#### What this does ####
# On success, logs events to Langfuse
import copy
import os
import traceback
from datetime import datetime
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union, cast
from packaging.version import Version
import litellm
from litellm._logging import verbose_logger
from litellm.litellm_core_utils.redact_messages import redact_user_api_key_info
from litellm.llms.custom_httpx.http_handler import _get_httpx_client
from litellm.secret_managers.main import str_to_bool
from litellm.types.integrations.langfuse import *
from litellm.types.llms.openai import HttpxBinaryResponseContent
from litellm.types.utils import (
EmbeddingResponse,
ImageResponse,
ModelResponse,
RerankResponse,
StandardLoggingPayload,
StandardLoggingPromptManagementMetadata,
TextCompletionResponse,
TranscriptionResponse,
)
if TYPE_CHECKING:
from litellm.litellm_core_utils.litellm_logging import DynamicLoggingCache
else:
DynamicLoggingCache = Any
class LangFuseLogger:
# Class variables or attributes
def __init__(
self,
langfuse_public_key=None,
langfuse_secret=None,
langfuse_host=None,
flush_interval=1,
):
try:
import langfuse
from langfuse import Langfuse
except Exception as e:
raise Exception(
f"\033[91mLangfuse not installed, try running 'pip install langfuse' to fix this error: {e}\n{traceback.format_exc()}\033[0m"
)
# Instance variables
self.secret_key = langfuse_secret or os.getenv("LANGFUSE_SECRET_KEY")
self.public_key = langfuse_public_key or os.getenv("LANGFUSE_PUBLIC_KEY")
self.langfuse_host = langfuse_host or os.getenv(
"LANGFUSE_HOST", "https://cloud.langfuse.com"
)
if not (
self.langfuse_host.startswith("http://")
or self.langfuse_host.startswith("https://")
):
# add http:// if unset, assume communicating over private network - e.g. render
self.langfuse_host = "http://" + self.langfuse_host
self.langfuse_release = os.getenv("LANGFUSE_RELEASE")
self.langfuse_debug = os.getenv("LANGFUSE_DEBUG")
self.langfuse_flush_interval = LangFuseLogger._get_langfuse_flush_interval(
flush_interval
)
http_client = _get_httpx_client()
self.langfuse_client = http_client.client
parameters = {
"public_key": self.public_key,
"secret_key": self.secret_key,
"host": self.langfuse_host,
"release": self.langfuse_release,
"debug": self.langfuse_debug,
"flush_interval": self.langfuse_flush_interval, # flush interval in seconds
"httpx_client": self.langfuse_client,
}
self.langfuse_sdk_version: str = langfuse.version.__version__
if Version(self.langfuse_sdk_version) >= Version("2.6.0"):
parameters["sdk_integration"] = "litellm"
self.Langfuse = Langfuse(**parameters)
# set the current langfuse project id in the environ
# this is used by Alerting to link to the correct project
try:
project_id = self.Langfuse.client.projects.get().data[0].id
os.environ["LANGFUSE_PROJECT_ID"] = project_id
except Exception:
project_id = None
if os.getenv("UPSTREAM_LANGFUSE_SECRET_KEY") is not None:
upstream_langfuse_debug = (
str_to_bool(self.upstream_langfuse_debug)
if self.upstream_langfuse_debug is not None
else None
)
self.upstream_langfuse_secret_key = os.getenv(
"UPSTREAM_LANGFUSE_SECRET_KEY"
)
self.upstream_langfuse_public_key = os.getenv(
"UPSTREAM_LANGFUSE_PUBLIC_KEY"
)
self.upstream_langfuse_host = os.getenv("UPSTREAM_LANGFUSE_HOST")
self.upstream_langfuse_release = os.getenv("UPSTREAM_LANGFUSE_RELEASE")
self.upstream_langfuse_debug = os.getenv("UPSTREAM_LANGFUSE_DEBUG")
self.upstream_langfuse = Langfuse(
public_key=self.upstream_langfuse_public_key,
secret_key=self.upstream_langfuse_secret_key,
host=self.upstream_langfuse_host,
release=self.upstream_langfuse_release,
debug=(
upstream_langfuse_debug
if upstream_langfuse_debug is not None
else False
),
)
else:
self.upstream_langfuse = None
@staticmethod
def add_metadata_from_header(litellm_params: dict, metadata: dict) -> dict:
"""
Adds metadata from proxy request headers to Langfuse logging if keys start with "langfuse_"
and overwrites litellm_params.metadata if already included.
