#### What this does ####
# Class for sending Slack Alerts #
import dotenv, os
from litellm.proxy._types import UserAPIKeyAuth, CallInfo
from litellm._logging import verbose_logger, verbose_proxy_logger
import litellm, threading
from typing import List, Literal, Any, Union, Optional, Dict
from litellm.caching import DualCache
import asyncio, time
import aiohttp
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
import datetime
from pydantic import BaseModel
from enum import Enum
from datetime import datetime as dt, timedelta, timezone
from litellm.integrations.custom_logger import CustomLogger
from litellm.proxy._types import WebhookEvent
import random
# we use this for the email header, please send a test email if you change this. verify it looks good on email
LITELLM_LOGO_URL = "https://litellm-listing.s3.amazonaws.com/litellm_logo.png"
class LiteLLMBase(BaseModel):
"""
Implements default functions, all pydantic objects should have.
"""
def json(self, **kwargs):
try:
return self.model_dump() # noqa
except:
# if using pydantic v1
return self.dict()
class SlackAlertingArgs(LiteLLMBase):
default_daily_report_frequency: int = 12 * 60 * 60 # 12 hours
daily_report_frequency: int = int(
os.getenv("SLACK_DAILY_REPORT_FREQUENCY", default_daily_report_frequency)
)
report_check_interval: int = 5 * 60 # 5 minutes
budget_alert_ttl: int = 24 * 60 * 60 # 24 hours
class DeploymentMetrics(LiteLLMBase):
"""
Metrics per deployment, stored in cache
Used for daily reporting
"""
id: str
"""id of deployment in router model list"""
failed_request: bool
"""did it fail the request?"""
latency_per_output_token: Optional[float]
"""latency/output token of deployment"""
updated_at: dt
"""Current time of deployment being updated"""
class SlackAlertingCacheKeys(Enum):
"""
Enum for deployment daily metrics keys - {deployment_id}:{enum}
"""
failed_requests_key = "failed_requests_daily_metrics"
latency_key = "latency_daily_metrics"
report_sent_key = "daily_metrics_report_sent"
class SlackAlerting(CustomLogger):
"""
Class for sending Slack Alerts
"""
# Class variables or attributes
def __init__(
self,
internal_usage_cache: Optional[DualCache] = None,
alerting_threshold: float = 300, # threshold for slow / hanging llm responses (in seconds)
alerting: Optional[List] = [],
alert_types: List[
Literal[
"llm_exceptions",
"llm_too_slow",
"llm_requests_hanging",
"budget_alerts",
"db_exceptions",
"daily_reports",
"spend_reports",
"cooldown_deployment",
"new_model_added",
]
] = [
"llm_exceptions",
"llm_too_slow",
"llm_requests_hanging",
"budget_alerts",
"db_exceptions",
"daily_reports",
"spend_reports",
"cooldown_deployment",
"new_model_added",
],
alert_to_webhook_url: Optional[
Dict
] = None, # if user wants to separate alerts to diff channels
alerting_args={},
default_webhook_url: Optional[str] = None,
):
self.alerting_threshold = alerting_threshold
self.alerting = alerting
self.alert_types = alert_types
self.internal_usage_cache = internal_usage_cache or DualCache()
self.async_http_handler = AsyncHTTPHandler()
self.alert_to_webhook_url = alert_to_webhook_url
self.is_running = False
self.alerting_args = SlackAlertingArgs(**alerting_args)
self.default_webhook_url = default_webhook_url
def update_values(
self,
alerting: Optional[List] = None,
alerting_threshold: Optional[float] = None,
alert_types: Optional[List] = None,
alert_to_webhook_url: Optional[Dict] = None,
alerting_args: Optional[Dict] = None,
):
if alerting is not None:
self.alerting = alerting
if alerting_threshold is not None:
self.alerting_threshold = alerting_threshold
if alert_types is not None:
self.alert_types = alert_types
if alerting_args is not None:
