forked from phoenix/litellm-mirror
7e9d8b58f6
* feat(pass_through_endpoints/): support logging anthropic/gemini pass through calls to langfuse/s3/etc. * fix(utils.py): allow disabling end user cost tracking with new param Allows proxy admin to disable cost tracking for end user - keeps prometheus metrics small * docs(configs.md): add disable_end_user_cost_tracking reference to docs * feat(key_management_endpoints.py): add support for restricting access to `/key/generate` by team/proxy level role Enables admin to restrict key creation, and assign team admins to handle distributing keys * test(test_key_management.py): add unit testing for personal / team key restriction checks * docs: add docs on restricting key creation * docs(finetuned_models.md): add new guide on calling finetuned models * docs(input.md): cleanup anthropic supported params Closes https://github.com/BerriAI/litellm/issues/6856 * test(test_embedding.py): add test for passing extra headers via embedding * feat(cohere/embed): pass client to async embedding * feat(rerank.py): add `/v1/rerank` if missing for cohere base url Closes https://github.com/BerriAI/litellm/issues/6844 * fix(main.py): pass extra_headers param to openai Fixes https://github.com/BerriAI/litellm/issues/6836 * fix(litellm_logging.py): don't disable global callbacks when dynamic callbacks are set Fixes issue where global callbacks - e.g. prometheus were overriden when langfuse was set dynamically * fix(handler.py): fix linting error * fix: fix typing * build: add conftest to proxy_admin_ui_tests/ * test: fix test * fix: fix linting errors * test: fix test * fix: fix pass through testing
1160 lines
44 KiB
Python
1160 lines
44 KiB
Python
# used for /metrics endpoint on LiteLLM Proxy
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#### What this does ####
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# On success, log events to Prometheus
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import os
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import subprocess
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import sys
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import traceback
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import uuid
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from datetime import date, datetime, timedelta
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from typing import Optional, TypedDict, Union
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import dotenv
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import requests # type: ignore
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import litellm
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from litellm._logging import print_verbose, verbose_logger
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from litellm.integrations.custom_logger import CustomLogger
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from litellm.proxy._types import UserAPIKeyAuth
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from litellm.types.integrations.prometheus import *
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from litellm.types.utils import StandardLoggingPayload
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from litellm.utils import get_end_user_id_for_cost_tracking
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class PrometheusLogger(CustomLogger):
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# Class variables or attributes
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def __init__(
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self,
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**kwargs,
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):
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try:
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from prometheus_client import Counter, Gauge, Histogram
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from litellm.proxy.proxy_server import CommonProxyErrors, premium_user
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if premium_user is not True:
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verbose_logger.warning(
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f"🚨🚨🚨 Prometheus Metrics is on LiteLLM Enterprise\n🚨 {CommonProxyErrors.not_premium_user.value}"
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)
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self.litellm_not_a_premium_user_metric = Counter(
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name="litellm_not_a_premium_user_metric",
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documentation=f"🚨🚨🚨 Prometheus Metrics is on LiteLLM Enterprise. 🚨 {CommonProxyErrors.not_premium_user.value}",
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)
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return
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self.litellm_proxy_failed_requests_metric = Counter(
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name="litellm_proxy_failed_requests_metric",
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documentation="Total number of failed responses from proxy - the client did not get a success response from litellm proxy",
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labelnames=[
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"end_user",
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"hashed_api_key",
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"api_key_alias",
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REQUESTED_MODEL,
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"team",
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"team_alias",
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"user",
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]
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+ EXCEPTION_LABELS,
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)
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self.litellm_proxy_total_requests_metric = Counter(
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name="litellm_proxy_total_requests_metric",
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documentation="Total number of requests made to the proxy server - track number of client side requests",
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labelnames=[
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"end_user",
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"hashed_api_key",
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"api_key_alias",
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REQUESTED_MODEL,
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"team",
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"team_alias",
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"user",
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],
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)
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# request latency metrics
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self.litellm_request_total_latency_metric = Histogram(
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"litellm_request_total_latency_metric",
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"Total latency (seconds) for a request to LiteLLM",
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labelnames=[
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"model",
