mirror of
https://github.com/BerriAI/litellm.git
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853b70aba9
allow user to call out a trace as pre-existing, this prevents creating a default trace name, and potentially overwriting past traces
419 lines
16 KiB
Python
419 lines
16 KiB
Python
#### What this does ####
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# On success, logs events to Langfuse
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import dotenv, os
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dotenv.load_dotenv() # Loading env variables using dotenv
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import copy
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import traceback
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from packaging.version import Version
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from litellm._logging import verbose_logger
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import litellm
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class LangFuseLogger:
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# Class variables or attributes
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def __init__(
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self, langfuse_public_key=None, langfuse_secret=None, flush_interval=1
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):
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try:
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from langfuse import Langfuse
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except Exception as e:
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raise Exception(
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f"\033[91mLangfuse not installed, try running 'pip install langfuse' to fix this error: {e}\n{traceback.format_exc()}\033[0m"
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)
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# Instance variables
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self.secret_key = langfuse_secret or os.getenv("LANGFUSE_SECRET_KEY")
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self.public_key = langfuse_public_key or os.getenv("LANGFUSE_PUBLIC_KEY")
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self.langfuse_host = os.getenv("LANGFUSE_HOST", "https://cloud.langfuse.com")
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self.langfuse_release = os.getenv("LANGFUSE_RELEASE")
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self.langfuse_debug = os.getenv("LANGFUSE_DEBUG")
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self.Langfuse = Langfuse(
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public_key=self.public_key,
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secret_key=self.secret_key,
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host=self.langfuse_host,
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release=self.langfuse_release,
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debug=self.langfuse_debug,
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flush_interval=flush_interval, # flush interval in seconds
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)
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# set the current langfuse project id in the environ
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# this is used by Alerting to link to the correct project
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try:
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project_id = self.Langfuse.client.projects.get().data[0].id
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os.environ["LANGFUSE_PROJECT_ID"] = project_id
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except:
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project_id = None
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if os.getenv("UPSTREAM_LANGFUSE_SECRET_KEY") is not None:
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self.upstream_langfuse_secret_key = os.getenv(
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"UPSTREAM_LANGFUSE_SECRET_KEY"
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)
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self.upstream_langfuse_public_key = os.getenv(
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"UPSTREAM_LANGFUSE_PUBLIC_KEY"
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)
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self.upstream_langfuse_host = os.getenv("UPSTREAM_LANGFUSE_HOST")
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self.upstream_langfuse_release = os.getenv("UPSTREAM_LANGFUSE_RELEASE")
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self.upstream_langfuse_debug = os.getenv("UPSTREAM_LANGFUSE_DEBUG")
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self.upstream_langfuse = Langfuse(
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public_key=self.upstream_langfuse_public_key,
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secret_key=self.upstream_langfuse_secret_key,
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host=self.upstream_langfuse_host,
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release=self.upstream_langfuse_release,
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debug=self.upstream_langfuse_debug,
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)
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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,
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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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try:
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print_verbose(
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f"Langfuse Logging - Enters logging function for model {kwargs}"
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)
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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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optional_params = copy.deepcopy(kwargs.get("optional_params", {}))
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prompt = {"messages": kwargs.get("messages")}
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functions = optional_params.pop("functions", None)
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tools = optional_params.pop("tools", None)
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if functions is not None:
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prompt["functions"] = functions
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if tools is not None:
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prompt["tools"] = tools
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# langfuse only accepts str, int, bool, float for logging
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for param, value in optional_params.items():
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if not isinstance(value, (str, int, bool, float)):
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try:
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optional_params[param] = str(value)
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except:
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# if casting value to str fails don't block logging
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pass
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# end of processing langfuse ########################
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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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elif response_obj is not None and isinstance(
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response_obj, litellm.ModelResponse
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):
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input = prompt
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output = response_obj["choices"][0]["message"].json()
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elif response_obj is not None and isinstance(
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response_obj, litellm.TextCompletionResponse
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):
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input = prompt
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output = response_obj.choices[0].text
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elif response_obj is not None and isinstance(
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response_obj, litellm.ImageResponse
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):
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input = prompt
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output = response_obj["data"]
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print_verbose(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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litellm_params,
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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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print_verbose(
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f"Langfuse Layer Logging - final response object: {response_obj}"
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)
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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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verbose_logger.debug(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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"""
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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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return Version(langfuse.version.__version__) >= Version("2.0.0")
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def _log_langfuse_v1(
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self,
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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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from langfuse.model import CreateTrace, CreateGeneration
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verbose_logger.warning(
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"Please upgrade langfuse to v2.0.0 or higher: https://github.com/langfuse/langfuse-python/releases/tag/v2.0.1"
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)
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trace = self.Langfuse.trace(
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CreateTrace(
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name=metadata.get("generation_name", "litellm-completion"),
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input=input,
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output=output,
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userId=user_id,
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)
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)
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trace.generation(
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CreateGeneration(
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name=metadata.get("generation_name", "litellm-completion"),
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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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prompt=input,
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completion=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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},
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metadata=metadata,
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)
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)
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def _log_langfuse_v2(
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self,
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user_id,
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metadata,
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litellm_params,
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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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import langfuse
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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_prompt = Version(langfuse.version.__version__) >= Version("2.7.3")
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supports_costs = Version(langfuse.version.__version__) >= Version("2.7.3")
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supports_completion_start_time = Version(
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langfuse.version.__version__
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) >= Version("2.7.3")
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print_verbose(f"Langfuse Layer Logging - logging to langfuse v2 ")
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if supports_tags:
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metadata_tags = metadata.get("tags", [])
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tags = metadata_tags
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trace_name = metadata.get("trace_name", None)
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trace_id = metadata.get("trace_id", None)
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existing_trace_id = metadata.get("existing_trace_id", None)
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if trace_name is None and existing_trace_id is None:
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# just log `litellm-{call_type}` as the trace name
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## DO NOT SET TRACE_NAME if trace-id set. this can lead to overwriting of past traces.