self.alerting_args = SlackAlertingArgs(**alerting_args)
if alert_to_webhook_url is not None:
# update the dict
if self.alert_to_webhook_url is None:
self.alert_to_webhook_url = alert_to_webhook_url
else:
self.alert_to_webhook_url.update(alert_to_webhook_url)
async def deployment_in_cooldown(self):
pass
async def deployment_removed_from_cooldown(self):
pass
def _all_possible_alert_types(self):
# used by the UI to show all supported alert types
# Note: This is not the alerts the user has configured, instead it's all possible alert types a user can select
return [
"llm_exceptions",
"llm_too_slow",
"llm_requests_hanging",
"budget_alerts",
"db_exceptions",
]
def _add_langfuse_trace_id_to_alert(
self,
request_data: Optional[dict] = None,
) -> Optional[str]:
"""
Returns langfuse trace url
- check:
-> existing_trace_id
-> trace_id
-> litellm_call_id
"""
# do nothing for now
if request_data is not None:
trace_id = None
if (
request_data.get("metadata", {}).get("existing_trace_id", None)
is not None
):
trace_id = request_data["metadata"]["existing_trace_id"]
elif request_data.get("metadata", {}).get("trace_id", None) is not None:
trace_id = request_data["metadata"]["trace_id"]
elif request_data.get("litellm_logging_obj", None) is not None and hasattr(
request_data["litellm_logging_obj"], "model_call_details"
):
trace_id = request_data["litellm_logging_obj"].model_call_details[
"litellm_call_id"
]
if litellm.utils.langFuseLogger is not None:
base_url = litellm.utils.langFuseLogger.Langfuse.base_url
return f"{base_url}/trace/{trace_id}"
return None
def _response_taking_too_long_callback_helper(
self,
kwargs, # kwargs to completion
start_time,
end_time, # start/end time
):
try:
time_difference = end_time - start_time
# Convert the timedelta to float (in seconds)
time_difference_float = time_difference.total_seconds()
litellm_params = kwargs.get("litellm_params", {})
model = kwargs.get("model", "")
api_base = litellm.get_api_base(model=model, optional_params=litellm_params)
messages = kwargs.get("messages", None)
# if messages does not exist fallback to "input"
if messages is None:
messages = kwargs.get("input", None)
# only use first 100 chars for alerting
_messages = str(messages)[:100]
return time_difference_float, model, api_base, _messages
except Exception as e:
raise e
def _get_deployment_latencies_to_alert(self, metadata=None):
if metadata is None:
return None
if "_latency_per_deployment" in metadata:
# Translate model_id to -> api_base
# _latency_per_deployment is a dictionary that looks like this:
"""
_latency_per_deployment: {
api_base: 0.01336697916666667
}
"""
_message_to_send = ""
_deployment_latencies = metadata["_latency_per_deployment"]
if len(_deployment_latencies) == 0:
return None
try:
# try sorting deployments by latency
_deployment_latencies = sorted(
_deployment_latencies.items(), key=lambda x: x[1]
)
_deployment_latencies = dict(_deployment_latencies)
except:
pass
for api_base, latency in _deployment_latencies.items():
_message_to_send += f"\n{api_base}: {round(latency,2)}s"
_message_to_send = "```" + _message_to_send + "```"
return _message_to_send
async def response_taking_too_long_callback(
self,
kwargs, # kwargs to completion
completion_response, # response from completion
start_time,
end_time, # start/end time
):
if self.alerting is None or self.alert_types is None:
return
time_difference_float, model, api_base, messages = (
self._response_taking_too_long_callback_helper(
kwargs=kwargs,
start_time=start_time,
end_time=end_time,
)
)
if litellm.turn_off_message_logging:
messages = "Message not logged. `litellm.turn_off_message_logging=True`."
