# used for /metrics endpoint on LiteLLM Proxy
#### What this does ####
# On success, log events to Prometheus
import asyncio
import sys
from datetime import datetime, timedelta
from typing import Any, Awaitable, Callable, List, Literal, Optional, Tuple, cast
import litellm
from litellm._logging import print_verbose, verbose_logger
from litellm.integrations.custom_logger import CustomLogger
from litellm.proxy._types import LiteLLM_TeamTable, UserAPIKeyAuth
from litellm.types.integrations.prometheus import *
from litellm.types.utils import StandardLoggingPayload
from litellm.utils import get_end_user_id_for_cost_tracking
class PrometheusLogger(CustomLogger):
# Class variables or attributes
def __init__(
self,
**kwargs,
):
try:
from prometheus_client import Counter, Gauge, Histogram
from litellm.proxy.proxy_server import CommonProxyErrors, premium_user
if premium_user is not True:
verbose_logger.warning(
f"🚨🚨🚨 Prometheus Metrics is on LiteLLM Enterprise\n🚨 {CommonProxyErrors.not_premium_user.value}"
)
self.litellm_not_a_premium_user_metric = Counter(
name="litellm_not_a_premium_user_metric",
documentation=f"🚨🚨🚨 Prometheus Metrics is on LiteLLM Enterprise. 🚨 {CommonProxyErrors.not_premium_user.value}",
)
return
self.litellm_proxy_failed_requests_metric = Counter(
name="litellm_proxy_failed_requests_metric",
documentation="Total number of failed responses from proxy - the client did not get a success response from litellm proxy",
labelnames=PrometheusMetricLabels.get_labels(
label_name="litellm_proxy_failed_requests_metric"
),
)
self.litellm_proxy_total_requests_metric = Counter(
name="litellm_proxy_total_requests_metric",
documentation="Total number of requests made to the proxy server - track number of client side requests",
labelnames=PrometheusMetricLabels.get_labels(
label_name="litellm_proxy_total_requests_metric"
),
)
# request latency metrics
self.litellm_request_total_latency_metric = Histogram(
"litellm_request_total_latency_metric",
"Total latency (seconds) for a request to LiteLLM",
labelnames=PrometheusMetricLabels.get_labels(
label_name="litellm_request_total_latency_metric"
),
buckets=LATENCY_BUCKETS,
)
self.litellm_llm_api_latency_metric = Histogram(
"litellm_llm_api_latency_metric",
"Total latency (seconds) for a models LLM API call",
labelnames=PrometheusMetricLabels.get_labels(
label_name="litellm_llm_api_latency_metric"
),
buckets=LATENCY_BUCKETS,
)
self.litellm_llm_api_time_to_first_token_metric = Histogram(
"litellm_llm_api_time_to_first_token_metric",
"Time to first token for a models LLM API call",
labelnames=[
"model",
"hashed_api_key",
"api_key_alias",
"team",
"team_alias",
],
buckets=LATENCY_BUCKETS,
)
# Counter for spend
self.litellm_spend_metric = Counter(
"litellm_spend_metric",
"Total spend on LLM requests",
labelnames=[
"end_user",
"hashed_api_key",
"api_key_alias",
"model",
"team",
"team_alias",
"user",
],
)
# Counter for total_output_tokens
self.litellm_tokens_metric = Counter(
"litellm_total_tokens",
"Total number of input + output tokens from LLM requests",
labelnames=[
"end_user",
"hashed_api_key",
"api_key_alias",
"model",
"team",
"team_alias",
"user",
],
)
self.litellm_input_tokens_metric = Counter(
"litellm_input_tokens",
"Total number of input tokens from LLM requests",
labelnames=PrometheusMetricLabels.get_labels(
label_name="litellm_input_tokens_metric"
),
)
self.litellm_output_tokens_metric = Counter(
"litellm_output_tokens",
"Total number of output tokens from LLM requests",
labelnames=PrometheusMetricLabels.get_labels(
label_name="litellm_output_tokens_metric"
),
)
# Remaining Budget for Team
self.litellm_remaining_team_budget_metric = Gauge(
"litellm_remaining_team_budget_metric",
"Remaining budget for team",
labelnames=PrometheusMetricLabels.get_labels(
label_name="litellm_remaining_team_budget_metric"
),
)
# Max Budget for Team
self.litellm_team_max_budget_metric = Gauge(
"litellm_team_max_budget_metric",
"Maximum budget set for team",
labelnames=PrometheusMetricLabels.get_labels(
label_name="litellm_team_max_budget_metric"
),
)
# Team Budget Reset At
self.litellm_team_budget_remaining_hours_metric = Gauge(
"litellm_team_budget_remaining_hours_metric",
"Remaining days for team budget to be reset",
labelnames=PrometheusMetricLabels.get_labels(
label_name="litellm_team_budget_remaining_hours_metric"
),
)
# Remaining Budget for API Key
self.litellm_remaining_api_key_budget_metric = Gauge(
"litellm_remaining_api_key_budget_metric",
"Remaining budget for api key",
labelnames=PrometheusMetricLabels.get_labels(
label_name="litellm_remaining_api_key_budget_metric"
),
)
# Max Budget for API Key
self.litellm_api_key_max_budget_metric = Gauge(
"litellm_api_key_max_budget_metric",
"Maximum budget set for api key",
labelnames=PrometheusMetricLabels.get_labels(
label_name="litellm_api_key_max_budget_metric"
),
)
self.litellm_api_key_budget_remaining_hours_metric = Gauge(
"litellm_api_key_budget_remaining_hours_metric",
"Remaining hours for api key budget to be reset",
labelnames=PrometheusMetricLabels.get_labels(
label_name="litellm_api_key_budget_remaining_hours_metric"
),
)
########################################
# LiteLLM Virtual API KEY metrics
########################################
# Remaining MODEL RPM limit for API Key
self.litellm_remaining_api_key_requests_for_model = Gauge(
"litellm_remaining_api_key_requests_for_model",
"Remaining Requests API Key can make for model (model based rpm limit on key)",
labelnames=["hashed_api_key", "api_key_alias", "model"],
)
# Remaining MODEL TPM limit for API Key
self.litellm_remaining_api_key_tokens_for_model = Gauge(
"litellm_remaining_api_key_tokens_for_model",
"Remaining Tokens API Key can make for model (model based tpm limit on key)",
labelnames=["hashed_api_key", "api_key_alias", "model"],
)
########################################
# LLM API Deployment Metrics / analytics
########################################
# Remaining Rate Limit for model
self.litellm_remaining_requests_metric = Gauge(
"litellm_remaining_requests",
"LLM Deployment Analytics - remaining requests for model, returned from LLM API Provider",
labelnames=[
"model_group",
"api_provider",
"api_base",
"litellm_model_name",
"hashed_api_key",
"api_key_alias",
],
)
self.litellm_remaining_tokens_metric = Gauge(
"litellm_remaining_tokens",
"remaining tokens for model, returned from LLM API Provider",
labelnames=[
"model_group",
"api_provider",
"api_base",
"litellm_model_name",
"hashed_api_key",
"api_key_alias",
],
)
self.litellm_overhead_latency_metric = Histogram(
"litellm_overhead_latency_metric",
"Latency overhead (milliseconds) added by LiteLLM processing",
labelnames=[
"model_group",
"api_provider",
"api_base",
"litellm_model_name",
"hashed_api_key",
"api_key_alias",
],
buckets=LATENCY_BUCKETS,
)
# llm api provider budget metrics
self.litellm_provider_remaining_budget_metric = Gauge(
"litellm_provider_remaining_budget_metric",