For example if you want to append your trace to an existing `trace_id` via header, send
`headers: { ..., langfuse_existing_trace_id: your-existing-trace-id }` via proxy request.
"""
if litellm_params is None:
return metadata
if litellm_params.get("proxy_server_request") is None:
return metadata
if metadata is None:
metadata = {}
proxy_headers = (
litellm_params.get("proxy_server_request", {}).get("headers", {}) or {}
)
for metadata_param_key in proxy_headers:
if metadata_param_key.startswith("langfuse_"):
trace_param_key = metadata_param_key.replace("langfuse_", "", 1)
if trace_param_key in metadata:
verbose_logger.warning(
f"Overwriting Langfuse `{trace_param_key}` from request header"
)
else:
verbose_logger.debug(
f"Found Langfuse `{trace_param_key}` in request header"
)
metadata[trace_param_key] = proxy_headers.get(metadata_param_key)
return metadata
def log_event_on_langfuse(
self,
kwargs: dict,
response_obj: Union[
None,
dict,
EmbeddingResponse,
ModelResponse,
TextCompletionResponse,
ImageResponse,
TranscriptionResponse,
RerankResponse,
HttpxBinaryResponseContent,
],
start_time: Optional[datetime] = None,
end_time: Optional[datetime] = None,
user_id: Optional[str] = None,
level: str = "DEFAULT",
status_message: Optional[str] = None,
) -> dict:
"""
Logs a success or error event on Langfuse
"""
try:
verbose_logger.debug(
f"Langfuse Logging - Enters logging function for model {kwargs}"
)
# set default values for input/output for langfuse logging
input = None
output = None
litellm_params = kwargs.get("litellm_params", {})
litellm_call_id = kwargs.get("litellm_call_id", None)
metadata = (
litellm_params.get("metadata", {}) or {}
) # if litellm_params['metadata'] == None
metadata = self.add_metadata_from_header(litellm_params, metadata)
optional_params = copy.deepcopy(kwargs.get("optional_params", {}))
prompt = {"messages": kwargs.get("messages")}
functions = optional_params.pop("functions", None)
tools = optional_params.pop("tools", None)
if functions is not None:
prompt["functions"] = functions
if tools is not None:
prompt["tools"] = tools
# langfuse only accepts str, int, bool, float for logging
for param, value in optional_params.items():
if not isinstance(value, (str, int, bool, float)):
try:
optional_params[param] = str(value)
except Exception:
# if casting value to str fails don't block logging
pass
input, output = self._get_langfuse_input_output_content(
kwargs=kwargs,
response_obj=response_obj,
prompt=prompt,
level=level,
status_message=status_message,
)
verbose_logger.debug(
f"OUTPUT IN LANGFUSE: {output}; original: {response_obj}"
)
trace_id = None
generation_id = None
if self._is_langfuse_v2():
trace_id, generation_id = self._log_langfuse_v2(
user_id=user_id,
metadata=metadata,
litellm_params=litellm_params,
output=output,
start_time=start_time,
end_time=end_time,
kwargs=kwargs,
optional_params=optional_params,
input=input,
response_obj=response_obj,
level=level,
litellm_call_id=litellm_call_id,
)
elif response_obj is not None:
self._log_langfuse_v1(
user_id=user_id,
metadata=metadata,
output=output,
start_time=start_time,
end_time=end_time,
kwargs=kwargs,
optional_params=optional_params,
input=input,
response_obj=response_obj,
)
verbose_logger.debug(
f"Langfuse Layer Logging - final response object: {response_obj}"
)
verbose_logger.info("Langfuse Layer Logging - logging success")
return {"trace_id": trace_id, "generation_id": generation_id}
except Exception as e:
verbose_logger.exception(
"Langfuse Layer Error(): Exception occured - {}".format(str(e))
)
return {"trace_id": None, "generation_id": None}
def _get_langfuse_input_output_content(
self,
kwargs: dict,
response_obj: Union[
None,
dict,
EmbeddingResponse,
ModelResponse,
TextCompletionResponse,
ImageResponse,
TranscriptionResponse,
RerankResponse,