request_info = f"\nRequest Model: `{model}`\nAPI Base: `{api_base}`\nMessages: `{messages}`"
slow_message = f"`Responses are slow - {round(time_difference_float,2)}s response time > Alerting threshold: {self.alerting_threshold}s`"
if time_difference_float > self.alerting_threshold:
# add deployment latencies to alert
if (
kwargs is not None
and "litellm_params" in kwargs
and "metadata" in kwargs["litellm_params"]
):
_metadata = kwargs["litellm_params"]["metadata"]
request_info = litellm.utils._add_key_name_and_team_to_alert(
request_info=request_info, metadata=_metadata
)
_deployment_latency_map = self._get_deployment_latencies_to_alert(
metadata=_metadata
)
if _deployment_latency_map is not None:
request_info += (
f"\nAvailable Deployment Latencies\n{_deployment_latency_map}"
)
await self.send_alert(
message=slow_message + request_info,
level="Low",
alert_type="llm_too_slow",
)
async def async_update_daily_reports(
self, deployment_metrics: DeploymentMetrics
) -> int:
"""
Store the perf by deployment in cache
- Number of failed requests per deployment
- Latency / output tokens per deployment
'deployment_id:daily_metrics:failed_requests'
'deployment_id:daily_metrics:latency_per_output_token'
Returns
int - count of metrics set (1 - if just latency, 2 - if failed + latency)
"""
return_val = 0
try:
## FAILED REQUESTS ##
if deployment_metrics.failed_request:
await self.internal_usage_cache.async_increment_cache(
key="{}:{}".format(
deployment_metrics.id,
SlackAlertingCacheKeys.failed_requests_key.value,
),
value=1,
)
return_val += 1
## LATENCY ##
if deployment_metrics.latency_per_output_token is not None:
await self.internal_usage_cache.async_increment_cache(
key="{}:{}".format(
deployment_metrics.id, SlackAlertingCacheKeys.latency_key.value
),
value=deployment_metrics.latency_per_output_token,
)
return_val += 1
return return_val
except Exception as e:
return 0
async def send_daily_reports(self, router) -> bool:
"""
Send a daily report on:
- Top 5 deployments with most failed requests
- Top 5 slowest deployments (normalized by latency/output tokens)
Get the value from redis cache (if available) or in-memory and send it
Cleanup:
- reset values in cache -> prevent memory leak
Returns:
True -> if successfuly sent
False -> if not sent
"""
ids = router.get_model_ids()
# get keys
failed_request_keys = [
"{}:{}".format(id, SlackAlertingCacheKeys.failed_requests_key.value)
for id in ids
]
latency_keys = [
"{}:{}".format(id, SlackAlertingCacheKeys.latency_key.value) for id in ids
]
combined_metrics_keys = failed_request_keys + latency_keys # reduce cache calls
combined_metrics_values = await self.internal_usage_cache.async_batch_get_cache(
keys=combined_metrics_keys
) # [1, 2, None, ..]
if combined_metrics_values is None:
return False
all_none = True
for val in combined_metrics_values:
if val is not None and val > 0:
all_none = False
break
if all_none:
return False
failed_request_values = combined_metrics_values[
: len(failed_request_keys)
] # # [1, 2, None, ..]
latency_values = combined_metrics_values[len(failed_request_keys) :]
# find top 5 failed
## Replace None values with a placeholder value (-1 in this case)
placeholder_value = 0
replaced_failed_values = [
value if value is not None else placeholder_value
for value in failed_request_values
]
## Get the indices of top 5 keys with the highest numerical values (ignoring None and 0 values)
top_5_failed = sorted(
range(len(replaced_failed_values)),
key=lambda i: replaced_failed_values[i],
reverse=True,
)[:5]
top_5_failed = [
index for index in top_5_failed if replaced_failed_values[index] > 0
]
# find top 5 slowest
# Replace None values with a placeholder value (-1 in this case)
placeholder_value = 0
replaced_slowest_values = [
value if value is not None else placeholder_value
for value in latency_values
]
# Get the indices of top 5 values with the highest numerical values (ignoring None and 0 values)
top_5_slowest = sorted(
range(len(replaced_slowest_values)),
key=lambda i: replaced_slowest_values[i],
reverse=True,
)[:5]
top_5_slowest = [
index for index in top_5_slowest if replaced_slowest_values[index] > 0
]
# format alert -> return the litellm model name + api base
message = f"\n\nTime: `{time.time()}`s\nHere are today's key metrics š: \n\n"
message += "\n\n*āļø Top Deployments with Most Failed Requests:*\n\n"
if not top_5_failed:
message += "\tNone\n"
for i in range(len(top_5_failed)):
key = failed_request_keys[top_5_failed[i]].split(":")[0]
_deployment = router.get_model_info(key)
if isinstance(_deployment, dict):