"Remaining budget for provider - used when you set provider budget limits",
labelnames=["api_provider"],
)
# Get all keys
_logged_llm_labels = [
UserAPIKeyLabelNames.v2_LITELLM_MODEL_NAME.value,
UserAPIKeyLabelNames.MODEL_ID.value,
UserAPIKeyLabelNames.API_BASE.value,
UserAPIKeyLabelNames.API_PROVIDER.value,
]
team_and_key_labels = [
"hashed_api_key",
"api_key_alias",
"team",
"team_alias",
]
# Metric for deployment state
self.litellm_deployment_state = Gauge(
"litellm_deployment_state",
"LLM Deployment Analytics - The state of the deployment: 0 = healthy, 1 = partial outage, 2 = complete outage",
labelnames=_logged_llm_labels,
)
self.litellm_deployment_cooled_down = Counter(
"litellm_deployment_cooled_down",
"LLM Deployment Analytics - Number of times a deployment has been cooled down by LiteLLM load balancing logic. exception_status is the status of the exception that caused the deployment to be cooled down",
labelnames=_logged_llm_labels + [EXCEPTION_STATUS],
)
self.litellm_deployment_success_responses = Counter(
name="litellm_deployment_success_responses",
documentation="LLM Deployment Analytics - Total number of successful LLM API calls via litellm",
labelnames=[REQUESTED_MODEL] + _logged_llm_labels + team_and_key_labels,
)
self.litellm_deployment_failure_responses = Counter(
name="litellm_deployment_failure_responses",
documentation="LLM Deployment Analytics - Total number of failed LLM API calls for a specific LLM deploymeny. exception_status is the status of the exception from the llm api",
labelnames=[REQUESTED_MODEL]
+ _logged_llm_labels
+ EXCEPTION_LABELS
+ team_and_key_labels,
)
self.litellm_deployment_failure_by_tag_responses = Counter(
"litellm_deployment_failure_by_tag_responses",
"Total number of failed LLM API calls for a specific LLM deploymeny by custom metadata tags",
labelnames=[
UserAPIKeyLabelNames.REQUESTED_MODEL.value,
UserAPIKeyLabelNames.TAG.value,
]
+ _logged_llm_labels
+ EXCEPTION_LABELS,
)
self.litellm_deployment_total_requests = Counter(
name="litellm_deployment_total_requests",
documentation="LLM Deployment Analytics - Total number of LLM API calls via litellm - success + failure",
labelnames=[REQUESTED_MODEL] + _logged_llm_labels + team_and_key_labels,
)
# Deployment Latency tracking
team_and_key_labels = [
"hashed_api_key",
"api_key_alias",
"team",
"team_alias",
]
self.litellm_deployment_latency_per_output_token = Histogram(
name="litellm_deployment_latency_per_output_token",
documentation="LLM Deployment Analytics - Latency per output token",
labelnames=PrometheusMetricLabels.get_labels(
label_name="litellm_deployment_latency_per_output_token"
),
)
self.litellm_deployment_successful_fallbacks = Counter(
"litellm_deployment_successful_fallbacks",
"LLM Deployment Analytics - Number of successful fallback requests from primary model -> fallback model",
PrometheusMetricLabels.get_labels(
"litellm_deployment_successful_fallbacks"
),
)
self.litellm_deployment_failed_fallbacks = Counter(
"litellm_deployment_failed_fallbacks",
"LLM Deployment Analytics - Number of failed fallback requests from primary model -> fallback model",
PrometheusMetricLabels.get_labels(
"litellm_deployment_failed_fallbacks"
),
)
self.litellm_llm_api_failed_requests_metric = Counter(
name="litellm_llm_api_failed_requests_metric",
documentation="deprecated - use litellm_proxy_failed_requests_metric",
labelnames=[
"end_user",
"hashed_api_key",
"api_key_alias",
"model",
"team",
"team_alias",
"user",
],
)
self.litellm_requests_metric = Counter(
name="litellm_requests_metric",
documentation="deprecated - use litellm_proxy_total_requests_metric. Total number of LLM calls to litellm - track total per API Key, team, user",
labelnames=PrometheusMetricLabels.get_labels(
label_name="litellm_requests_metric"
),
)
self._initialize_prometheus_startup_metrics()
except Exception as e:
print_verbose(f"Got exception on init prometheus client {str(e)}")
raise e
async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
# Define prometheus client
from litellm.types.utils import StandardLoggingPayload
verbose_logger.debug(
f"prometheus Logging - Enters success logging function for kwargs {kwargs}"
)
# unpack kwargs
standard_logging_payload: Optional[StandardLoggingPayload] = kwargs.get(
"standard_logging_object"
)
if standard_logging_payload is None or not isinstance(
standard_logging_payload, dict
):
raise ValueError(
f"standard_logging_object is required, got={standard_logging_payload}"
)
model = kwargs.get("model", "")
litellm_params = kwargs.get("litellm_params", {}) or {}
_metadata = litellm_params.get("metadata", {})
end_user_id = get_end_user_id_for_cost_tracking(
litellm_params, service_type="prometheus"
)
user_id = standard_logging_payload["metadata"]["user_api_key_user_id"]
user_api_key = standard_logging_payload["metadata"]["user_api_key_hash"]
user_api_key_alias = standard_logging_payload["metadata"]["user_api_key_alias"]
user_api_team = standard_logging_payload["metadata"]["user_api_key_team_id"]
user_api_team_alias = standard_logging_payload["metadata"][
"user_api_key_team_alias"
]
output_tokens = standard_logging_payload["completion_tokens"]
tokens_used = standard_logging_payload["total_tokens"]
response_cost = standard_logging_payload["response_cost"]
_requester_metadata = standard_logging_payload["metadata"].get(
"requester_metadata"
)
if standard_logging_payload is not None and isinstance(
standard_logging_payload, dict
):
_tags = standard_logging_payload["request_tags"]
else:
_tags = []
print_verbose(
f"inside track_prometheus_metrics, model {model}, response_cost {response_cost}, tokens_used {tokens_used}, end_user_id {end_user_id}, user_api_key {user_api_key}"
)
enum_values = UserAPIKeyLabelValues(
end_user=end_user_id,
hashed_api_key=user_api_key,
api_key_alias=user_api_key_alias,
requested_model=standard_logging_payload["model_group"],
team=user_api_team,
team_alias=user_api_team_alias,
user=user_id,
user_email=standard_logging_payload["metadata"]["user_api_key_user_email"],
status_code="200",
model=model,
litellm_model_name=model,
tags=_tags,
model_id=standard_logging_payload["model_id"],
api_base=standard_logging_payload["api_base"],
api_provider=standard_logging_payload["custom_llm_provider"],
exception_status=None,
exception_class=None,
custom_metadata_labels=get_custom_labels_from_metadata(
metadata=standard_logging_payload["metadata"].get("requester_metadata")
or {}
),
)
if (
user_api_key is not None
and isinstance(user_api_key, str)
and user_api_key.startswith("sk-")
):
from litellm.proxy.utils import hash_token
user_api_key = hash_token(user_api_key)
# increment total LLM requests and spend metric
self._increment_top_level_request_and_spend_metrics(
end_user_id=end_user_id,
user_api_key=user_api_key,
user_api_key_alias=user_api_key_alias,
model=model,
user_api_team=user_api_team,
user_api_team_alias=user_api_team_alias,
user_id=user_id,
response_cost=response_cost,
enum_values=enum_values,
)