HttpxBinaryResponseContent,
],
prompt: dict,
level: str,
status_message: Optional[str],
) -> Tuple[Optional[dict], Optional[Union[str, dict, list]]]:
"""
Get the input and output content for Langfuse logging
Args:
kwargs: The keyword arguments passed to the function
response_obj: The response object returned by the function
prompt: The prompt used to generate the response
level: The level of the log message
status_message: The status message of the log message
Returns:
input: The input content for Langfuse logging
output: The output content for Langfuse logging
"""
input = None
output: Optional[Union[str, dict, List[Any]]] = None
if (
level == "ERROR"
and status_message is not None
and isinstance(status_message, str)
):
input = prompt
output = status_message
elif response_obj is not None and (
kwargs.get("call_type", None) == "embedding"
or isinstance(response_obj, litellm.EmbeddingResponse)
):
input = prompt
output = None
elif response_obj is not None and isinstance(
response_obj, litellm.ModelResponse
):
input = prompt
output = self._get_chat_content_for_langfuse(response_obj)
elif response_obj is not None and isinstance(
response_obj, litellm.HttpxBinaryResponseContent
):
input = prompt
output = "speech-output"
elif response_obj is not None and isinstance(
response_obj, litellm.TextCompletionResponse
):
input = prompt
output = self._get_text_completion_content_for_langfuse(response_obj)
elif response_obj is not None and isinstance(
response_obj, litellm.ImageResponse
):
input = prompt
output = response_obj.get("data", None)
elif response_obj is not None and isinstance(
response_obj, litellm.TranscriptionResponse
):
input = prompt
output = response_obj.get("text", None)
elif response_obj is not None and isinstance(
response_obj, litellm.RerankResponse
):
input = prompt
output = response_obj.results
elif (
kwargs.get("call_type") is not None
and kwargs.get("call_type") == "_arealtime"
and response_obj is not None
and isinstance(response_obj, list)
):
input = kwargs.get("input")
output = response_obj
elif (
kwargs.get("call_type") is not None
and kwargs.get("call_type") == "pass_through_endpoint"
and response_obj is not None
and isinstance(response_obj, dict)
):
input = prompt
output = response_obj.get("response", "")
return input, output
async def _async_log_event(
self, kwargs, response_obj, start_time, end_time, user_id
):
"""
Langfuse SDK uses a background thread to log events
This approach does not impact latency and runs in the background
"""
def _is_langfuse_v2(self):
import langfuse
return Version(langfuse.version.__version__) >= Version("2.0.0")
def _log_langfuse_v1(
self,
user_id,
metadata,
output,
start_time,
end_time,
kwargs,
optional_params,
input,
response_obj,
):
from langfuse.model import CreateGeneration, CreateTrace # type: ignore
verbose_logger.warning(
"Please upgrade langfuse to v2.0.0 or higher: https://github.com/langfuse/langfuse-python/releases/tag/v2.0.1"
)
trace = self.Langfuse.trace( # type: ignore
CreateTrace( # type: ignore
name=metadata.get("generation_name", "litellm-completion"),
input=input,
output=output,
userId=user_id,
)
)
trace.generation(
CreateGeneration(
name=metadata.get("generation_name", "litellm-completion"),
startTime=start_time,
endTime=end_time,
model=kwargs["model"],
modelParameters=optional_params,
prompt=input,
completion=output,
usage={
"prompt_tokens": response_obj.usage.prompt_tokens,
"completion_tokens": response_obj.usage.completion_tokens,
},
metadata=metadata,
)
)
def _log_langfuse_v2( # noqa: PLR0915
self,
user_id: Optional[str],
metadata: dict,
litellm_params: dict,
output: Optional[Union[str, dict, list]],
start_time: Optional[datetime],
end_time: Optional[datetime],
kwargs: dict,
optional_params: dict,
input: Optional[dict],
response_obj,
level: str,