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"hashed_api_key",
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"api_key_alias",
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"team",
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"team_alias",
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],
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buckets=LATENCY_BUCKETS,
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)
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self.litellm_llm_api_latency_metric = Histogram(
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"litellm_llm_api_latency_metric",
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"Total latency (seconds) for a models LLM API call",
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labelnames=[
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"model",
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"hashed_api_key",
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"api_key_alias",
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"team",
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"team_alias",
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],
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buckets=LATENCY_BUCKETS,
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)
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self.litellm_llm_api_time_to_first_token_metric = Histogram(
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"litellm_llm_api_time_to_first_token_metric",
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"Time to first token for a models LLM API call",
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labelnames=[
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"model",
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"hashed_api_key",
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"api_key_alias",
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"team",
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"team_alias",
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],
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buckets=LATENCY_BUCKETS,
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)
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# Counter for spend
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self.litellm_spend_metric = Counter(
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"litellm_spend_metric",
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"Total spend on LLM requests",
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labelnames=[
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"end_user",
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"hashed_api_key",
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"api_key_alias",
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"model",
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"team",
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"team_alias",
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"user",
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],
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)
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# Counter for total_output_tokens
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self.litellm_tokens_metric = Counter(
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"litellm_total_tokens",
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"Total number of input + output tokens from LLM requests",
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labelnames=[
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"end_user",
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"hashed_api_key",
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"api_key_alias",
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"model",
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"team",
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"team_alias",
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"user",
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],
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)
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self.litellm_input_tokens_metric = Counter(
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"litellm_input_tokens",
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"Total number of input tokens from LLM requests",
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labelnames=[
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"end_user",
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"hashed_api_key",
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"api_key_alias",
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"model",
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"team",
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"team_alias",
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"user",
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],
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)
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self.litellm_output_tokens_metric = Counter(
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"litellm_output_tokens",
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"Total number of output tokens from LLM requests",
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labelnames=[
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"end_user",
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"hashed_api_key",
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"api_key_alias",
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"model",
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"team",
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"team_alias",
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"user",
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],
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)
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# Remaining Budget for Team
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self.litellm_remaining_team_budget_metric = Gauge(
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"litellm_remaining_team_budget_metric",
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"Remaining budget for team",
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labelnames=["team_id", "team_alias"],
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)
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# Remaining Budget for API Key
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self.litellm_remaining_api_key_budget_metric = Gauge(
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"litellm_remaining_api_key_budget_metric",
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"Remaining budget for api key",
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labelnames=["hashed_api_key", "api_key_alias"],
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)
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########################################
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# LiteLLM Virtual API KEY metrics
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########################################
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# Remaining MODEL RPM limit for API Key