# input, output, total token metrics
self._increment_token_metrics(
# why type ignore below?
# 1. We just checked if isinstance(standard_logging_payload, dict). Pyright complains.
# 2. Pyright does not allow us to run isinstance(standard_logging_payload, StandardLoggingPayload) <- this would be ideal
standard_logging_payload=standard_logging_payload, # type: ignore
end_user_id=end_user_id,
user_api_key=user_api_key,
user_api_key_alias=user_api_key_alias,
model=model,
user_api_team=user_api_team,
user_api_team_alias=user_api_team_alias,
user_id=user_id,
enum_values=enum_values,
)
# remaining budget metrics
await self._increment_remaining_budget_metrics(
user_api_team=user_api_team,
user_api_team_alias=user_api_team_alias,
user_api_key=user_api_key,
user_api_key_alias=user_api_key_alias,
litellm_params=litellm_params,
response_cost=response_cost,
)
# set proxy virtual key rpm/tpm metrics
self._set_virtual_key_rate_limit_metrics(
user_api_key=user_api_key,
user_api_key_alias=user_api_key_alias,
kwargs=kwargs,
metadata=_metadata,
)
# set latency metrics
self._set_latency_metrics(
kwargs=kwargs,
model=model,
user_api_key=user_api_key,
user_api_key_alias=user_api_key_alias,
user_api_team=user_api_team,
user_api_team_alias=user_api_team_alias,
# why type ignore below?
# 1. We just checked if isinstance(standard_logging_payload, dict). Pyright complains.
# 2. Pyright does not allow us to run isinstance(standard_logging_payload, StandardLoggingPayload) <- this would be ideal
enum_values=enum_values,
)
# set x-ratelimit headers
self.set_llm_deployment_success_metrics(
kwargs, start_time, end_time, enum_values, output_tokens
)
if (
standard_logging_payload["stream"] is True
): # log successful streaming requests from logging event hook.
_labels = prometheus_label_factory(
supported_enum_labels=PrometheusMetricLabels.get_labels(
label_name="litellm_proxy_total_requests_metric"
),
enum_values=enum_values,
)
self.litellm_proxy_total_requests_metric.labels(**_labels).inc()
def _increment_token_metrics(
self,
standard_logging_payload: StandardLoggingPayload,
end_user_id: Optional[str],
user_api_key: Optional[str],
user_api_key_alias: Optional[str],
model: Optional[str],
user_api_team: Optional[str],
user_api_team_alias: Optional[str],
user_id: Optional[str],
enum_values: UserAPIKeyLabelValues,
):
# token metrics
self.litellm_tokens_metric.labels(
end_user_id,
user_api_key,
user_api_key_alias,
model,
user_api_team,
user_api_team_alias,
user_id,
).inc(standard_logging_payload["total_tokens"])
if standard_logging_payload is not None and isinstance(
standard_logging_payload, dict
):
_tags = standard_logging_payload["request_tags"]
_labels = prometheus_label_factory(
supported_enum_labels=PrometheusMetricLabels.get_labels(
label_name="litellm_input_tokens_metric"
),
enum_values=enum_values,
)
self.litellm_input_tokens_metric.labels(**_labels).inc(
standard_logging_payload["prompt_tokens"]
)
_labels = prometheus_label_factory(
supported_enum_labels=PrometheusMetricLabels.get_labels(
label_name="litellm_output_tokens_metric"
),
enum_values=enum_values,
)
self.litellm_output_tokens_metric.labels(**_labels).inc(
standard_logging_payload["completion_tokens"]
)
async def _increment_remaining_budget_metrics(
self,
user_api_team: Optional[str],
user_api_team_alias: Optional[str],
user_api_key: Optional[str],
user_api_key_alias: Optional[str],
litellm_params: dict,
response_cost: float,
):
_team_spend = litellm_params.get("metadata", {}).get(
"user_api_key_team_spend", None
)
_team_max_budget = litellm_params.get("metadata", {}).get(
"user_api_key_team_max_budget", None
)
_api_key_spend = litellm_params.get("metadata", {}).get(
"user_api_key_spend", None
)
_api_key_max_budget = litellm_params.get("metadata", {}).get(
"user_api_key_max_budget", None
)
await self._set_api_key_budget_metrics_after_api_request(
user_api_key=user_api_key,
user_api_key_alias=user_api_key_alias,
response_cost=response_cost,
key_max_budget=_api_key_max_budget,
key_spend=_api_key_spend,
)
await self._set_team_budget_metrics_after_api_request(
user_api_team=user_api_team,
user_api_team_alias=user_api_team_alias,
team_spend=_team_spend,
team_max_budget=_team_max_budget,
response_cost=response_cost,
)
def _increment_top_level_request_and_spend_metrics(
self,
end_user_id: Optional[str],
user_api_key: Optional[str],
user_api_key_alias: Optional[str],
model: Optional[str],
user_api_team: Optional[str],
user_api_team_alias: Optional[str],
user_id: Optional[str],
response_cost: float,
enum_values: UserAPIKeyLabelValues,
):
_labels = prometheus_label_factory(
supported_enum_labels=PrometheusMetricLabels.get_labels(
label_name="litellm_requests_metric"
),
enum_values=enum_values,
)
self.litellm_requests_metric.labels(**_labels).inc()
self.litellm_spend_metric.labels(
end_user_id,
user_api_key,
user_api_key_alias,
model,
user_api_team,
user_api_team_alias,
user_id,
).inc(response_cost)
def _set_virtual_key_rate_limit_metrics(
self,
user_api_key: Optional[str],
user_api_key_alias: Optional[str],
kwargs: dict,
metadata: dict,
):
from litellm.proxy.common_utils.callback_utils import (
get_model_group_from_litellm_kwargs,
)
# Set remaining rpm/tpm for API Key + model
# see parallel_request_limiter.py - variables are set there
model_group = get_model_group_from_litellm_kwargs(kwargs)
remaining_requests_variable_name = (
f"litellm-key-remaining-requests-{model_group}"
)
remaining_tokens_variable_name = f"litellm-key-remaining-tokens-{model_group}"
remaining_requests = (
metadata.get(remaining_requests_variable_name, sys.maxsize) or sys.maxsize
)
remaining_tokens = (
metadata.get(remaining_tokens_variable_name, sys.maxsize) or sys.maxsize
)
self.litellm_remaining_api_key_requests_for_model.labels(
user_api_key, user_api_key_alias, model_group
).set(remaining_requests)
self.litellm_remaining_api_key_tokens_for_model.labels(
user_api_key, user_api_key_alias, model_group
).set(remaining_tokens)
def _set_latency_metrics(
self,
kwargs: dict,
model: Optional[str],
user_api_key: Optional[str],
user_api_key_alias: Optional[str],
user_api_team: Optional[str],
user_api_team_alias: Optional[str],
enum_values: UserAPIKeyLabelValues,
):
