Revert "(fix) GCS bucket logger - apply `truncate_standard_logging_payload_co…" (#7515)

This reverts commit 26a37c5.

Author: ishaan-jaff

litellm/integrations/datadog/datadog.py

@@ -256,6 +256,10 @@ def create_datadog_logging_payload(
         """
         import json
 
+        from litellm.litellm_core_utils.litellm_logging import (
+            truncate_standard_logging_payload_content,
+        )
+
         standard_logging_object: Optional[StandardLoggingPayload] = kwargs.get(
             "standard_logging_object", None
         )
@@ -267,6 +271,7 @@ def create_datadog_logging_payload(
             status = DataDogStatus.ERROR
 
         # Build the initial payload
+        truncate_standard_logging_payload_content(standard_logging_object)
         json_payload = json.dumps(standard_logging_object, default=str)
 
         verbose_logger.debug("Datadog: Logger - Logging payload = %s", json_payload)

litellm/integrations/gcs_bucket/gcs_bucket.py

@@ -138,12 +138,12 @@ async def async_send_batch(self):
                     logging_payload=logging_payload,
                 )
 
-        except Exception as e:
-            verbose_logger.exception(f"GCS Bucket batch logging error: {str(e)}")
-        finally:
             # Clear the queue after processing
             self.log_queue.clear()
 
+        except Exception as e:
+            verbose_logger.exception(f"GCS Bucket batch logging error: {str(e)}")
+
     def _get_object_name(
         self, kwargs: Dict, logging_payload: StandardLoggingPayload, response_obj: Any
     ) -> str:

litellm/litellm_core_utils/core_helpers.py

@@ -5,7 +5,7 @@
 import httpx
 
 from litellm._logging import verbose_logger
-from litellm.types.llms.openai import AllMessageValues
+from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolParam
 
 if TYPE_CHECKING:
     from opentelemetry.trace import Span as _Span

litellm/litellm_core_utils/litellm_logging.py

@@ -3026,8 +3026,6 @@ def get_standard_logging_object_payload(
             ),
         )
 
-        truncate_standard_logging_payload_content(payload)
-
         return payload
     except Exception as e:
         verbose_logger.exception(
@@ -3042,26 +3040,20 @@ def truncate_standard_logging_payload_content(
     """
     Truncate error strings and message content in logging payload
 
-    Most logging integrations - DataDog / GCS Bucket / have a limit on the size of the payload. ~around(1MB)
+    Some loggers like DataDog have a limit on the size of the payload. (1MB)
 
     This function truncates the error string and the message content if they exceed a certain length.
     """
-    try:
-        MAX_STR_LENGTH = 10_000
-
-        # Truncate fields that might exceed max length
-        fields_to_truncate = ["error_str", "messages", "response"]
-        for field in fields_to_truncate:
-            _truncate_field(
-                standard_logging_object=standard_logging_object,
-                field_name=field,
-                max_length=MAX_STR_LENGTH,
-            )
-    except Exception as e:
-        verbose_logger.exception(
-            "Error truncating standard logging payload - {}".format(str(e))
+    MAX_STR_LENGTH = 10_000
+
+    # Truncate fields that might exceed max length
+    fields_to_truncate = ["error_str", "messages", "response"]
+    for field in fields_to_truncate:
+        _truncate_field(
+            standard_logging_object=standard_logging_object,
+            field_name=field,
+            max_length=MAX_STR_LENGTH,
         )
-        return
 
 
 def _truncate_text(text: str, max_length: int) -> str:

litellm/llms/azure_ai/chat/transformation.py

@@ -13,7 +13,7 @@
 from litellm.llms.openai.common_utils import drop_params_from_unprocessable_entity_error
 from litellm.llms.openai.openai import OpenAIConfig
 from litellm.secret_managers.main import get_secret_str
-from litellm.types.llms.openai import AllMessageValues
+from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolParam
 from litellm.types.utils import ModelResponse, ProviderField
 from litellm.utils import _add_path_to_api_base
 

tests/logging_callback_tests/test_datadog.py

@@ -1,10 +1,7 @@
 import io
 import os
-from re import M
 import sys
 
-from litellm.integrations.custom_logger import CustomLogger
-
 
 sys.path.insert(0, os.path.abspath("../.."))
 
@@ -395,28 +392,22 @@ async def test_datadog_payload_environment_variables():
         pytest.fail(f"Test failed with exception: {str(e)}")
 
 
-class TestDDLogger(CustomLogger):
-    def __init__(self):
-        self.standard_logging_object: Optional[StandardLoggingPayload] = None
-
-    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
-        self.standard_logging_object = kwargs["standard_logging_object"]
-
-
 @pytest.mark.asyncio
 async def test_datadog_payload_content_truncation():
     """
     Test that DataDog payload correctly truncates long content
 
     DataDog has a limit of 1MB for the logged payload size.
     """
-    dd_logger = TestDDLogger()
-    litellm.callbacks = [dd_logger]
+    dd_logger = DataDogLogger()
 
+    # Create a standard payload with very long content
+    standard_payload = create_standard_logging_payload()
     long_content = "x" * 80_000  # Create string longer than MAX_STR_LENGTH (10_000)
 
-    # messages with long content
-    messages = [
+    # Modify payload with long content
+    standard_payload["error_str"] = long_content
+    standard_payload["messages"] = [
         {
             "role": "user",
             "content": [
@@ -430,26 +421,28 @@ async def test_datadog_payload_content_truncation():
             ],
         }
     ]
-    await litellm.acompletion(
-        model="gpt-3.5-turbo",
-        messages=messages,
-        temperature=0.2,
-        mock_response=long_content,
-    )
-
-    await asyncio.sleep(2)
+    standard_payload["response"] = {"choices": [{"message": {"content": long_content}}]}
 
     # Create the payload
-    standard_logging_payload = dd_logger.standard_logging_object
+    dd_payload = dd_logger.create_datadog_logging_payload(
+        kwargs={"standard_logging_object": standard_payload},
+        response_obj=None,
+        start_time=datetime.now(),
+        end_time=datetime.now(),
+    )
+
+    print("dd_payload", json.dumps(dd_payload, indent=2))
 
-    print("standard_logging_payload", json.dumps(standard_logging_payload, indent=2))
+    # Parse the message back to dict to verify truncation
+    message_dict = json.loads(dd_payload["message"])
 
     # Verify truncation of fields
+    assert len(message_dict["error_str"]) < 10_100, "error_str not truncated correctly"
     assert (
-        len(str(standard_logging_payload["messages"])) < 10_100
+        len(str(message_dict["messages"])) < 10_100
     ), "messages not truncated correctly"
     assert (
-        len(str(standard_logging_payload["response"])) < 10_100
+        len(str(message_dict["response"])) < 10_100
     ), "response not truncated correctly"