litellm_call_id: Optional[str],
) -> tuple:
verbose_logger.debug("Langfuse Layer Logging - logging to langfuse v2")
try:
metadata = metadata or {}
standard_logging_object: Optional[StandardLoggingPayload] = cast(
Optional[StandardLoggingPayload],
kwargs.get("standard_logging_object", None),
)
tags = (
self._get_langfuse_tags(standard_logging_object=standard_logging_object)
if self._supports_tags()
else []
)
if standard_logging_object is None:
end_user_id = None
prompt_management_metadata: Optional[
StandardLoggingPromptManagementMetadata
] = None
else:
end_user_id = standard_logging_object["metadata"].get(
"user_api_key_end_user_id", None
)
prompt_management_metadata = cast(
Optional[StandardLoggingPromptManagementMetadata],
standard_logging_object["metadata"].get(
"prompt_management_metadata", None
),
)
# Clean Metadata before logging - never log raw metadata
# the raw metadata can contain circular references which leads to infinite recursion
# we clean out all extra litellm metadata params before logging
clean_metadata: Dict[str, Any] = {}
if prompt_management_metadata is not None:
clean_metadata["prompt_management_metadata"] = (
prompt_management_metadata
)
if isinstance(metadata, dict):
for key, value in metadata.items():
# generate langfuse tags - Default Tags sent to Langfuse from LiteLLM Proxy
if (
litellm.langfuse_default_tags is not None
and isinstance(litellm.langfuse_default_tags, list)
and key in litellm.langfuse_default_tags
):
tags.append(f"{key}:{value}")
# clean litellm metadata before logging
if key in [
"headers",
"endpoint",
"caching_groups",
"previous_models",
]:
continue
else:
clean_metadata[key] = value
# Add default langfuse tags
tags = self.add_default_langfuse_tags(
tags=tags, kwargs=kwargs, metadata=metadata
)
session_id = clean_metadata.pop("session_id", None)
trace_name = cast(Optional[str], clean_metadata.pop("trace_name", None))
trace_id = clean_metadata.pop("trace_id", litellm_call_id)
existing_trace_id = clean_metadata.pop("existing_trace_id", None)
update_trace_keys = cast(list, clean_metadata.pop("update_trace_keys", []))
debug = clean_metadata.pop("debug_langfuse", None)
mask_input = clean_metadata.pop("mask_input", False)
mask_output = clean_metadata.pop("mask_output", False)
clean_metadata = redact_user_api_key_info(metadata=clean_metadata)
if trace_name is None and existing_trace_id is None:
# just log `litellm-{call_type}` as the trace name
## DO NOT SET TRACE_NAME if trace-id set. this can lead to overwriting of past traces.
trace_name = f"litellm-{kwargs.get('call_type', 'completion')}"
if existing_trace_id is not None:
trace_params: Dict[str, Any] = {"id": existing_trace_id}
# Update the following keys for this trace
for metadata_param_key in update_trace_keys:
trace_param_key = metadata_param_key.replace("trace_", "")
if trace_param_key not in trace_params:
updated_trace_value = clean_metadata.pop(
metadata_param_key, None
)
if updated_trace_value is not None:
trace_params[trace_param_key] = updated_trace_value
# Pop the trace specific keys that would have been popped if there were a new trace
for key in list(
filter(lambda key: key.startswith("trace_"), clean_metadata.keys())
):
clean_metadata.pop(key, None)
# Special keys that are found in the function arguments and not the metadata
if "input" in update_trace_keys:
trace_params["input"] = (
input if not mask_input else "redacted-by-litellm"
)
if "output" in update_trace_keys:
trace_params["output"] = (
output if not mask_output else "redacted-by-litellm"
)
else: # don't overwrite an existing trace
trace_params = {
"id": trace_id,
"name": trace_name,
"session_id": session_id,
"input": input if not mask_input else "redacted-by-litellm",
"version": clean_metadata.pop(
"trace_version", clean_metadata.get("version", None)