# latency metrics
end_time: datetime = kwargs.get("end_time") or datetime.now()
start_time: Optional[datetime] = kwargs.get("start_time")
api_call_start_time = kwargs.get("api_call_start_time", None)
completion_start_time = kwargs.get("completion_start_time", None)
time_to_first_token_seconds = self._safe_duration_seconds(
start_time=api_call_start_time,
end_time=completion_start_time,
)
if (
time_to_first_token_seconds is not None
and kwargs.get("stream", False) is True # only emit for streaming requests
):
self.litellm_llm_api_time_to_first_token_metric.labels(
model,
user_api_key,
user_api_key_alias,
user_api_team,
user_api_team_alias,
).observe(time_to_first_token_seconds)
else:
verbose_logger.debug(
"Time to first token metric not emitted, stream option in model_parameters is not True"
)
api_call_total_time_seconds = self._safe_duration_seconds(
start_time=api_call_start_time,
end_time=end_time,
)
if api_call_total_time_seconds is not None:
_labels = prometheus_label_factory(
supported_enum_labels=PrometheusMetricLabels.get_labels(
label_name="litellm_llm_api_latency_metric"
),
enum_values=enum_values,
)
self.litellm_llm_api_latency_metric.labels(**_labels).observe(
api_call_total_time_seconds
)
# total request latency
total_time_seconds = self._safe_duration_seconds(
start_time=start_time,
end_time=end_time,
)
if total_time_seconds is not None:
_labels = prometheus_label_factory(
supported_enum_labels=PrometheusMetricLabels.get_labels(
label_name="litellm_request_total_latency_metric"
),
enum_values=enum_values,
)
self.litellm_request_total_latency_metric.labels(**_labels).observe(
total_time_seconds
)
async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
from litellm.types.utils import StandardLoggingPayload
verbose_logger.debug(
f"prometheus Logging - Enters failure logging function for kwargs {kwargs}"
)
# unpack kwargs
model = kwargs.get("model", "")
standard_logging_payload: StandardLoggingPayload = kwargs.get(
"standard_logging_object", {}
)
litellm_params = kwargs.get("litellm_params", {}) or {}
end_user_id = get_end_user_id_for_cost_tracking(
litellm_params, service_type="prometheus"
)
user_id = standard_logging_payload["metadata"]["user_api_key_user_id"]
user_api_key = standard_logging_payload["metadata"]["user_api_key_hash"]
user_api_key_alias = standard_logging_payload["metadata"]["user_api_key_alias"]
user_api_team = standard_logging_payload["metadata"]["user_api_key_team_id"]
user_api_team_alias = standard_logging_payload["metadata"][
"user_api_key_team_alias"
]
kwargs.get("exception", None)
try:
self.litellm_llm_api_failed_requests_metric.labels(
end_user_id,
user_api_key,
user_api_key_alias,
model,
user_api_team,
user_api_team_alias,
user_id,
).inc()
self.set_llm_deployment_failure_metrics(kwargs)
except Exception as e:
verbose_logger.exception(
"prometheus Layer Error(): Exception occured - {}".format(str(e))
)
pass
pass
async def async_post_call_failure_hook(
self,
request_data: dict,
original_exception: Exception,
user_api_key_dict: UserAPIKeyAuth,
):
"""
Track client side failures
Proxy level tracking - failed client side requests
labelnames=[
"end_user",
"hashed_api_key",
"api_key_alias",
REQUESTED_MODEL,
"team",
"team_alias",
] + EXCEPTION_LABELS,
"""
try:
_tags = cast(List[str], request_data.get("tags") or [])
enum_values = UserAPIKeyLabelValues(
end_user=user_api_key_dict.end_user_id,
user=user_api_key_dict.user_id,
user_email=user_api_key_dict.user_email,
hashed_api_key=user_api_key_dict.api_key,
api_key_alias=user_api_key_dict.key_alias,
team=user_api_key_dict.team_id,
team_alias=user_api_key_dict.team_alias,
requested_model=request_data.get("model", ""),
status_code=str(getattr(original_exception, "status_code", None)),
exception_status=str(getattr(original_exception, "status_code", None)),
exception_class=str(original_exception.__class__.__name__),
tags=_tags,
)
_labels = prometheus_label_factory(
supported_enum_labels=PrometheusMetricLabels.get_labels(
label_name="litellm_proxy_failed_requests_metric"
),
enum_values=enum_values,
)
self.litellm_proxy_failed_requests_metric.labels(**_labels).inc()
_labels = prometheus_label_factory(
supported_enum_labels=PrometheusMetricLabels.get_labels(
label_name="litellm_proxy_total_requests_metric"
),
enum_values=enum_values,
)
self.litellm_proxy_total_requests_metric.labels(**_labels).inc()
except Exception as e:
verbose_logger.exception(
"prometheus Layer Error(): Exception occured - {}".format(str(e))
)
pass
async def async_post_call_success_hook(
self, data: dict, user_api_key_dict: UserAPIKeyAuth, response
):
"""
Proxy level tracking - triggered when the proxy responds with a success response to the client
"""
try:
enum_values = UserAPIKeyLabelValues(
end_user=user_api_key_dict.end_user_id,
hashed_api_key=user_api_key_dict.api_key,
api_key_alias=user_api_key_dict.key_alias,
requested_model=data.get("model", ""),
team=user_api_key_dict.team_id,
team_alias=user_api_key_dict.team_alias,
user=user_api_key_dict.user_id,
user_email=user_api_key_dict.user_email,
status_code="200",
)
_labels = prometheus_label_factory(
supported_enum_labels=PrometheusMetricLabels.get_labels(
label_name="litellm_proxy_total_requests_metric"
),
enum_values=enum_values,
)
self.litellm_proxy_total_requests_metric.labels(**_labels).inc()
except Exception as e:
verbose_logger.exception(
"prometheus Layer Error(): Exception occured - {}".format(str(e))
)
pass
def set_llm_deployment_failure_metrics(self, request_kwargs: dict):
"""
Sets Failure metrics when an LLM API call fails
- mark the deployment as partial outage
- increment deployment failure responses metric
- increment deployment total requests metric
Args:
request_kwargs: dict
"""
try:
verbose_logger.debug("setting remaining tokens requests metric")
standard_logging_payload: StandardLoggingPayload = request_kwargs.get(
"standard_logging_object", {}
)
_litellm_params = request_kwargs.get("litellm_params", {}) or {}
litellm_model_name = request_kwargs.get("model", None)
model_group = standard_logging_payload.get("model_group", None)
api_base = standard_logging_payload.get("api_base", None)
model_id = standard_logging_payload.get("model_id", None)
exception: Exception = request_kwargs.get("exception", None)