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self.litellm_remaining_api_key_requests_for_model = Gauge(
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"litellm_remaining_api_key_requests_for_model",
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"Remaining Requests API Key can make for model (model based rpm limit on key)",
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labelnames=["hashed_api_key", "api_key_alias", "model"],
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)
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# Remaining MODEL TPM limit for API Key
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self.litellm_remaining_api_key_tokens_for_model = Gauge(
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"litellm_remaining_api_key_tokens_for_model",
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"Remaining Tokens API Key can make for model (model based tpm limit on key)",
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labelnames=["hashed_api_key", "api_key_alias", "model"],
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)
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########################################
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# LLM API Deployment Metrics / analytics
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########################################
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# Remaining Rate Limit for model
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self.litellm_remaining_requests_metric = Gauge(
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"litellm_remaining_requests",
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"LLM Deployment Analytics - remaining requests for model, returned from LLM API Provider",
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labelnames=[
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"model_group",
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"api_provider",
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"api_base",
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"litellm_model_name",
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"hashed_api_key",
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"api_key_alias",
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],
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)
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self.litellm_remaining_tokens_metric = Gauge(
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"litellm_remaining_tokens",
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"remaining tokens for model, returned from LLM API Provider",
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labelnames=[
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"model_group",
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"api_provider",
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"api_base",
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"litellm_model_name",
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"hashed_api_key",
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"api_key_alias",
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],
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)
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# llm api provider budget metrics
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self.litellm_provider_remaining_budget_metric = Gauge(
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"litellm_provider_remaining_budget_metric",
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"Remaining budget for provider - used when you set provider budget limits",
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labelnames=["api_provider"],
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)
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# Get all keys
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_logged_llm_labels = [
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"litellm_model_name",
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"model_id",
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"api_base",
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"api_provider",
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]
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team_and_key_labels = [
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"hashed_api_key",
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"api_key_alias",
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"team",
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"team_alias",
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]
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# Metric for deployment state
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self.litellm_deployment_state = Gauge(
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"litellm_deployment_state",
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"LLM Deployment Analytics - The state of the deployment: 0 = healthy, 1 = partial outage, 2 = complete outage",
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labelnames=_logged_llm_labels,
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)
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self.litellm_deployment_cooled_down = Counter(
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"litellm_deployment_cooled_down",
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"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",
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labelnames=_logged_llm_labels + [EXCEPTION_STATUS],
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)
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self.litellm_deployment_success_responses = Counter(
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name="litellm_deployment_success_responses",
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documentation="LLM Deployment Analytics - Total number of successful LLM API calls via litellm",
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labelnames=[REQUESTED_MODEL] + _logged_llm_labels + team_and_key_labels,
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)
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self.litellm_deployment_failure_responses = Counter(
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name="litellm_deployment_failure_responses",
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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",
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labelnames=[REQUESTED_MODEL]
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+ _logged_llm_labels
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+ EXCEPTION_LABELS
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+ team_and_key_labels,
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)
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self.litellm_deployment_total_requests = Counter(
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name="litellm_deployment_total_requests",
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documentation="LLM Deployment Analytics - Total number of LLM API calls via litellm - success + failure",
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labelnames=[REQUESTED_MODEL] + _logged_llm_labels + team_and_key_labels,
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)
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# Deployment Latency tracking
