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(feat) DataDog Logger - Add Failure logging + use Standard Logging payload (#6929)
* add async_log_failure_event for dd * use standard logging payload for DD logging * use standard logging payload for DD * fix use SLP status * allow opting into _create_v0_logging_payload * add unit tests for DD logging payload * fix dd logging tests
This commit is contained in:
Ishaan Jaff
GitHub
parent
ece149c30b
commit
b08b37b11d
3 changed files with 257 additions and 90 deletions
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@ -33,6 +33,7 @@ from litellm.llms.custom_httpx.http_handler import (
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httpxSpecialProvider,
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)
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from litellm.types.services import ServiceLoggerPayload
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from litellm.types.utils import StandardLoggingPayload
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from .types import DD_ERRORS, DatadogPayload, DataDogStatus
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from .utils import make_json_serializable
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@ -106,20 +107,20 @@ class DataDogLogger(CustomBatchLogger):
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verbose_logger.debug(
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"Datadog: Logging - Enters logging function for model %s", kwargs
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)
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dd_payload = self.create_datadog_logging_payload(
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kwargs=kwargs,
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response_obj=response_obj,
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start_time=start_time,
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end_time=end_time,
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)
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await self._log_async_event(kwargs, response_obj, start_time, end_time)
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self.log_queue.append(dd_payload)
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except Exception as e:
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verbose_logger.exception(
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f"Datadog Layer Error - {str(e)}\n{traceback.format_exc()}"
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)
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pass
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async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
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try:
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verbose_logger.debug(
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f"Datadog, event added to queue. Will flush in {self.flush_interval} seconds..."
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"Datadog: Logging - Enters logging function for model %s", kwargs
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)
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if len(self.log_queue) >= self.batch_size:
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await self.async_send_batch()
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await self._log_async_event(kwargs, response_obj, start_time, end_time)
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except Exception as e:
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verbose_logger.exception(
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@ -181,12 +182,20 @@ class DataDogLogger(CustomBatchLogger):
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verbose_logger.debug(
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"Datadog: Logging - Enters logging function for model %s", kwargs
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)
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dd_payload = self.create_datadog_logging_payload(
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kwargs=kwargs,
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response_obj=response_obj,
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start_time=start_time,
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end_time=end_time,
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)
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if litellm.datadog_use_v1 is True:
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dd_payload = self._create_v0_logging_payload(
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kwargs=kwargs,
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response_obj=response_obj,
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start_time=start_time,
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end_time=end_time,
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)
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else:
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dd_payload = self.create_datadog_logging_payload(
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kwargs=kwargs,
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response_obj=response_obj,
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start_time=start_time,
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end_time=end_time,
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)
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response = self.sync_client.post(
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url=self.intake_url,
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@ -215,6 +224,22 @@ class DataDogLogger(CustomBatchLogger):
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pass
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pass
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async def _log_async_event(self, kwargs, response_obj, start_time, end_time):
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dd_payload = self.create_datadog_logging_payload(
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kwargs=kwargs,
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response_obj=response_obj,
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start_time=start_time,
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end_time=end_time,
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)
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self.log_queue.append(dd_payload)
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verbose_logger.debug(
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f"Datadog, event added to queue. Will flush in {self.flush_interval} seconds..."