llm_provider = _litellm_params.get("custom_llm_provider", None)
"""
log these labels
["litellm_model_name", "model_id", "api_base", "api_provider"]
"""
self.set_deployment_partial_outage(
litellm_model_name=litellm_model_name,
model_id=model_id,
api_base=api_base,
api_provider=llm_provider,
)
self.litellm_deployment_failure_responses.labels(
litellm_model_name=litellm_model_name,
model_id=model_id,
api_base=api_base,
api_provider=llm_provider,
exception_status=str(getattr(exception, "status_code", None)),
exception_class=exception.__class__.__name__,
requested_model=model_group,
hashed_api_key=standard_logging_payload["metadata"][
"user_api_key_hash"
],
api_key_alias=standard_logging_payload["metadata"][
"user_api_key_alias"
],
team=standard_logging_payload["metadata"]["user_api_key_team_id"],
team_alias=standard_logging_payload["metadata"][
"user_api_key_team_alias"
],
).inc()
# tag based tracking
if standard_logging_payload is not None and isinstance(
standard_logging_payload, dict
):
_tags = standard_logging_payload["request_tags"]
for tag in _tags:
self.litellm_deployment_failure_by_tag_responses.labels(
**{
UserAPIKeyLabelNames.REQUESTED_MODEL.value: model_group,
UserAPIKeyLabelNames.TAG.value: tag,
UserAPIKeyLabelNames.v2_LITELLM_MODEL_NAME.value: litellm_model_name,
UserAPIKeyLabelNames.MODEL_ID.value: model_id,
UserAPIKeyLabelNames.API_BASE.value: api_base,
UserAPIKeyLabelNames.API_PROVIDER.value: llm_provider,
UserAPIKeyLabelNames.EXCEPTION_CLASS.value: exception.__class__.__name__,
UserAPIKeyLabelNames.EXCEPTION_STATUS.value: str(
getattr(exception, "status_code", None)
),
}
).inc()
self.litellm_deployment_total_requests.labels(
litellm_model_name=litellm_model_name,
model_id=model_id,
api_base=api_base,
api_provider=llm_provider,
requested_model=model_group,
hashed_api_key=standard_logging_payload["metadata"][
"user_api_key_hash"
],
api_key_alias=standard_logging_payload["metadata"][
"user_api_key_alias"
],
team=standard_logging_payload["metadata"]["user_api_key_team_id"],
team_alias=standard_logging_payload["metadata"][
"user_api_key_team_alias"
],
).inc()
pass
except Exception as e:
verbose_logger.debug(
"Prometheus Error: set_llm_deployment_failure_metrics. Exception occured - {}".format(
str(e)
)
)
def set_llm_deployment_success_metrics(
self,
request_kwargs: dict,
start_time,
end_time,
enum_values: UserAPIKeyLabelValues,
output_tokens: float = 1.0,
):
try:
verbose_logger.debug("setting remaining tokens requests metric")
standard_logging_payload: Optional[StandardLoggingPayload] = (
request_kwargs.get("standard_logging_object")
)
if standard_logging_payload is None:
return
model_group = standard_logging_payload["model_group"]
api_base = standard_logging_payload["api_base"]
_response_headers = request_kwargs.get("response_headers")
_litellm_params = request_kwargs.get("litellm_params", {}) or {}
_metadata = _litellm_params.get("metadata", {})
litellm_model_name = request_kwargs.get("model", None)
llm_provider = _litellm_params.get("custom_llm_provider", None)
_model_info = _metadata.get("model_info") or {}
model_id = _model_info.get("id", None)
remaining_requests: Optional[int] = None
remaining_tokens: Optional[int] = None
if additional_headers := standard_logging_payload["hidden_params"][
"additional_headers"
]:
# OpenAI / OpenAI Compatible headers
remaining_requests = additional_headers.get(
"x_ratelimit_remaining_requests", None
)
remaining_tokens = additional_headers.get(
"x_ratelimit_remaining_tokens", None
)
if litellm_overhead_time_ms := standard_logging_payload[
"hidden_params"
].get("litellm_overhead_time_ms"):
self.litellm_overhead_latency_metric.labels(
model_group,
llm_provider,
api_base,
litellm_model_name,
standard_logging_payload["metadata"]["user_api_key_hash"],
standard_logging_payload["metadata"]["user_api_key_alias"],
).observe(
litellm_overhead_time_ms / 1000
) # set as seconds
if remaining_requests:
"""
"model_group",
"api_provider",
"api_base",
"litellm_model_name"
"""
self.litellm_remaining_requests_metric.labels(
model_group,
llm_provider,
api_base,
litellm_model_name,
standard_logging_payload["metadata"]["user_api_key_hash"],
standard_logging_payload["metadata"]["user_api_key_alias"],
).set(remaining_requests)
if remaining_tokens:
self.litellm_remaining_tokens_metric.labels(
model_group,
llm_provider,
api_base,
litellm_model_name,
standard_logging_payload["metadata"]["user_api_key_hash"],
standard_logging_payload["metadata"]["user_api_key_alias"],
).set(remaining_tokens)
"""
log these labels
["litellm_model_name", "requested_model", model_id", "api_base", "api_provider"]
"""
self.set_deployment_healthy(
litellm_model_name=litellm_model_name,
model_id=model_id,
api_base=api_base,
api_provider=llm_provider,
)
self.litellm_deployment_success_responses.labels(
litellm_model_name=litellm_model_name,
model_id=model_id,
api_base=api_base,
api_provider=llm_provider,
requested_model=model_group,
hashed_api_key=standard_logging_payload["metadata"][
"user_api_key_hash"
],
api_key_alias=standard_logging_payload["metadata"][
"user_api_key_alias"
],
team=standard_logging_payload["metadata"]["user_api_key_team_id"],
team_alias=standard_logging_payload["metadata"][
"user_api_key_team_alias"
],
).inc()
self.litellm_deployment_total_requests.labels(
litellm_model_name=litellm_model_name,
model_id=model_id,
api_base=api_base,
api_provider=llm_provider,
requested_model=model_group,
hashed_api_key=standard_logging_payload["metadata"][
"user_api_key_hash"
],
api_key_alias=standard_logging_payload["metadata"][
"user_api_key_alias"
],
team=standard_logging_payload["metadata"]["user_api_key_team_id"],
team_alias=standard_logging_payload["metadata"][
"user_api_key_team_alias"
],
).inc()
# Track deployment Latency
response_ms: timedelta = end_time - start_time
time_to_first_token_response_time: Optional[timedelta] = None
if (
request_kwargs.get("stream", None) is not None
and request_kwargs["stream"] is True
):
# only log ttft for streaming request
time_to_first_token_response_time = (
request_kwargs.get("completion_start_time", end_time) - start_time
)