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team_and_key_labels = [
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"hashed_api_key",
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"api_key_alias",
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"team",
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"team_alias",
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]
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self.litellm_deployment_latency_per_output_token = Histogram(
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name="litellm_deployment_latency_per_output_token",
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documentation="LLM Deployment Analytics - Latency per output token",
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labelnames=_logged_llm_labels + team_and_key_labels,
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)
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self.litellm_deployment_successful_fallbacks = Counter(
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"litellm_deployment_successful_fallbacks",
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"LLM Deployment Analytics - Number of successful fallback requests from primary model -> fallback model",
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[REQUESTED_MODEL, "fallback_model"]
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+ team_and_key_labels
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+ EXCEPTION_LABELS,
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)
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self.litellm_deployment_failed_fallbacks = Counter(
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"litellm_deployment_failed_fallbacks",
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"LLM Deployment Analytics - Number of failed fallback requests from primary model -> fallback model",
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[REQUESTED_MODEL, "fallback_model"]
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+ team_and_key_labels
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+ EXCEPTION_LABELS,
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)
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self.litellm_llm_api_failed_requests_metric = Counter(
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name="litellm_llm_api_failed_requests_metric",
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documentation="deprecated - use litellm_proxy_failed_requests_metric",
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labelnames=[
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"end_user",
|
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"hashed_api_key",
|
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"api_key_alias",
|
|
"model",
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"team",
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"team_alias",
|
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"user",
|
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],
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)
|
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self.litellm_requests_metric = Counter(
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name="litellm_requests_metric",
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documentation="deprecated - use litellm_proxy_total_requests_metric. Total number of LLM calls to litellm - track total per API Key, team, user",
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labelnames=[
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"end_user",
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"hashed_api_key",
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"api_key_alias",
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"model",
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"team",
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"team_alias",
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"user",
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],
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)
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except Exception as e:
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print_verbose(f"Got exception on init prometheus client {str(e)}")
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raise e
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async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
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# Define prometheus client
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from litellm.types.utils import StandardLoggingPayload
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verbose_logger.debug(
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f"prometheus Logging - Enters success logging function for kwargs {kwargs}"
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)
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# unpack kwargs
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standard_logging_payload: Optional[StandardLoggingPayload] = kwargs.get(
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"standard_logging_object"
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)
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if standard_logging_payload is None or not isinstance(
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standard_logging_payload, dict
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):
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raise ValueError(
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f"standard_logging_object is required, got={standard_logging_payload}"
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)
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model = kwargs.get("model", "")
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litellm_params = kwargs.get("litellm_params", {}) or {}
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_metadata = litellm_params.get("metadata", {})
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end_user_id = get_end_user_id_for_cost_tracking(litellm_params)
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user_id = standard_logging_payload["metadata"]["user_api_key_user_id"]
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user_api_key = standard_logging_payload["metadata"]["user_api_key_hash"]
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user_api_key_alias = standard_logging_payload["metadata"]["user_api_key_alias"]
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user_api_team = standard_logging_payload["metadata"]["user_api_key_team_id"]
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user_api_team_alias = standard_logging_payload["metadata"][
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"user_api_key_team_alias"
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]
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output_tokens = standard_logging_payload["completion_tokens"]
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tokens_used = standard_logging_payload["total_tokens"]
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response_cost = standard_logging_payload["response_cost"]
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print_verbose(
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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}"