deployment_name = _deployment["litellm_params"].get("model", "")
else:
return False
api_base = litellm.get_api_base(
model=deployment_name,
optional_params=(
_deployment["litellm_params"] if _deployment is not None else {}
),
)
if api_base is None:
api_base = ""
value = replaced_failed_values[top_5_failed[i]]
message += f"\t{i+1}. Deployment: `{deployment_name}`, Failed Requests: `{value}`, API Base: `{api_base}`\n"
message += "\n\n*š
Top Slowest Deployments:*\n\n"
if not top_5_slowest:
message += "\tNone\n"
for i in range(len(top_5_slowest)):
key = latency_keys[top_5_slowest[i]].split(":")[0]
_deployment = router.get_model_info(key)
if _deployment is not None:
deployment_name = _deployment["litellm_params"].get("model", "")
else:
deployment_name = ""
api_base = litellm.get_api_base(
model=deployment_name,
optional_params=(
_deployment["litellm_params"] if _deployment is not None else {}
),
)
value = round(replaced_slowest_values[top_5_slowest[i]], 3)
message += f"\t{i+1}. Deployment: `{deployment_name}`, Latency per output token: `{value}s/token`, API Base: `{api_base}`\n\n"
# cache cleanup -> reset values to 0
latency_cache_keys = [(key, 0) for key in latency_keys]
failed_request_cache_keys = [(key, 0) for key in failed_request_keys]
combined_metrics_cache_keys = latency_cache_keys + failed_request_cache_keys
await self.internal_usage_cache.async_batch_set_cache(
cache_list=combined_metrics_cache_keys
)
message += f"\n\nNext Run is in: `{time.time() + self.alerting_args.daily_report_frequency}`s"
# send alert
await self.send_alert(message=message, level="Low", alert_type="daily_reports")
return True
async def response_taking_too_long(
self,
start_time: Optional[datetime.datetime] = None,
end_time: Optional[datetime.datetime] = None,
type: Literal["hanging_request", "slow_response"] = "hanging_request",
request_data: Optional[dict] = None,
):
if self.alerting is None or self.alert_types is None:
return
if request_data is not None:
model = request_data.get("model", "")
messages = request_data.get("messages", None)
if messages is None:
# if messages does not exist fallback to "input"
messages = request_data.get("input", None)
# try casting messages to str and get the first 100 characters, else mark as None
try:
messages = str(messages)
messages = messages[:100]
except:
messages = ""
if litellm.turn_off_message_logging:
messages = (
"Message not logged. `litellm.turn_off_message_logging=True`."
)
request_info = f"\nRequest Model: `{model}`\nMessages: `{messages}`"
else:
request_info = ""
if type == "hanging_request":
await asyncio.sleep(
self.alerting_threshold
) # Set it to 5 minutes - i'd imagine this might be different for streaming, non-streaming, non-completion (embedding + img) requests
if (
request_data is not None
and request_data.get("litellm_status", "") != "success"
and request_data.get("litellm_status", "") != "fail"
):
if request_data.get("deployment", None) is not None and isinstance(
request_data["deployment"], dict
):
_api_base = litellm.get_api_base(
model=model,
optional_params=request_data["deployment"].get(
"litellm_params", {}
),
)
if _api_base is None:
_api_base = ""
request_info += f"\nAPI Base: {_api_base}"
elif request_data.get("metadata", None) is not None and isinstance(
request_data["metadata"], dict
):
# In hanging requests sometime it has not made it to the point where the deployment is passed to the `request_data``
# in that case we fallback to the api base set in the request metadata
_metadata = request_data["metadata"]
_api_base = _metadata.get("api_base", "")
request_info = litellm.utils._add_key_name_and_team_to_alert(
request_info=request_info, metadata=_metadata
)
if _api_base is None:
_api_base = ""
request_info += f"\nAPI Base: `{_api_base}`"
# only alert hanging responses if they have not been marked as success
alerting_message = (
f"`Requests are hanging - {self.alerting_threshold}s+ request time`"
)
if "langfuse" in litellm.success_callback:
langfuse_url = self._add_langfuse_trace_id_to_alert(
request_data=request_data,
)
if langfuse_url is not None:
request_info += "\nšŖ¢ Langfuse Trace: {}".format(langfuse_url)
# add deployment latencies to alert
_deployment_latency_map = self._get_deployment_latencies_to_alert(
metadata=request_data.get("metadata", {})
)
if _deployment_latency_map is not None:
request_info += f"\nDeployment Latencies\n{_deployment_latency_map}"
await self.send_alert(
message=alerting_message + request_info,
level="Medium",
alert_type="llm_requests_hanging",
)
