluator - request:", request)
-
         execute_response = await self._execute_evaluator_request(
             evaluator_slug, request, timeout_in_sec
         )
 
-        print("NOMI - run_experiment_evaluator - execute_response:", execute_response)
-
         sse_client = SSEClient(shared_client=self._async_http_client)
         sse_result = await sse_client.wait_for_result(
             execute_response.execution_id,
             execute_response.stream_url,
             timeout_in_sec,
         )
 
-        print("NOMI - run_experiment_evaluator - sse_result:", sse_result)
-
         return sse_result
 
     async def trigger_experiment_evaluator(

packages/traceloop-sdk/traceloop/sdk/guardrails/guardrails.py

@@ -47,17 +47,11 @@ def guardrail(
 
     slug, evaluator_version, evaluator_config, required_input_fields = evaluator_details
 
-    print("NOMI - guardrail - slug:", slug)
-    print("NOMI - guardrail - evaluator_version:", evaluator_version)
-    print("NOMI - guardrail - evaluator_config:", evaluator_config)
-    print("NOMI - guardrail - required_input_fields:", required_input_fields)
-
     def decorator(func: Callable[P, R]) -> Callable[P, Dict[str, Any]]:
         @wraps(func)
         async def async_wrapper(*args: P.args, **kwargs: P.kwargs) -> Dict[str, Any]:
             # Execute the original function - should return a dict with fields matching required_input_fields
             original_result = await func(*args, **kwargs)
-            print("NOMI - guardrail - original_result:", original_result)
 
             # Validate that original_result is a dict
             if not isinstance(original_result, dict):
@@ -77,8 +71,6 @@ async def async_wrapper(*args: P.args, **kwargs: P.kwargs) -> Dict[str, Any]:
                 slug, original_result, evaluator_version, evaluator_config
             )
 
-            print("NOMI - guardrail - evaluator_result:", evaluator_result)
-
             # Use callback if provided, otherwise use default behavior
             if on_evaluation_complete:
                 return on_evaluation_complete(evaluator_result, original_result)
@@ -178,10 +170,6 @@ async def execute_evaluator(
         """
         try:
 
-            print("NOMI - guardrails - data:", data)
-            print("NOMI - guardrails - evaluator_version:", evaluator_version)
-            print("NOMI - guardrails - evaluator_config:", evaluator_config)
-
             # Use dummy IDs for guardrails (they don't need experiment tracking)
             result = await self._evaluator.run_experiment_evaluator(
                 evaluator_slug=slug,
@@ -203,8 +191,6 @@ async def execute_evaluator(
                 evaluator_result = raw_result.get("evaluator_result", "")
                 success = evaluator_result == "pass"
                 reason = raw_result.get("reason", None)
-                print("NOMI - guardrails - reason:", reason)
-                print("NOMI - guardrails - success:", success)
                 return OutputSchema(reason=reason, success=success)
 
             # Otherwise, try custom evaluator format with 'pass' and 'reason' fields

packages/traceloop-sdk/traceloop/sdk/guardrails/types.py

@@ -1,5 +1,5 @@
 from typing import Dict, Optional, Any
-from pydantic import BaseModel, Field
+from pydantic import BaseModel
 
 
 class InputExtractor(BaseModel):