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)
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if (
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user_api_key is not None
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and isinstance(user_api_key, str)
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and user_api_key.startswith("sk-")
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):
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from litellm.proxy.utils import hash_token
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|
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user_api_key = hash_token(user_api_key)
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|
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# increment total LLM requests and spend metric
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|
self._increment_top_level_request_and_spend_metrics(
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end_user_id=end_user_id,
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user_api_key=user_api_key,
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user_api_key_alias=user_api_key_alias,
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model=model,
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user_api_team=user_api_team,
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user_api_team_alias=user_api_team_alias,
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user_id=user_id,
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response_cost=response_cost,
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)
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|
|
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# input, output, total token metrics
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|
self._increment_token_metrics(
|
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# why type ignore below?
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|
# 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,
|
|
)
|
|
|
|
# remaining budget metrics
|
|
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,
|
|
)
|
|
|
|
# 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
|
|
standard_logging_payload=standard_logging_payload, # type: ignore
|
|
)
|
|
|
|
# set x-ratelimit headers
|
|
self.set_llm_deployment_success_metrics(
|
|
kwargs, start_time, end_time, output_tokens
|
|
)
|
|
pass
|
|
|
|
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],
|
|
):
|
|
# 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"])
|
|
|
|
self.litellm_input_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["prompt_tokens"])
|
|
|
|
self.litellm_output_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["completion_tokens"])
|
|
|
|
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,
|
|
):
|
|
_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
|
|
)
|
|
_remaining_team_budget = self._safe_get_remaining_budget(
|
|
max_budget=_team_max_budget, spend=_team_spend
|
|
)
|
|
|
|
_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
|
|
)
|
|
_remaining_api_key_budget = self._safe_get_remaining_budget(
|
|
max_budget=_api_key_max_budget, spend=_api_key_spend
|
|
)
|
|
# Remaining Budget Metrics
|
|
self.litellm_remaining_team_budget_metric.labels(
|
|
user_api_team, user_api_team_alias
|
|
).set(_remaining_team_budget)
|
|
|
|
self.litellm_remaining_api_key_budget_metric.labels(
|
|
user_api_key, user_api_key_alias
|
|
).set(_remaining_api_key_budget)
|
|
|
|
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,
|
|
):
|
|
self.litellm_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.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)
|
|
remaining_tokens = metadata.get(remaining_tokens_variable_name, 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],
|
|
standard_logging_payload: StandardLoggingPayload,
|
|
):
|
|
# latency metrics
|
|
model_parameters: dict = standard_logging_payload["model_parameters"]
|
|
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)
|
|
|
|
if (
|
|
completion_start_time is not None
|
|
and isinstance(completion_start_time, datetime)
|
|
and model_parameters.get("stream")
|
|
is True # only emit for streaming requests
|
|
):
|
|
time_to_first_token_seconds = (
|
|
completion_start_time - api_call_start_time
|
|
).total_seconds()
|
|
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"
|
|
)
|
|
if api_call_start_time is not None and isinstance(
|
|
api_call_start_time, datetime
|
|
):
|
|
api_call_total_time: timedelta = end_time - api_call_start_time
|
|
api_call_total_time_seconds = api_call_total_time.total_seconds()
|
|
self.litellm_llm_api_latency_metric.labels(
|
|
model,
|
|
user_api_key,
|
|
user_api_key_alias,
|
|
user_api_team,
|
|
user_api_team_alias,
|
|
).observe(api_call_total_time_seconds)
|
|
|
|
# total request latency
|
|
if start_time is not None and isinstance(start_time, datetime):
|
|
total_time: timedelta = end_time - start_time
|
|
total_time_seconds = total_time.total_seconds()
|
|
self.litellm_request_total_latency_metric.labels(
|
|
model,
|
|
user_api_key,
|
|
user_api_key_alias,
|
|
user_api_team,
|
|
user_api_team_alias,
|
|
).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)
|
|
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:
|
|
self.litellm_proxy_failed_requests_metric.labels(
|
|
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=request_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,
|
|
exception_status=getattr(original_exception, "status_code", None),
|
|
exception_class=str(original_exception.__class__.__name__),
|
|
).inc()
|
|
|
|
self.litellm_proxy_total_requests_metric.labels(
|
|
user_api_key_dict.end_user_id,
|
|
user_api_key_dict.api_key,
|
|
user_api_key_dict.key_alias,
|
|
request_data.get("model", ""),
|
|
user_api_key_dict.team_id,
|
|
user_api_key_dict.team_alias,
|
|
user_api_key_dict.user_id,
|
|
).inc()
|
|
pass
|
|
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:
|
|
self.litellm_proxy_total_requests_metric.labels(
|
|
user_api_key_dict.end_user_id,
|
|
user_api_key_dict.api_key,
|
|
user_api_key_dict.key_alias,
|
|
data.get("model", ""),
|
|
user_api_key_dict.team_id,
|
|
user_api_key_dict.team_alias,
|
|
user_api_key_dict.user_id,
|
|
).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()
|
|
|
|
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:
|
|
pass
|
|
|
|
def set_llm_deployment_success_metrics(
|
|
self,
|
|
request_kwargs: dict,
|
|
start_time,
|
|
end_time,
|
|
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 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
|
|
self.litellm_deployment_latency_per_output_token.labels(
|
|
litellm_model_name=litellm_model_name,
|
|
model_id=model_id,
|
|
api_base=api_base,
|
|
api_provider=llm_provider,
|
|
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"
|
|
],
|
|
).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")
|
|
self.litellm_deployment_successful_fallbacks.labels(
|
|
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__),
|
|
).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", {})
|
|
standard_metadata: StandardLoggingMetadata = (
|
|
StandardLoggingPayloadSetup.get_standard_logging_metadata(
|
|
metadata=_metadata
|
|
)
|
|
)
|
|
self.litellm_deployment_failed_fallbacks.labels(
|
|
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__),
|
|
).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
|