mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-24 18:24:20 +00:00
fix(langfuse.py): support logging failed llm api calls to langfuse
This commit is contained in:
Krrish Dholakia
parent
77fe71ee08
commit
a1bbb16ab2
2 changed files with 151 additions and 105 deletions
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@ -55,8 +55,21 @@ class LangFuseLogger:
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else:
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self.upstream_langfuse = None
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# def log_error(kwargs, response_obj, start_time, end_time):
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# generation = trace.generation(
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# level ="ERROR" # can be any of DEBUG, DEFAULT, WARNING or ERROR
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# status_message='error' # can be any string (e.g. stringified stack trace or error body)
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# )
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def log_event(
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self, kwargs, response_obj, start_time, end_time, user_id, print_verbose
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self,
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kwargs,
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response_obj,
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start_time,
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end_time,
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user_id,
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print_verbose,
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level="DEFAULT",
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status_message=None,
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):
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# Method definition
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@ -84,37 +97,49 @@ class LangFuseLogger:
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pass
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# end of processing langfuse ########################
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if kwargs.get("call_type", None) == "embedding" or isinstance(
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response_obj, litellm.EmbeddingResponse
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if (
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level == "ERROR"
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and status_message is not None
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and isinstance(status_message, str)
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):
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input = prompt
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output = status_message
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elif response_obj is not None and (
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kwargs.get("call_type", None) == "embedding"
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or isinstance(response_obj, litellm.EmbeddingResponse)
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):
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input = prompt
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output = response_obj["data"]
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else:
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elif response_obj is not None:
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input = prompt
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output = response_obj["choices"][0]["message"].json()
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print_verbose(f"OUTPUT IN LANGFUSE: {output}; original: {response_obj}")
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self._log_langfuse_v2(
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user_id,
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metadata,
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output,
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start_time,
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end_time,
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kwargs,
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optional_params,
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input,
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response_obj,
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print_verbose,
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) if self._is_langfuse_v2() else self._log_langfuse_v1(
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user_id,
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metadata,
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output,
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start_time,
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end_time,
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kwargs,
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optional_params,
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input,
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response_obj,
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)
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print(f"OUTPUT IN LANGFUSE: {output}; original: {response_obj}")
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if self._is_langfuse_v2():
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self._log_langfuse_v2(
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user_id,
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metadata,
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output,
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start_time,
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end_time,
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kwargs,
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optional_params,
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input,
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response_obj,
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level,
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print_verbose,
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)
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elif response_obj is not None:
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self._log_langfuse_v1(
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user_id,
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metadata,
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output,
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start_time,
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end_time,
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kwargs,
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optional_params,
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input,
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response_obj,
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)
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self.Langfuse.flush()
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print_verbose(
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@ -123,15 +148,15 @@ class LangFuseLogger:
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verbose_logger.info(f"Langfuse Layer Logging - logging success")
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except:
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traceback.print_exc()
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print_verbose(f"Langfuse Layer Error - {traceback.format_exc()}")
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print(f"Langfuse Layer Error - {traceback.format_exc()}")
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pass
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async def _async_log_event(
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self, kwargs, response_obj, start_time, end_time, user_id, print_verbose
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):
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self.log_event(
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kwargs, response_obj, start_time, end_time, user_id, print_verbose
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)
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"""
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TODO: support async calls when langfuse is truly async
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"""
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def _is_langfuse_v2(self):
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import langfuse
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@ -193,57 +218,78 @@ class LangFuseLogger:
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optional_params,
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input,
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response_obj,
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level,
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print_verbose,
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):
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import langfuse
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tags = []
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supports_tags = Version(langfuse.version.__version__) >= Version("2.6.3")
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supports_costs = Version(langfuse.version.__version__) >= Version("2.7.3")
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try:
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tags = []
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supports_tags = Version(langfuse.version.__version__) >= Version("2.6.3")
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supports_costs = Version(langfuse.version.__version__) >= Version("2.7.3")
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print_verbose(f"Langfuse Layer Logging - logging to langfuse v2 ")
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print_verbose(f"Langfuse Layer Logging - logging to langfuse v2 ")
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generation_name = metadata.get("generation_name", None)
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if generation_name is None:
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# just log `litellm-{call_type}` as the generation name
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generation_name = f"litellm-{kwargs.get('call_type', 'completion')}"
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generation_name = metadata.get("generation_name", None)
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if generation_name is None:
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# just log `litellm-{call_type}` as the generation name
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generation_name = f"litellm-{kwargs.get('call_type', 'completion')}"
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trace_params = {
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"name": generation_name,
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"input": input,
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"output": output,
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"user_id": metadata.get("trace_user_id", user_id),
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"id": metadata.get("trace_id", None),
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"session_id": metadata.get("session_id", None),
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}
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cost = kwargs["response_cost"]
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print_verbose(f"trace: {cost}")
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if supports_tags:
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for key, value in metadata.items():
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tags.append(f"{key}:{value}")
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if "cache_hit" in kwargs:
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tags.append(f"cache_hit:{kwargs['cache_hit']}")
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trace_params.update({"tags": tags})