# use the metric that is not None
# if streaming - use time_to_first_token_response
# if not streaming - use response_ms
_latency: timedelta = time_to_first_token_response_time or response_ms
_latency_seconds = _latency.total_seconds()
# latency per output token
latency_per_token = None
if output_tokens is not None and output_tokens > 0:
latency_per_token = _latency_seconds / output_tokens
_labels = prometheus_label_factory(
supported_enum_labels=PrometheusMetricLabels.get_labels(
label_name="litellm_deployment_latency_per_output_token"
),
enum_values=enum_values,
)
self.litellm_deployment_latency_per_output_token.labels(
**_labels
).observe(latency_per_token)
except Exception as e:
verbose_logger.error(
"Prometheus Error: set_llm_deployment_success_metrics. Exception occured - {}".format(
str(e)
)
)
return
async def log_success_fallback_event(
self, original_model_group: str, kwargs: dict, original_exception: Exception
):
"""
Logs a successful LLM fallback event on prometheus
"""
from litellm.litellm_core_utils.litellm_logging import (
StandardLoggingMetadata,
StandardLoggingPayloadSetup,
)
verbose_logger.debug(
"Prometheus: log_success_fallback_event, original_model_group: %s, kwargs: %s",
original_model_group,
kwargs,
)
_metadata = kwargs.get("metadata", {})
standard_metadata: StandardLoggingMetadata = (
StandardLoggingPayloadSetup.get_standard_logging_metadata(
metadata=_metadata
)
)
_new_model = kwargs.get("model")
_tags = cast(List[str], kwargs.get("tags") or [])
enum_values = UserAPIKeyLabelValues(
requested_model=original_model_group,
fallback_model=_new_model,
hashed_api_key=standard_metadata["user_api_key_hash"],
api_key_alias=standard_metadata["user_api_key_alias"],
team=standard_metadata["user_api_key_team_id"],
team_alias=standard_metadata["user_api_key_team_alias"],
exception_status=str(getattr(original_exception, "status_code", None)),
exception_class=str(original_exception.__class__.__name__),
tags=_tags,
)
_labels = prometheus_label_factory(
supported_enum_labels=PrometheusMetricLabels.get_labels(
label_name="litellm_deployment_successful_fallbacks"
),
enum_values=enum_values,
)
self.litellm_deployment_successful_fallbacks.labels(**_labels).inc()
async def log_failure_fallback_event(
self, original_model_group: str, kwargs: dict, original_exception: Exception
):
"""
Logs a failed LLM fallback event on prometheus
"""
from litellm.litellm_core_utils.litellm_logging import (
StandardLoggingMetadata,
StandardLoggingPayloadSetup,
)
verbose_logger.debug(
"Prometheus: log_failure_fallback_event, original_model_group: %s, kwargs: %s",
original_model_group,
kwargs,
)
_new_model = kwargs.get("model")
_metadata = kwargs.get("metadata", {})
_tags = cast(List[str], kwargs.get("tags") or [])
standard_metadata: StandardLoggingMetadata = (
StandardLoggingPayloadSetup.get_standard_logging_metadata(
metadata=_metadata
)
)
enum_values = UserAPIKeyLabelValues(
requested_model=original_model_group,
fallback_model=_new_model,
hashed_api_key=standard_metadata["user_api_key_hash"],
api_key_alias=standard_metadata["user_api_key_alias"],
team=standard_metadata["user_api_key_team_id"],
team_alias=standard_metadata["user_api_key_team_alias"],
exception_status=str(getattr(original_exception, "status_code", None)),
exception_class=str(original_exception.__class__.__name__),
tags=_tags,
)
_labels = prometheus_label_factory(
supported_enum_labels=PrometheusMetricLabels.get_labels(
label_name="litellm_deployment_failed_fallbacks"
),
enum_values=enum_values,
)
self.litellm_deployment_failed_fallbacks.labels(**_labels).inc()
def set_litellm_deployment_state(
self,
state: int,
litellm_model_name: str,
model_id: Optional[str],
api_base: Optional[str],
api_provider: str,
):
self.litellm_deployment_state.labels(
litellm_model_name, model_id, api_base, api_provider
).set(state)
def set_deployment_healthy(
self,
litellm_model_name: str,
model_id: str,
api_base: str,
api_provider: str,
):
self.set_litellm_deployment_state(
0, litellm_model_name, model_id, api_base, api_provider
)
def set_deployment_partial_outage(
self,
litellm_model_name: str,
model_id: Optional[str],
api_base: Optional[str],
api_provider: str,
):
self.set_litellm_deployment_state(
1, litellm_model_name, model_id, api_base, api_provider
)
def set_deployment_complete_outage(
self,
litellm_model_name: str,
model_id: Optional[str],
api_base: Optional[str],
api_provider: str,
):
self.set_litellm_deployment_state(
2, litellm_model_name, model_id, api_base, api_provider
)
def increment_deployment_cooled_down(
self,
litellm_model_name: str,
model_id: str,
api_base: str,
api_provider: str,
exception_status: str,
):
"""
increment metric when litellm.Router / load balancing logic places a deployment in cool down
"""
self.litellm_deployment_cooled_down.labels(
litellm_model_name, model_id, api_base, api_provider, exception_status
).inc()
def track_provider_remaining_budget(
self, provider: str, spend: float, budget_limit: float
):
"""
Track provider remaining budget in Prometheus
"""
self.litellm_provider_remaining_budget_metric.labels(provider).set(
self._safe_get_remaining_budget(
max_budget=budget_limit,
spend=spend,
)
)
def _safe_get_remaining_budget(
self, max_budget: Optional[float], spend: Optional[float]
) -> float:
if max_budget is None:
return float("inf")
if spend is None:
return max_budget
return max_budget - spend
def _initialize_prometheus_startup_metrics(self):
"""
Initialize prometheus startup metrics
Helper to create tasks for initializing metrics that are required on startup - eg. remaining budget metrics
"""
if litellm.prometheus_initialize_budget_metrics is not True:
verbose_logger.debug("Prometheus: skipping budget metrics initialization")
return
try:
if asyncio.get_running_loop():
asyncio.create_task(self._initialize_remaining_budget_metrics())
except RuntimeError as e: # no running event loop
verbose_logger.exception(
f"No running event loop - skipping budget metrics initialization: {str(e)}"
)
async def _initialize_budget_metrics(
self,
data_fetch_function: Callable[..., Awaitable[Tuple[List[Any], Optional[int]]]],
set_metrics_function: Callable[[List[Any]], Awaitable[None]],
data_type: Literal["teams", "keys"],
):
"""
Generic method to initialize budget metrics for teams or API keys.