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)
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if len(self.log_queue) >= self.batch_size:
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await self.async_send_batch()
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def create_datadog_logging_payload(
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self,
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kwargs: Union[dict, Any],
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@ -236,63 +261,19 @@ class DataDogLogger(CustomBatchLogger):
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"""
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import json
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litellm_params = kwargs.get("litellm_params", {})
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metadata = (
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litellm_params.get("metadata", {}) or {}
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) # if litellm_params['metadata'] == None
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messages = kwargs.get("messages")
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optional_params = kwargs.get("optional_params", {})
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call_type = kwargs.get("call_type", "litellm.completion")
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cache_hit = kwargs.get("cache_hit", False)
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usage = response_obj["usage"]
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id = response_obj.get("id", str(uuid.uuid4()))
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usage = dict(usage)
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try:
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response_time = (end_time - start_time).total_seconds() * 1000
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except Exception:
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response_time = None
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standard_logging_object: Optional[StandardLoggingPayload] = kwargs.get(
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"standard_logging_object", None
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)
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if standard_logging_object is None:
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raise ValueError("standard_logging_object not found in kwargs")
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try:
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response_obj = dict(response_obj)
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except Exception:
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response_obj = response_obj
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# Clean Metadata before logging - never log raw metadata
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# the raw metadata can contain circular references which leads to infinite recursion
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# we clean out all extra litellm metadata params before logging
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clean_metadata = {}
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if isinstance(metadata, dict):
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for key, value in metadata.items():
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# clean litellm metadata before logging
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if key in [
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"endpoint",
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"caching_groups",
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"previous_models",
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]:
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continue
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else:
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clean_metadata[key] = value
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status = DataDogStatus.INFO
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if standard_logging_object.get("status") == "failure":
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status = DataDogStatus.ERROR
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# Build the initial payload
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payload = {
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"id": id,
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"call_type": call_type,
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"cache_hit": cache_hit,
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"start_time": start_time,
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"end_time": end_time,
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"response_time": response_time,
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"model": kwargs.get("model", ""),
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"user": kwargs.get("user", ""),
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"model_parameters": optional_params,
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"spend": kwargs.get("response_cost", 0),
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"messages": messages,
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"response": response_obj,
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"usage": usage,
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"metadata": clean_metadata,
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}
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make_json_serializable(payload)
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json_payload = json.dumps(payload)
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make_json_serializable(standard_logging_object)
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json_payload = json.dumps(standard_logging_object)
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verbose_logger.debug("Datadog: Logger - Logging payload = %s", json_payload)
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@ -302,7 +283,7 @@ class DataDogLogger(CustomBatchLogger):
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hostname="",
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message=json_payload,
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service="litellm-server",
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status=DataDogStatus.INFO,
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status=status,
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)
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return dd_payload
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@ -382,3 +363,88 @@ class DataDogLogger(CustomBatchLogger):
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No user has asked for this so far, this might be spammy on datatdog. If need arises we can implement this
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"""
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return
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def _create_v0_logging_payload(
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self,
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kwargs: Union[dict, Any],
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response_obj: Any,
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start_time: datetime.datetime,
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end_time: datetime.datetime,
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) -> DatadogPayload:
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"""
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Note: This is our V1 Version of DataDog Logging Payload
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(Not Recommended) If you want this to get logged set `litellm.datadog_use_v1 = True`
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"""
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import json
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litellm_params = kwargs.get("litellm_params", {})
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metadata = (
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litellm_params.get("metadata", {}) or {}
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) # if litellm_params['metadata'] == None
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messages = kwargs.get("messages")
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optional_params = kwargs.get("optional_params", {})
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call_type = kwargs.get("call_type", "litellm.completion")
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cache_hit = kwargs.get("cache_hit", False)
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usage = response_obj["usage"]
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id = response_obj.get("id", str(uuid.uuid4()))
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usage = dict(usage)
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try:
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response_time = (end_time - start_time).total_seconds() * 1000
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except Exception:
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response_time = None
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try:
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response_obj = dict(response_obj)
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except Exception:
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response_obj = response_obj
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# Clean Metadata before logging - never log raw metadata
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# the raw metadata can contain circular references which leads to infinite recursion
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# we clean out all extra litellm metadata params before logging
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clean_metadata = {}
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if isinstance(metadata, dict):
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for key, value in metadata.items():
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# clean litellm metadata before logging
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if key in [
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"endpoint",
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"caching_groups",
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"previous_models",
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]:
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continue
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else:
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clean_metadata[key] = value
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# Build the initial payload
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payload = {
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"id": id,
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"call_type": call_type,
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"cache_hit": cache_hit,
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"start_time": start_time,
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"end_time": end_time,
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"response_time": response_time,
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"model": kwargs.get("model", ""),
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"user": kwargs.get("user", ""),
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"model_parameters": optional_params,
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"spend": kwargs.get("response_cost", 0),
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"messages": messages,
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"response": response_obj,
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"usage": usage,
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"metadata": clean_metadata,
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}
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make_json_serializable(payload)
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json_payload = json.dumps(payload)
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verbose_logger.debug("Datadog: Logger - Logging payload = %s", json_payload)
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dd_payload = DatadogPayload(
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ddsource=os.getenv("DD_SOURCE", "litellm"),
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ddtags="",
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hostname="",
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message=json_payload,
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service="litellm-server",
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status=DataDogStatus.INFO,
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)
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return dd_payload
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