), # If provided just version, it will applied to the trace as well, if applied a trace version it will take precedence
"user_id": end_user_id,
}
for key in list(
filter(lambda key: key.startswith("trace_"), clean_metadata.keys())
):
trace_params[key.replace("trace_", "")] = clean_metadata.pop(
key, None
)
if level == "ERROR":
trace_params["status_message"] = output
else:
trace_params["output"] = (
output if not mask_output else "redacted-by-litellm"
)
if debug is True or (isinstance(debug, str) and debug.lower() == "true"):
if "metadata" in trace_params:
# log the raw_metadata in the trace
trace_params["metadata"]["metadata_passed_to_litellm"] = metadata
else:
trace_params["metadata"] = {"metadata_passed_to_litellm": metadata}
cost = kwargs.get("response_cost", None)
verbose_logger.debug(f"trace: {cost}")
clean_metadata["litellm_response_cost"] = cost
if standard_logging_object is not None:
clean_metadata["hidden_params"] = standard_logging_object[
"hidden_params"
]
if (
litellm.langfuse_default_tags is not None
and isinstance(litellm.langfuse_default_tags, list)
and "proxy_base_url" in litellm.langfuse_default_tags
):
proxy_base_url = os.environ.get("PROXY_BASE_URL", None)
if proxy_base_url is not None:
tags.append(f"proxy_base_url:{proxy_base_url}")
api_base = litellm_params.get("api_base", None)
if api_base:
clean_metadata["api_base"] = api_base
vertex_location = kwargs.get("vertex_location", None)
if vertex_location:
clean_metadata["vertex_location"] = vertex_location
aws_region_name = kwargs.get("aws_region_name", None)
if aws_region_name:
clean_metadata["aws_region_name"] = aws_region_name
if self._supports_tags():
if "cache_hit" in kwargs:
if kwargs["cache_hit"] is None:
kwargs["cache_hit"] = False
clean_metadata["cache_hit"] = kwargs["cache_hit"]
if existing_trace_id is None:
trace_params.update({"tags": tags})
proxy_server_request = litellm_params.get("proxy_server_request", None)
if proxy_server_request:
proxy_server_request.get("method", None)
proxy_server_request.get("url", None)
headers = proxy_server_request.get("headers", None)
clean_headers = {}
if headers:
for key, value in headers.items():
# these headers can leak our API keys and/or JWT tokens
if key.lower() not in ["authorization", "cookie", "referer"]:
clean_headers[key] = value
# clean_metadata["request"] = {
# "method": method,
# "url": url,
# "headers": clean_headers,
# }
trace = self.Langfuse.trace(**trace_params)
# Log provider specific information as a span
log_provider_specific_information_as_span(trace, clean_metadata)
generation_id = None
usage = None
if response_obj is not None:
if (
hasattr(response_obj, "id")
and response_obj.get("id", None) is not None
):
generation_id = litellm.utils.get_logging_id(
start_time, response_obj
)
_usage_obj = getattr(response_obj, "usage", None)
if _usage_obj:
usage = {
"prompt_tokens": _usage_obj.prompt_tokens,
"completion_tokens": _usage_obj.completion_tokens,
"total_cost": cost if self._supports_costs() else None,
}
generation_name = clean_metadata.pop("generation_name", None)
if generation_name is None:
# if `generation_name` is None, use sensible default values
# If using litellm proxy user `key_alias` if not None
# If `key_alias` is None, just log `litellm-{call_type}` as the generation name
_user_api_key_alias = cast(
Optional[str], clean_metadata.get("user_api_key_alias", None)
)
generation_name = (
f"litellm-{cast(str, kwargs.get('call_type', 'completion'))}"
)
if _user_api_key_alias is not None:
generation_name = f"litellm:{_user_api_key_alias}"
if response_obj is not None:
system_fingerprint = getattr(response_obj, "system_fingerprint", None)
else:
system_fingerprint = None
if system_fingerprint is not None:
optional_params["system_fingerprint"] = system_fingerprint
generation_params = {
"name": generation_name,