Args:
data_fetch_function: Function to fetch data with pagination.
set_metrics_function: Function to set metrics for the fetched data.
data_type: String representing the type of data ("teams" or "keys") for logging purposes.
"""
from litellm.proxy.proxy_server import prisma_client
if prisma_client is None:
return
try:
page = 1
page_size = 50
data, total_count = await data_fetch_function(
page_size=page_size, page=page
)
if total_count is None:
total_count = len(data)
# Calculate total pages needed
total_pages = (total_count + page_size - 1) // page_size
# Set metrics for first page of data
await set_metrics_function(data)
# Get and set metrics for remaining pages
for page in range(2, total_pages + 1):
data, _ = await data_fetch_function(page_size=page_size, page=page)
await set_metrics_function(data)
except Exception as e:
verbose_logger.exception(
f"Error initializing {data_type} budget metrics: {str(e)}"
)
async def _initialize_team_budget_metrics(self):
"""
Initialize team budget metrics by reusing the generic pagination logic.
"""
from litellm.proxy.management_endpoints.team_endpoints import (
get_paginated_teams,
)
from litellm.proxy.proxy_server import prisma_client
if prisma_client is None:
verbose_logger.debug(
"Prometheus: skipping team metrics initialization, DB not initialized"
)
return
async def fetch_teams(
page_size: int, page: int
) -> Tuple[List[LiteLLM_TeamTable], Optional[int]]:
teams, total_count = await get_paginated_teams(
prisma_client=prisma_client, page_size=page_size, page=page
)
if total_count is None:
total_count = len(teams)
return teams, total_count
await self._initialize_budget_metrics(
data_fetch_function=fetch_teams,
set_metrics_function=self._set_team_list_budget_metrics,
data_type="teams",
)
async def _initialize_api_key_budget_metrics(self):
"""
Initialize API key budget metrics by reusing the generic pagination logic.
"""
from typing import Union
from litellm.constants import UI_SESSION_TOKEN_TEAM_ID
from litellm.proxy.management_endpoints.key_management_endpoints import (
_list_key_helper,
)
from litellm.proxy.proxy_server import prisma_client
if prisma_client is None:
verbose_logger.debug(
"Prometheus: skipping key metrics initialization, DB not initialized"
)
return
async def fetch_keys(
page_size: int, page: int
) -> Tuple[List[Union[str, UserAPIKeyAuth]], Optional[int]]:
key_list_response = await _list_key_helper(
prisma_client=prisma_client,
page=page,
size=page_size,
user_id=None,
team_id=None,
key_alias=None,
exclude_team_id=UI_SESSION_TOKEN_TEAM_ID,
return_full_object=True,
organization_id=None,
)
keys = key_list_response.get("keys", [])
total_count = key_list_response.get("total_count")
if total_count is None:
total_count = len(keys)
return keys, total_count
await self._initialize_budget_metrics(
data_fetch_function=fetch_keys,
set_metrics_function=self._set_key_list_budget_metrics,
data_type="keys",
)
async def _initialize_remaining_budget_metrics(self):
"""
Initialize remaining budget metrics for all teams to avoid metric discrepancies.
Runs when prometheus logger starts up.
"""
await self._initialize_team_budget_metrics()
await self._initialize_api_key_budget_metrics()
async def _set_key_list_budget_metrics(
self, keys: List[Union[str, UserAPIKeyAuth]]
):
"""Helper function to set budget metrics for a list of keys"""
for key in keys:
if isinstance(key, UserAPIKeyAuth):
self._set_key_budget_metrics(key)
async def _set_team_list_budget_metrics(self, teams: List[LiteLLM_TeamTable]):
"""Helper function to set budget metrics for a list of teams"""
for team in teams:
self._set_team_budget_metrics(team)
async def _set_team_budget_metrics_after_api_request(
self,
user_api_team: Optional[str],
user_api_team_alias: Optional[str],
team_spend: float,
team_max_budget: float,
response_cost: float,
):
"""
Set team budget metrics after an LLM API request
- Assemble a LiteLLM_TeamTable object
- looks up team info from db if not available in metadata
- Set team budget metrics
"""
if user_api_team:
team_object = await self._assemble_team_object(
team_id=user_api_team,
team_alias=user_api_team_alias or "",
spend=team_spend,
max_budget=team_max_budget,
response_cost=response_cost,
)
self._set_team_budget_metrics(team_object)
async def _assemble_team_object(
self,
team_id: str,
team_alias: str,
spend: Optional[float],
max_budget: Optional[float],
response_cost: float,
) -> LiteLLM_TeamTable:
"""
Assemble a LiteLLM_TeamTable object
for fields not available in metadata, we fetch from db
Fields not available in metadata:
- `budget_reset_at`
"""
from litellm.proxy.auth.auth_checks import get_team_object
from litellm.proxy.proxy_server import prisma_client, user_api_key_cache
_total_team_spend = (spend or 0) + response_cost
team_object = LiteLLM_TeamTable(
team_id=team_id,
team_alias=team_alias,
spend=_total_team_spend,
max_budget=max_budget,
)
try:
team_info = await get_team_object(
team_id=team_id,
prisma_client=prisma_client,
user_api_key_cache=user_api_key_cache,
)
except Exception as e:
verbose_logger.debug(
f"[Non-Blocking] Prometheus: Error getting team info: {str(e)}"
)
return team_object
if team_info:
team_object.budget_reset_at = team_info.budget_reset_at
return team_object
def _set_team_budget_metrics(
self,
team: LiteLLM_TeamTable,
):
"""
Set team budget metrics for a single team
- Remaining Budget
- Max Budget
- Budget Reset At
"""
enum_values = UserAPIKeyLabelValues(
team=team.team_id,
team_alias=team.team_alias or "",
)
_labels = prometheus_label_factory(
supported_enum_labels=PrometheusMetricLabels.get_labels(
label_name="litellm_remaining_team_budget_metric"
),
enum_values=enum_values,
)