async def failed_tracking_alert(self, error_message: str):
"""Raise alert when tracking failed for specific model"""
_cache: DualCache = self.internal_usage_cache
message = "Failed Tracking Cost for" + error_message
_cache_key = "budget_alerts:failed_tracking:{}".format(message)
result = await _cache.async_get_cache(key=_cache_key)
if result is None:
await self.send_alert(
message=message, level="High", alert_type="budget_alerts"
)
await _cache.async_set_cache(
key=_cache_key,
value="SENT",
ttl=self.alerting_args.budget_alert_ttl,
)
async def budget_alerts(
self,
type: Literal[
"token_budget",
"user_budget",
"team_budget",
"proxy_budget",
"projected_limit_exceeded",
],
user_info: CallInfo,
):
## PREVENTITIVE ALERTING ## - https://github.com/BerriAI/litellm/issues/2727
# - Alert once within 24hr period
# - Cache this information
# - Don't re-alert, if alert already sent
_cache: DualCache = self.internal_usage_cache
if self.alerting is None or self.alert_types is None:
# do nothing if alerting is not switched on
return
if "budget_alerts" not in self.alert_types:
return
_id: str = "default_id" # used for caching
user_info_json = user_info.model_dump(exclude_none=True)
for k, v in user_info_json.items():
user_info_str = "\n{}: {}\n".format(k, v)
event: Optional[
Literal["budget_crossed", "threshold_crossed", "projected_limit_exceeded"]
] = None
event_group: Optional[Literal["user", "team", "key", "proxy"]] = None
event_message: str = ""
webhook_event: Optional[WebhookEvent] = None
if type == "proxy_budget":
event_group = "proxy"
event_message += "Proxy Budget: "
elif type == "user_budget":
event_group = "user"
event_message += "User Budget: "
_id = user_info.user_id or _id
elif type == "team_budget":
event_group = "team"
event_message += "Team Budget: "
_id = user_info.team_id or _id
elif type == "token_budget":
event_group = "key"
event_message += "Key Budget: "
_id = user_info.token
elif type == "projected_limit_exceeded":
event_group = "key"
event_message += "Key Budget: Projected Limit Exceeded"
event = "projected_limit_exceeded"
_id = user_info.token
# percent of max_budget left to spend
if user_info.max_budget > 0:
percent_left = (
user_info.max_budget - user_info.spend
) / user_info.max_budget
else:
percent_left = 0
# check if crossed budget
if user_info.spend >= user_info.max_budget:
event = "budget_crossed"
event_message += f"Budget Crossed\n Total Budget:`{user_info.max_budget}`"
elif percent_left <= 0.05:
event = "threshold_crossed"
event_message += "5% Threshold Crossed "
elif percent_left <= 0.15:
event = "threshold_crossed"
event_message += "15% Threshold Crossed"
if event is not None and event_group is not None:
_cache_key = "budget_alerts:{}:{}".format(event, _id)
result = await _cache.async_get_cache(key=_cache_key)
if result is None:
webhook_event = WebhookEvent(
event=event,
event_group=event_group,
event_message=event_message,
**user_info_json,
)
await self.send_alert(
message=event_message + "\n\n" + user_info_str,
level="High",
alert_type="budget_alerts",
user_info=webhook_event,
)
await _cache.async_set_cache(
key=_cache_key,
value="SENT",
ttl=self.alerting_args.budget_alert_ttl,
)
return
return
async def model_added_alert(
self, model_name: str, litellm_model_name: str, passed_model_info: Any
):
base_model_from_user = getattr(passed_model_info, "base_model", None)
model_info = {}
base_model = ""
if base_model_from_user is not None:
model_info = litellm.model_cost.get(base_model_from_user, {})
base_model = f"Base Model: `{base_model_from_user}`\n"
else:
model_info = litellm.model_cost.get(litellm_model_name, {})
model_info_str = ""
for k, v in model_info.items():
if k == "input_cost_per_token" or k == "output_cost_per_token":
# when converting to string it should not be 1.63e-06
v = "{:.8f}".format(v)
model_info_str += f"{k}: {v}\n"
message = f"""
*š
New Model Added*
Model Name: `{model_name}`
{base_model}
Usage OpenAI Python SDK:
```
import openai
client = openai.OpenAI(
api_key="your_api_key",
base_url={os.getenv("PROXY_BASE_URL", "http://0.0.0.0:4000")}
)
response = client.chat.completions.create(
model="{model_name}", # model to send to the proxy
messages = [
{{
"role": "user",
"content": "this is a test request, write a short poem"
}}
]
)
```
Model Info:
```
{model_info_str}
```
"""
await self.send_alert(
message=message, level="Low", alert_type="new_model_added"
)
pass
async def model_removed_alert(self, model_name: str):
pass
async def send_webhook_alert(self, webhook_event: WebhookEvent) -> bool:
"""
Sends structured alert to webhook, if set.