"id": clean_metadata.pop("generation_id", generation_id),
"start_time": start_time,
"end_time": end_time,
"model": kwargs["model"],
"model_parameters": optional_params,
"input": input if not mask_input else "redacted-by-litellm",
"output": output if not mask_output else "redacted-by-litellm",
"usage": usage,
"metadata": log_requester_metadata(clean_metadata),
"level": level,
"version": clean_metadata.pop("version", None),
}
parent_observation_id = metadata.get("parent_observation_id", None)
if parent_observation_id is not None:
generation_params["parent_observation_id"] = parent_observation_id
if self._supports_prompt():
generation_params = _add_prompt_to_generation_params(
generation_params=generation_params,
clean_metadata=clean_metadata,
prompt_management_metadata=prompt_management_metadata,
langfuse_client=self.Langfuse,
)
if output is not None and isinstance(output, str) and level == "ERROR":
generation_params["status_message"] = output
if self._supports_completion_start_time():
generation_params["completion_start_time"] = kwargs.get(
"completion_start_time", None
)
generation_client = trace.generation(**generation_params)
return generation_client.trace_id, generation_id
except Exception:
verbose_logger.error(f"Langfuse Layer Error - {traceback.format_exc()}")
return None, None
@staticmethod
def _get_chat_content_for_langfuse(
response_obj: ModelResponse,
):
"""
Get the chat content for Langfuse logging
"""
if response_obj.choices and len(response_obj.choices) > 0:
output = response_obj["choices"][0]["message"].json()
return output
else:
return None
@staticmethod
def _get_text_completion_content_for_langfuse(
response_obj: TextCompletionResponse,
):
"""
Get the text completion content for Langfuse logging
"""
if response_obj.choices and len(response_obj.choices) > 0:
return response_obj.choices[0].text
else:
return None
@staticmethod
def _get_langfuse_tags(
standard_logging_object: Optional[StandardLoggingPayload],
) -> List[str]:
if standard_logging_object is None:
return []
return standard_logging_object.get("request_tags", []) or []
def add_default_langfuse_tags(self, tags, kwargs, metadata):
"""
Helper function to add litellm default langfuse tags
- Special LiteLLM tags:
- cache_hit
- cache_key
"""
if litellm.langfuse_default_tags is not None and isinstance(
litellm.langfuse_default_tags, list
):
if "cache_hit" in litellm.langfuse_default_tags:
_cache_hit_value = kwargs.get("cache_hit", False)
tags.append(f"cache_hit:{_cache_hit_value}")
if "cache_key" in litellm.langfuse_default_tags:
_hidden_params = metadata.get("hidden_params", {}) or {}
_cache_key = _hidden_params.get("cache_key", None)
if _cache_key is None and litellm.cache is not None:
# fallback to using "preset_cache_key"
_preset_cache_key = litellm.cache._get_preset_cache_key_from_kwargs(
**kwargs
)
_cache_key = _preset_cache_key
tags.append(f"cache_key:{_cache_key}")
return tags
def _supports_tags(self):
"""Check if current langfuse version supports tags"""
return Version(self.langfuse_sdk_version) >= Version("2.6.3")
def _supports_prompt(self):
"""Check if current langfuse version supports prompt"""
return Version(self.langfuse_sdk_version) >= Version("2.7.3")
def _supports_costs(self):
"""Check if current langfuse version supports costs"""
return Version(self.langfuse_sdk_version) >= Version("2.7.3")
def _supports_completion_start_time(self):
"""Check if current langfuse version supports completion start time"""
return Version(self.langfuse_sdk_version) >= Version("2.7.3")
@staticmethod
def _get_langfuse_flush_interval(flush_interval: int) -> int:
"""
Get the langfuse flush interval to initialize the Langfuse client
Reads `LANGFUSE_FLUSH_INTERVAL` from the environment variable.
If not set, uses the flush interval passed in as an argument.