self.litellm_remaining_team_budget_metric.labels(**_labels).set(
self._safe_get_remaining_budget(
max_budget=team.max_budget,
spend=team.spend,
)
)
if team.max_budget is not None:
_labels = prometheus_label_factory(
supported_enum_labels=PrometheusMetricLabels.get_labels(
label_name="litellm_team_max_budget_metric"
),
enum_values=enum_values,
)
self.litellm_team_max_budget_metric.labels(**_labels).set(team.max_budget)
if team.budget_reset_at is not None:
_labels = prometheus_label_factory(
supported_enum_labels=PrometheusMetricLabels.get_labels(
label_name="litellm_team_budget_remaining_hours_metric"
),
enum_values=enum_values,
)
self.litellm_team_budget_remaining_hours_metric.labels(**_labels).set(
self._get_remaining_hours_for_budget_reset(
budget_reset_at=team.budget_reset_at
)
)
def _set_key_budget_metrics(self, user_api_key_dict: UserAPIKeyAuth):
"""
Set virtual key budget metrics
- Remaining Budget
- Max Budget
- Budget Reset At
"""
enum_values = UserAPIKeyLabelValues(
hashed_api_key=user_api_key_dict.token,
api_key_alias=user_api_key_dict.key_alias or "",
)
_labels = prometheus_label_factory(
supported_enum_labels=PrometheusMetricLabels.get_labels(
label_name="litellm_remaining_api_key_budget_metric"
),
enum_values=enum_values,
)
self.litellm_remaining_api_key_budget_metric.labels(**_labels).set(
self._safe_get_remaining_budget(
max_budget=user_api_key_dict.max_budget,
spend=user_api_key_dict.spend,
)
)
if user_api_key_dict.max_budget is not None:
_labels = prometheus_label_factory(
supported_enum_labels=PrometheusMetricLabels.get_labels(
label_name="litellm_api_key_max_budget_metric"
),
enum_values=enum_values,
)
self.litellm_api_key_max_budget_metric.labels(**_labels).set(
user_api_key_dict.max_budget
)
if user_api_key_dict.budget_reset_at is not None:
self.litellm_api_key_budget_remaining_hours_metric.labels(**_labels).set(
self._get_remaining_hours_for_budget_reset(
budget_reset_at=user_api_key_dict.budget_reset_at
)
)
async def _set_api_key_budget_metrics_after_api_request(
self,
user_api_key: Optional[str],
user_api_key_alias: Optional[str],
response_cost: float,
key_max_budget: float,
key_spend: Optional[float],
):
if user_api_key:
user_api_key_dict = await self._assemble_key_object(
user_api_key=user_api_key,
user_api_key_alias=user_api_key_alias or "",
key_max_budget=key_max_budget,
key_spend=key_spend,
response_cost=response_cost,
)
self._set_key_budget_metrics(user_api_key_dict)
async def _assemble_key_object(
self,
user_api_key: str,
user_api_key_alias: str,
key_max_budget: float,
key_spend: Optional[float],
response_cost: float,
) -> UserAPIKeyAuth:
"""
Assemble a UserAPIKeyAuth object
"""
from litellm.proxy.auth.auth_checks import get_key_object
from litellm.proxy.proxy_server import prisma_client, user_api_key_cache
_total_key_spend = (key_spend or 0) + response_cost
user_api_key_dict = UserAPIKeyAuth(
token=user_api_key,
key_alias=user_api_key_alias,
max_budget=key_max_budget,
spend=_total_key_spend,
)
try:
if user_api_key_dict.token:
key_object = await get_key_object(
hashed_token=user_api_key_dict.token,
prisma_client=prisma_client,
user_api_key_cache=user_api_key_cache,
)
if key_object:
user_api_key_dict.budget_reset_at = key_object.budget_reset_at
except Exception as e:
verbose_logger.debug(
f"[Non-Blocking] Prometheus: Error getting key info: {str(e)}"
)
return user_api_key_dict
def _get_remaining_hours_for_budget_reset(self, budget_reset_at: datetime) -> float:
"""
Get remaining hours for budget reset
"""
return (
budget_reset_at - datetime.now(budget_reset_at.tzinfo)
).total_seconds() / 3600
def _safe_duration_seconds(
self,
start_time: Any,
end_time: Any,
) -> Optional[float]:
"""
Compute the duration in seconds between two objects.
Returns the duration as a float if both start and end are instances of datetime,
otherwise returns None.
"""
if isinstance(start_time, datetime) and isinstance(end_time, datetime):
return (end_time - start_time).total_seconds()
return None
def prometheus_label_factory(
supported_enum_labels: List[str],
enum_values: UserAPIKeyLabelValues,
tag: Optional[str] = None,
) -> dict:
"""
Returns a dictionary of label + values for prometheus.
Ensures end_user param is not sent to prometheus if it is not supported.
"""
# Extract dictionary from Pydantic object
enum_dict = enum_values.model_dump()
# Filter supported labels
filtered_labels = {
label: value
for label, value in enum_dict.items()
if label in supported_enum_labels
}
if UserAPIKeyLabelNames.END_USER.value in filtered_labels:
filtered_labels["end_user"] = get_end_user_id_for_cost_tracking(
litellm_params={"user_api_key_end_user_id": enum_values.end_user},
service_type="prometheus",
)
if enum_values.custom_metadata_labels is not None:
for key, value in enum_values.custom_metadata_labels.items():
if key in supported_enum_labels:
filtered_labels[key] = value
for label in supported_enum_labels:
if label not in filtered_labels:
filtered_labels[label] = None
return filtered_labels
def get_custom_labels_from_metadata(metadata: dict) -> Dict[str, str]:
"""
Get custom labels from metadata
"""
keys = litellm.custom_prometheus_metadata_labels
if keys is None or len(keys) == 0:
return {}
result: Dict[str, str] = {}
for key in keys:
# Split the dot notation key into parts
original_key = key
key = key.replace("metadata.", "", 1) if key.startswith("metadata.") else key
keys_parts = key.split(".")
# Traverse through the dictionary using the parts
value = metadata
for part in keys_parts:
value = value.get(part, None) # Get the value, return None if not found
if value is None:
break
if value is not None and isinstance(value, str):
result[original_key.replace(".", "_")] = value
return result