Currently only implemented for budget alerts
Returns -> True if sent, False if not.
"""
webhook_url = os.getenv("WEBHOOK_URL", None)
if webhook_url is None:
raise Exception("Missing webhook_url from environment")
payload = webhook_event.model_dump_json()
headers = {"Content-type": "application/json"}
response = await self.async_http_handler.post(
url=webhook_url,
headers=headers,
data=payload,
)
if response.status_code == 200:
return True
else:
print("Error sending webhook alert. Error=", response.text) # noqa
return False
async def send_key_created_email(self, webhook_event: WebhookEvent) -> bool:
from litellm.proxy.utils import send_email
if self.alerting is None or "email" not in self.alerting:
# do nothing if user does not want email alerts
return False
# make sure this is a premium user
from litellm.proxy.proxy_server import premium_user
from litellm.proxy.proxy_server import CommonProxyErrors, prisma_client
if premium_user != True:
raise Exception(
f"Trying to use Email Alerting on key creation\n {CommonProxyErrors.not_premium_user.value}"
)
event_name = webhook_event.event_message
recipient_email = webhook_event.user_email
recipient_user_id = webhook_event.user_id
if (
recipient_email is None
and recipient_user_id is not None
and prisma_client is not None
):
user_row = await prisma_client.db.litellm_usertable.find_unique(
where={"user_id": recipient_user_id}
)
if user_row is not None:
recipient_email = user_row.user_email
key_name = webhook_event.key_alias
key_token = webhook_event.token
key_budget = webhook_event.max_budget
email_html_content = "Alert from LiteLLM Server"
if recipient_email is None:
verbose_proxy_logger.error(
"Trying to send email alert to no recipient", extra=webhook_event.dict()
)
email_html_content = f"""
Hi {recipient_email},
I'm happy to provide you with an OpenAI Proxy API Key, loaded with ${key_budget} per month.
Key:
{key_token}
Usage Example
Detailed Documentation on Usage with OpenAI Python SDK, Langchain, LlamaIndex, Curl
import openai
client = openai.OpenAI(
api_key="{key_token}",
base_url={os.getenv("PROXY_BASE_URL", "http://0.0.0.0:4000")}
)
response = client.chat.completions.create(
model="gpt-3.5-turbo", # model to send to the proxy
messages = [
{{
"role": "user",
"content": "this is a test request, write a short poem"
}}
]
)
If you have any questions, please send an email to support@berri.ai
Best,
The LiteLLM team
"""
payload = webhook_event.model_dump_json()
email_event = {
"to": recipient_email,
"subject": f"LiteLLM: {event_name}",
"html": email_html_content,
}
response = await send_email(
receiver_email=email_event["to"],
subject=email_event["subject"],
html=email_event["html"],
)
return False
async def send_email_alert_using_smtp(self, webhook_event: WebhookEvent) -> bool:
"""
Sends structured Email alert to an SMTP server
Currently only implemented for budget alerts
Returns -> True if sent, False if not.
"""
from litellm.proxy.utils import send_email
event_name = webhook_event.event_message
recipient_email = webhook_event.user_email
user_name = webhook_event.user_id
max_budget = webhook_event.max_budget
email_html_content = "Alert from LiteLLM Server"
if recipient_email is None:
verbose_proxy_logger.error(
"Trying to send email alert to no recipient", extra=webhook_event.dict()
)
if webhook_event.event == "budget_crossed":
email_html_content = f"""
Hi {user_name},
Your LLM API usage this month has reached your account's monthly budget of ${max_budget}
API requests will be rejected until either (a) you increase your monthly budget or (b) your monthly usage resets at the beginning of the next calendar month.
If you have any questions, please send an email to support@berri.ai
Best,
The LiteLLM team
"""
payload = webhook_event.model_dump_json()
email_event = {
"to": recipient_email,
"subject": f"LiteLLM: {event_name}",
"html": email_html_content,
}
response = await send_email(
receiver_email=email_event["to"],
subject=email_event["subject"],
html=email_event["html"],
)
return False
async def send_alert(
self,
message: str,
level: Literal["Low", "Medium", "High"],
alert_type: Literal[
"llm_exceptions",
"llm_too_slow",
"llm_requests_hanging",
"budget_alerts",
"db_exceptions",
"daily_reports",
"spend_reports",
"new_model_added",
"cooldown_deployment",
],
user_info: Optional[WebhookEvent] = None,
**kwargs,
):
"""
Alerting based on thresholds: - https://github.com/BerriAI/litellm/issues/1298
- Responses taking too long
- Requests are hanging
- Calls are failing
- DB Read/Writes are failing
- Proxy Close to max budget
- Key Close to max budget
Parameters:
level: str - Low|Medium|High - if calls might fail (Medium) or are failing (High); Currently, no alerts would be 'Low'.