Args:
flush_interval: The flush interval to use if LANGFUSE_FLUSH_INTERVAL is not set
Returns:
[int] The flush interval to use to initialize the Langfuse client
"""
return int(os.getenv("LANGFUSE_FLUSH_INTERVAL") or flush_interval)
def _add_prompt_to_generation_params(
generation_params: dict,
clean_metadata: dict,
prompt_management_metadata: Optional[StandardLoggingPromptManagementMetadata],
langfuse_client: Any,
) -> dict:
from langfuse import Langfuse
from langfuse.model import (
ChatPromptClient,
Prompt_Chat,
Prompt_Text,
TextPromptClient,
)
langfuse_client = cast(Langfuse, langfuse_client)
user_prompt = clean_metadata.pop("prompt", None)
if user_prompt is None and prompt_management_metadata is None:
pass
elif isinstance(user_prompt, dict):
if user_prompt.get("type", "") == "chat":
_prompt_chat = Prompt_Chat(**user_prompt)
generation_params["prompt"] = ChatPromptClient(prompt=_prompt_chat)
elif user_prompt.get("type", "") == "text":
_prompt_text = Prompt_Text(**user_prompt)
generation_params["prompt"] = TextPromptClient(prompt=_prompt_text)
elif "version" in user_prompt and "prompt" in user_prompt:
# prompts
if isinstance(user_prompt["prompt"], str):
prompt_text_params = getattr(
Prompt_Text, "model_fields", Prompt_Text.__fields__
)
_data = {
"name": user_prompt["name"],
"prompt": user_prompt["prompt"],
"version": user_prompt["version"],
"config": user_prompt.get("config", None),
}
if "labels" in prompt_text_params and "tags" in prompt_text_params:
_data["labels"] = user_prompt.get("labels", []) or []
_data["tags"] = user_prompt.get("tags", []) or []
_prompt_obj = Prompt_Text(**_data) # type: ignore
generation_params["prompt"] = TextPromptClient(prompt=_prompt_obj)
elif isinstance(user_prompt["prompt"], list):
prompt_chat_params = getattr(
Prompt_Chat, "model_fields", Prompt_Chat.__fields__
)
_data = {
"name": user_prompt["name"],
"prompt": user_prompt["prompt"],
"version": user_prompt["version"],
"config": user_prompt.get("config", None),
}
if "labels" in prompt_chat_params and "tags" in prompt_chat_params:
_data["labels"] = user_prompt.get("labels", []) or []
_data["tags"] = user_prompt.get("tags", []) or []
_prompt_obj = Prompt_Chat(**_data) # type: ignore
generation_params["prompt"] = ChatPromptClient(prompt=_prompt_obj)
else:
verbose_logger.error(
"[Non-blocking] Langfuse Logger: Invalid prompt format"
)
else:
verbose_logger.error(
"[Non-blocking] Langfuse Logger: Invalid prompt format. No prompt logged to Langfuse"
)
elif (
prompt_management_metadata is not None
and prompt_management_metadata["prompt_integration"] == "langfuse"
):
try:
generation_params["prompt"] = langfuse_client.get_prompt(
prompt_management_metadata["prompt_id"]
)
except Exception as e:
verbose_logger.debug(
f"[Non-blocking] Langfuse Logger: Error getting prompt client for logging: {e}"
)
pass
else:
generation_params["prompt"] = user_prompt
return generation_params
def log_provider_specific_information_as_span(
trace,
clean_metadata,
):
"""
Logs provider-specific information as spans.
Parameters:
trace: The tracing object used to log spans.
clean_metadata: A dictionary containing metadata to be logged.
Returns:
None
"""
_hidden_params = clean_metadata.get("hidden_params", None)
if _hidden_params is None:
return
vertex_ai_grounding_metadata = _hidden_params.get(
"vertex_ai_grounding_metadata", None
)
if vertex_ai_grounding_metadata is not None:
if isinstance(vertex_ai_grounding_metadata, list):
for elem in vertex_ai_grounding_metadata:
if isinstance(elem, dict):
for key, value in elem.items():
trace.span(
name=key,
input=value,
)
else:
trace.span(
name="vertex_ai_grounding_metadata",
input=elem,
)
else:
trace.span(
name="vertex_ai_grounding_metadata",
input=vertex_ai_grounding_metadata,
)
def log_requester_metadata(clean_metadata: dict):
returned_metadata = {}
requester_metadata = clean_metadata.get("requester_metadata") or {}
for k, v in clean_metadata.items():
if k not in requester_metadata:
returned_metadata[k] = v
returned_metadata.update({"requester_metadata": requester_metadata})
return returned_metadata