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trace_name = f"litellm-{kwargs.get('call_type', 'completion')}"
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trace_params = {
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"name": trace_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": trace_id or existing_trace_id,
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"session_id": metadata.get("session_id", None),
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}
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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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cost = kwargs.get("response_cost", None)
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print_verbose(f"trace: {cost}")
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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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# generate langfuse tags - Default Tags sent to Langfuse from LiteLLM Proxy
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if (
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litellm._langfuse_default_tags is not None
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and isinstance(litellm._langfuse_default_tags, list)
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and key in litellm._langfuse_default_tags
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):
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tags.append(f"{key}:{value}")
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# clean litellm metadata before logging
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if key in [
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"headers",
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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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if (
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litellm._langfuse_default_tags is not None
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and isinstance(litellm._langfuse_default_tags, list)
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and "proxy_base_url" in litellm._langfuse_default_tags
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):
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proxy_base_url = os.environ.get("PROXY_BASE_URL", None)
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if proxy_base_url is not None:
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tags.append(f"proxy_base_url:{proxy_base_url}")
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api_base = litellm_params.get("api_base", None)
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if api_base:
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clean_metadata["api_base"] = api_base
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vertex_location = kwargs.get("vertex_location", None)
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if vertex_location:
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clean_metadata["vertex_location"] = vertex_location
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aws_region_name = kwargs.get("aws_region_name", None)
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if aws_region_name:
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clean_metadata["aws_region_name"] = aws_region_name
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if supports_tags:
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if "cache_hit" in kwargs:
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if kwargs["cache_hit"] is None:
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kwargs["cache_hit"] = False
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tags.append(f"cache_hit:{kwargs['cache_hit']}")
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clean_metadata["cache_hit"] = kwargs["cache_hit"]
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trace_params.update({"tags": tags})
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proxy_server_request = litellm_params.get("proxy_server_request", None)
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if proxy_server_request:
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method = proxy_server_request.get("method", None)
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url = proxy_server_request.get("url", None)
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headers = proxy_server_request.get("headers", None)
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clean_headers = {}
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if headers:
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for key, value in headers.items():
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# these headers can leak our API keys and/or JWT tokens
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if key.lower() not in ["authorization", "cookie", "referer"]:
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clean_headers[key] = value
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clean_metadata["request"] = {
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"method": method,
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"url": url,
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"headers": clean_headers,
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}
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print_verbose(f"trace_params: {trace_params}")
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trace = self.Langfuse.trace(**trace_params)
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generation_id = None
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usage = None
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if response_obj is not None and 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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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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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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if response_obj is not None and "system_fingerprint" in response_obj:
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system_fingerprint = response_obj.get("system_fingerprint", None)
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else:
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system_fingerprint = None
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if system_fingerprint is not None:
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optional_params["system_fingerprint"] = system_fingerprint
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generation_params = {
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"name": generation_name,
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"id": metadata.get("generation_id", generation_id),
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"start_time": start_time,
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"end_time": end_time,
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"model": kwargs["model"],
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"model_parameters": optional_params,
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"input": input,
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"output": output,
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"usage": usage,
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"metadata": clean_metadata,
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"level": level,
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}
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if supports_prompt:
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generation_params["prompt"] = metadata.get("prompt", None)
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if output is not None and isinstance(output, str) and level == "ERROR":
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generation_params["status_message"] = output
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if supports_completion_start_time:
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generation_params["completion_start_time"] = kwargs.get(
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"completion_start_time", None
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)
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print_verbose(f"generation_params: {generation_params}")
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trace.generation(**generation_params)
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except Exception as e:
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verbose_logger.debug(f"Langfuse Layer Error - {traceback.format_exc()}")
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