message: str - what is the alert about
"""
if self.alerting is None:
return
if (
"webhook" in self.alerting
and alert_type == "budget_alerts"
and user_info is not None
):
await self.send_webhook_alert(webhook_event=user_info)
if (
"email" in self.alerting
and alert_type == "budget_alerts"
and user_info is not None
):
# only send budget alerts over Email
await self.send_email_alert_using_smtp(webhook_event=user_info)
if "slack" not in self.alerting:
return
if alert_type not in self.alert_types:
return
from datetime import datetime
import json
# Get the current timestamp
current_time = datetime.now().strftime("%H:%M:%S")
_proxy_base_url = os.getenv("PROXY_BASE_URL", None)
if alert_type == "daily_reports" or alert_type == "new_model_added":
formatted_message = message
else:
formatted_message = (
f"Level: `{level}`\nTimestamp: `{current_time}`\n\nMessage: {message}"
)
if kwargs:
for key, value in kwargs.items():
formatted_message += f"\n\n{key}: `{value}`\n\n"
if _proxy_base_url is not None:
formatted_message += f"\n\nProxy URL: `{_proxy_base_url}`"
# check if we find the slack webhook url in self.alert_to_webhook_url
if (
self.alert_to_webhook_url is not None
and alert_type in self.alert_to_webhook_url
):
slack_webhook_url = self.alert_to_webhook_url[alert_type]
elif self.default_webhook_url is not None:
slack_webhook_url = self.default_webhook_url
else:
slack_webhook_url = os.getenv("SLACK_WEBHOOK_URL", None)
if slack_webhook_url is None:
raise Exception("Missing SLACK_WEBHOOK_URL from environment")
payload = {"text": formatted_message}
headers = {"Content-type": "application/json"}
response = await self.async_http_handler.post(
url=slack_webhook_url,
headers=headers,
data=json.dumps(payload),
)
if response.status_code == 200:
pass
else:
print("Error sending slack alert. Error=", response.text) # noqa
async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
"""Log deployment latency"""
try:
if "daily_reports" in self.alert_types:
model_id = (
kwargs.get("litellm_params", {}).get("model_info", {}).get("id", "")
)
response_s: timedelta = end_time - start_time
final_value = response_s
total_tokens = 0
if isinstance(response_obj, litellm.ModelResponse):
completion_tokens = response_obj.usage.completion_tokens
if completion_tokens is not None and completion_tokens > 0:
final_value = float(
response_s.total_seconds() / completion_tokens
)
await self.async_update_daily_reports(
DeploymentMetrics(
id=model_id,
failed_request=False,
latency_per_output_token=final_value,
updated_at=litellm.utils.get_utc_datetime(),
)
)
except Exception as e:
verbose_proxy_logger.error(
"[Non-Blocking Error] Slack Alerting: Got error in logging LLM deployment latency: ",
e,
)
pass
async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
"""Log failure + deployment latency"""
if "daily_reports" in self.alert_types:
model_id = (
kwargs.get("litellm_params", {}).get("model_info", {}).get("id", "")
)
await self.async_update_daily_reports(
DeploymentMetrics(
id=model_id,
failed_request=True,
latency_per_output_token=None,
updated_at=litellm.utils.get_utc_datetime(),
)
)
async def _run_scheduler_helper(self, llm_router) -> bool:
"""
Returns:
- True -> report sent
- False -> report not sent
"""
report_sent_bool = False
report_sent = await self.internal_usage_cache.async_get_cache(
key=SlackAlertingCacheKeys.report_sent_key.value
) # None | float
current_time = time.time()
if report_sent is None:
await self.internal_usage_cache.async_set_cache(
key=SlackAlertingCacheKeys.report_sent_key.value,
value=current_time,
)
elif isinstance(report_sent, float):
# Check if current time - interval >= time last sent
interval_seconds = self.alerting_args.daily_report_frequency
if current_time - report_sent >= interval_seconds:
# Sneak in the reporting logic here
await self.send_daily_reports(router=llm_router)