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trace_params = {
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"name": generation_name,
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"input": input,
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"user_id": metadata.get("trace_user_id", user_id),
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"id": metadata.get("trace_id", None),
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"session_id": metadata.get("session_id", None),
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}
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trace = self.Langfuse.trace(**trace_params)
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if level == "ERROR":
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trace_params["status_message"] = output
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else:
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trace_params["output"] = output
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# get generation_id
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generation_id = None
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if response_obj.get("id", None) is not None:
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generation_id = litellm.utils.get_logging_id(start_time, response_obj)
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trace.generation(
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name=generation_name,
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id=metadata.get("generation_id", generation_id),
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startTime=start_time,
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endTime=end_time,
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model=kwargs["model"],
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modelParameters=optional_params,
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input=input,
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output=output,
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usage={
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"prompt_tokens": response_obj["usage"]["prompt_tokens"],
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"completion_tokens": response_obj["usage"]["completion_tokens"],
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"total_cost": cost if supports_costs else None,
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},
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metadata=metadata,
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)
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cost = kwargs.get("response_cost", None)
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print_verbose(f"trace: {cost}")
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if supports_tags:
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for key, value in metadata.items():
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tags.append(f"{key}:{value}")
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if "cache_hit" in kwargs:
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tags.append(f"cache_hit:{kwargs['cache_hit']}")
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trace_params.update({"tags": tags})
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trace = self.Langfuse.trace(**trace_params)
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if level == "ERROR":
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trace.generation(
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level="ERROR", # can be any of DEBUG, DEFAULT, WARNING or ERROR
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status_message=output, # can be any string (e.g. stringified stack trace or error body)
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)
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print(f"SUCCESSFULLY LOGGED ERROR")
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else:
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# get generation_id
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generation_id = None
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if (
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response_obj is not None
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and response_obj.get("id", None) is not None
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):
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generation_id = litellm.utils.get_logging_id(
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start_time, response_obj
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)
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trace.generation(
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name=generation_name,
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id=metadata.get("generation_id", generation_id),
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startTime=start_time,
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endTime=end_time,
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model=kwargs["model"],
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modelParameters=optional_params,
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input=input,
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output=output,
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usage={
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"prompt_tokens": response_obj["usage"]["prompt_tokens"],
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"completion_tokens": response_obj["usage"]["completion_tokens"],
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"total_cost": cost if supports_costs else None,
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},
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metadata=metadata,
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)
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except Exception as e:
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print(f"Langfuse Layer Error - {traceback.format_exc()}")
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@ -1636,34 +1636,6 @@ class Logging:
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end_time=end_time,
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print_verbose=print_verbose,
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)
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if callback == "langfuse":
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global langFuseLogger
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print_verbose("reaches Async langfuse for logging!")
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kwargs = {}
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for k, v in self.model_call_details.items():
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if (
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k != "original_response"
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): # copy.deepcopy raises errors as this could be a coroutine
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kwargs[k] = v
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# this only logs streaming once, complete_streaming_response exists i.e when stream ends
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if self.stream:
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if "complete_streaming_response" not in kwargs:
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return
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else:
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print_verbose(
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"reaches Async langfuse for streaming logging!"
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)
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result = kwargs["complete_streaming_response"]
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if langFuseLogger is None:
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langFuseLogger = LangFuseLogger()
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await langFuseLogger._async_log_event(
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kwargs=kwargs,
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response_obj=result,
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start_time=start_time,
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end_time=end_time,
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user_id=kwargs.get("user", None),
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print_verbose=print_verbose,
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)
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except:
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print_verbose(
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f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {traceback.format_exc()}"
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@ -1788,9 +1760,37 @@ class Logging:
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response_obj=result,
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kwargs=self.model_call_details,
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)
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elif callback == "langfuse":
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global langFuseLogger
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verbose_logger.debug("reaches langfuse for logging!")
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kwargs = {}
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for k, v in self.model_call_details.items():
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if (
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k != "original_response"
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): # copy.deepcopy raises errors as this could be a coroutine
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kwargs[k] = v
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# this only logs streaming once, complete_streaming_response exists i.e when stream ends
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if langFuseLogger is None or (
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self.langfuse_public_key != langFuseLogger.public_key
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and self.langfuse_secret != langFuseLogger.secret_key
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):
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langFuseLogger = LangFuseLogger(
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langfuse_public_key=self.langfuse_public_key,
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langfuse_secret=self.langfuse_secret,
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)
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langFuseLogger.log_event(
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start_time=start_time,
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end_time=end_time,
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response_obj=None,
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user_id=kwargs.get("user", None),
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print_verbose=print_verbose,
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status_message=str(exception),
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level="ERROR",
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kwargs=self.model_call_details,
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)
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except Exception as e:
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print_verbose(
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f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while failure logging with integrations {traceback.format_exc()}"
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f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while failure logging with integrations {str(e)}"
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)
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print_verbose(
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f"LiteLLM.Logging: is sentry capture exception initialized {capture_exception}"
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