# Also, don't forget to update the report_sent time after sending the report!
await self.internal_usage_cache.async_set_cache(
key=SlackAlertingCacheKeys.report_sent_key.value,
value=current_time,
)
report_sent_bool = True
return report_sent_bool
async def _run_scheduled_daily_report(self, llm_router: Optional[Any] = None):
"""
If 'daily_reports' enabled
Ping redis cache every 5 minutes to check if we should send the report
If yes -> call send_daily_report()
"""
if llm_router is None or self.alert_types is None:
return
if "daily_reports" in self.alert_types:
while True:
await self._run_scheduler_helper(llm_router=llm_router)
interval = random.randint(
self.alerting_args.report_check_interval - 3,
self.alerting_args.report_check_interval + 3,
) # shuffle to prevent collisions
await asyncio.sleep(interval)
return
async def send_weekly_spend_report(self):
""" """
try:
from litellm.proxy.proxy_server import _get_spend_report_for_time_range
todays_date = datetime.datetime.now().date()
week_before = todays_date - datetime.timedelta(days=7)
weekly_spend_per_team, weekly_spend_per_tag = (
await _get_spend_report_for_time_range(
start_date=week_before.strftime("%Y-%m-%d"),
end_date=todays_date.strftime("%Y-%m-%d"),
)
)
_weekly_spend_message = f"*šø Weekly Spend Report for `{week_before.strftime('%m-%d-%Y')} - {todays_date.strftime('%m-%d-%Y')}` *\n"
if weekly_spend_per_team is not None:
_weekly_spend_message += "\n*Team Spend Report:*\n"
for spend in weekly_spend_per_team:
_team_spend = spend["total_spend"]
_team_spend = float(_team_spend)
# round to 4 decimal places
_team_spend = round(_team_spend, 4)
_weekly_spend_message += (
f"Team: `{spend['team_alias']}` | Spend: `${_team_spend}`\n"
)
if weekly_spend_per_tag is not None:
_weekly_spend_message += "\n*Tag Spend Report:*\n"
for spend in weekly_spend_per_tag:
_tag_spend = spend["total_spend"]
_tag_spend = float(_tag_spend)
# round to 4 decimal places
_tag_spend = round(_tag_spend, 4)
_weekly_spend_message += f"Tag: `{spend['individual_request_tag']}` | Spend: `${_tag_spend}`\n"
await self.send_alert(
message=_weekly_spend_message,
level="Low",
alert_type="spend_reports",
)
except Exception as e:
verbose_proxy_logger.error("Error sending weekly spend report", e)
async def send_monthly_spend_report(self):
""" """
try:
from calendar import monthrange
from litellm.proxy.proxy_server import _get_spend_report_for_time_range
todays_date = datetime.datetime.now().date()
first_day_of_month = todays_date.replace(day=1)
_, last_day_of_month = monthrange(todays_date.year, todays_date.month)
last_day_of_month = first_day_of_month + datetime.timedelta(
days=last_day_of_month - 1
)
monthly_spend_per_team, monthly_spend_per_tag = (
await _get_spend_report_for_time_range(
start_date=first_day_of_month.strftime("%Y-%m-%d"),
end_date=last_day_of_month.strftime("%Y-%m-%d"),
)
)
_spend_message = f"*šø Monthly Spend Report for `{first_day_of_month.strftime('%m-%d-%Y')} - {last_day_of_month.strftime('%m-%d-%Y')}` *\n"
if monthly_spend_per_team is not None:
_spend_message += "\n*Team Spend Report:*\n"
for spend in monthly_spend_per_team:
_team_spend = spend["total_spend"]
_team_spend = float(_team_spend)
# round to 4 decimal places
_team_spend = round(_team_spend, 4)
_spend_message += (
f"Team: `{spend['team_alias']}` | Spend: `${_team_spend}`\n"
)
if monthly_spend_per_tag is not None:
_spend_message += "\n*Tag Spend Report:*\n"
for spend in monthly_spend_per_tag:
_tag_spend = spend["total_spend"]
_tag_spend = float(_tag_spend)
# round to 4 decimal places
_tag_spend = round(_tag_spend, 4)
_spend_message += f"Tag: `{spend['individual_request_tag']}` | Spend: `${_tag_spend}`\n"
await self.send_alert(
message=_spend_message,
level="Low",
alert_type="spend_reports",
)
except Exception as e:
verbose_proxy_logger.error("Error sending weekly spend report", e)