# +-----------------------------------------------+ # | | # | Give Feedback / Get Help | # | https://github.com/BerriAI/litellm/issues/new | # | | # +-----------------------------------------------+ # # Thank you ! We ❤️ you! - Krrish & Ishaan import asyncio import copy import enum import hashlib import inspect import json import logging import threading import time import traceback import uuid from collections import defaultdict from functools import lru_cache from typing import ( TYPE_CHECKING, Any, Callable, Dict, List, Literal, Optional, Tuple, Union, cast, ) import httpx import openai from openai import AsyncOpenAI from pydantic import BaseModel from typing_extensions import overload import litellm import litellm.litellm_core_utils import litellm.litellm_core_utils.exception_mapping_utils from litellm import get_secret_str from litellm._logging import verbose_router_logger from litellm.caching.caching import DualCache, InMemoryCache, RedisCache from litellm.integrations.custom_logger import CustomLogger from litellm.litellm_core_utils.asyncify import run_async_function from litellm.litellm_core_utils.core_helpers import _get_parent_otel_span_from_kwargs from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging from litellm.router_strategy.budget_limiter import RouterBudgetLimiting from litellm.router_strategy.least_busy import LeastBusyLoggingHandler from litellm.router_strategy.lowest_cost import LowestCostLoggingHandler from litellm.router_strategy.lowest_latency import LowestLatencyLoggingHandler from litellm.router_strategy.lowest_tpm_rpm import LowestTPMLoggingHandler from litellm.router_strategy.lowest_tpm_rpm_v2 import LowestTPMLoggingHandler_v2 from litellm.router_strategy.simple_shuffle import simple_shuffle from litellm.router_strategy.tag_based_routing import get_deployments_for_tag from litellm.router_utils.add_retry_headers import add_retry_headers_to_response from litellm.router_utils.batch_utils import ( _get_router_metadata_variable_name, replace_model_in_jsonl, ) from litellm.router_utils.client_initalization_utils import InitalizeOpenAISDKClient from litellm.router_utils.cooldown_cache import CooldownCache from litellm.router_utils.cooldown_handlers import ( DEFAULT_COOLDOWN_TIME_SECONDS, _async_get_cooldown_deployments, _async_get_cooldown_deployments_with_debug_info, _get_cooldown_deployments, _set_cooldown_deployments, ) from litellm.router_utils.fallback_event_handlers import ( _check_non_standard_fallback_format, get_fallback_model_group, run_async_fallback, ) from litellm.router_utils.get_retry_from_policy import ( get_num_retries_from_retry_policy as _get_num_retries_from_retry_policy, ) from litellm.router_utils.handle_error import ( async_raise_no_deployment_exception, send_llm_exception_alert, ) from litellm.router_utils.pre_call_checks.prompt_caching_deployment_check import ( PromptCachingDeploymentCheck, ) from litellm.router_utils.router_callbacks.track_deployment_metrics import ( increment_deployment_failures_for_current_minute, increment_deployment_successes_for_current_minute, ) from litellm.scheduler import FlowItem, Scheduler from litellm.types.llms.openai import AllMessageValues, Batch, FileObject, FileTypes from litellm.types.router import ( CONFIGURABLE_CLIENTSIDE_AUTH_PARAMS, VALID_LITELLM_ENVIRONMENTS, AlertingConfig, AllowedFailsPolicy, AssistantsTypedDict, CustomRoutingStrategyBase, Deployment, DeploymentTypedDict, LiteLLM_Params, ModelGroupInfo, OptionalPreCallChecks, RetryPolicy, RouterCacheEnum, RouterGeneralSettings, RouterModelGroupAliasItem, RouterRateLimitError, RouterRateLimitErrorBasic, RoutingStrategy, ) from litellm.types.services import ServiceTypes from litellm.types.utils import GenericBudgetConfigType from litellm.types.utils import ModelInfo as ModelMapInfo from litellm.types.utils import StandardLoggingPayload from litellm.utils import ( CustomStreamWrapper, EmbeddingResponse, ModelResponse, Rules, function_setup, get_llm_provider, get_non_default_completion_params, get_secret, get_utc_datetime, is_region_allowed, ) from .router_utils.pattern_match_deployments import PatternMatchRouter if TYPE_CHECKING: from opentelemetry.trace import Span as _Span Span = _Span else: Span = Any class RoutingArgs(enum.Enum): ttl = 60 # 1min (RPM/TPM expire key) class Router: model_names: List = [] cache_responses: Optional[bool] = False default_cache_time_seconds: int = 1 * 60 * 60 # 1 hour tenacity = None leastbusy_logger: Optional[LeastBusyLoggingHandler] = None lowesttpm_logger: Optional[LowestTPMLoggingHandler] = None def __init__( # noqa: PLR0915 self, model_list: Optional[ Union[List[DeploymentTypedDict], List[Dict[str, Any]]] ] = None, ## ASSISTANTS API ## assistants_config: Optional[AssistantsTypedDict] = None, ## CACHING ## redis_url: Optional[str] = None, redis_host: Optional[str] = None, redis_port: Optional[int] = None, redis_password: Optional[str] = None, cache_responses: Optional[bool] = False, cache_kwargs: dict = {}, # additional kwargs to pass to RedisCache (see caching.py) caching_groups: Optional[ List[tuple] ] = None, # if you want to cache across model groups client_ttl: int = 3600, # ttl for cached clients - will re-initialize after this time in seconds ## SCHEDULER ## polling_interval: Optional[float] = None, default_priority: Optional[int] = None, ## RELIABILITY ## num_retries: Optional[int] = None, max_fallbacks: Optional[ int ] = None, # max fallbacks to try before exiting the call. Defaults to 5. timeout: Optional[float] = None, stream_timeout: Optional[float] = None, default_litellm_params: Optional[ dict ] = None, # default params for Router.chat.completion.create default_max_parallel_requests: Optional[int] = None, set_verbose: bool = False, debug_level: Literal["DEBUG", "INFO"] = "INFO", default_fallbacks: Optional[ List[str] ] = None, # generic fallbacks, works across all deployments fallbacks: List = [], context_window_fallbacks: List = [], content_policy_fallbacks: List = [], model_group_alias: Optional[ Dict[str, Union[str, RouterModelGroupAliasItem]] ] = {}, enable_pre_call_checks: bool = False, enable_tag_filtering: bool = False, retry_after: int = 0, # min time to wait before retrying a failed request retry_policy: Optional[ Union[RetryPolicy, dict] ] = None, # set custom retries for different exceptions model_group_retry_policy: Dict[ str, RetryPolicy ] = {}, # set custom retry policies based on model group allowed_fails: Optional[ int ] = None, # Number of times a deployment can failbefore being added to cooldown allowed_fails_policy: Optional[ AllowedFailsPolicy ] = None, # set custom allowed fails policy cooldown_time: Optional[ float ] = None, # (seconds) time to cooldown a deployment after failure disable_cooldowns: Optional[bool] = None, routing_strategy: Literal[ "simple-shuffle", "least-busy", "usage-based-routing", "latency-based-routing", "cost-based-routing", "usage-based-routing-v2", ] = "simple-shuffle", optional_pre_call_checks: Optional[OptionalPreCallChecks] = None, routing_strategy_args: dict = {}, # just for latency-based provider_budget_config: Optional[GenericBudgetConfigType] = None, alerting_config: Optional[AlertingConfig] = None, router_general_settings: Optional[ RouterGeneralSettings ] = RouterGeneralSettings(), ) -> None: """ Initialize the Router class with the given parameters for caching, reliability, and routing strategy. Args: model_list (Optional[list]): List of models to be used. Defaults to None. redis_url (Optional[str]): URL of the Redis server. Defaults to None. redis_host (Optional[str]): Hostname of the Redis server. Defaults to None. redis_port (Optional[int]): Port of the Redis server. Defaults to None. redis_password (Optional[str]): Password of the Redis server. Defaults to None. cache_responses (Optional[bool]): Flag to enable caching of responses. Defaults to False. cache_kwargs (dict): Additional kwargs to pass to RedisCache. Defaults to {}. caching_groups (Optional[List[tuple]]): List of model groups for caching across model groups. Defaults to None. client_ttl (int): Time-to-live for cached clients in seconds. Defaults to 3600. polling_interval: (Optional[float]): frequency of polling queue. Only for '.scheduler_acompletion()'. Default is 3ms. default_priority: (Optional[int]): the default priority for a request. Only for '.scheduler_acompletion()'. Default is None. num_retries (Optional[int]): Number of retries for failed requests. Defaults to 2. timeout (Optional[float]): Timeout for requests. Defaults to None. default_litellm_params (dict): Default parameters for Router.chat.completion.create. Defaults to {}. set_verbose (bool): Flag to set verbose mode. Defaults to False. debug_level (Literal["DEBUG", "INFO"]): Debug level for logging. Defaults to "INFO". fallbacks (List): List of fallback options. Defaults to []. context_window_fallbacks (List): List of context window fallback options. Defaults to []. enable_pre_call_checks (boolean): Filter out deployments which are outside context window limits for a given prompt model_group_alias (Optional[dict]): Alias for model groups. Defaults to {}. retry_after (int): Minimum time to wait before retrying a failed request. Defaults to 0. allowed_fails (Optional[int]): Number of allowed fails before adding to cooldown. Defaults to None. cooldown_time (float): Time to cooldown a deployment after failure in seconds. Defaults to 1. routing_strategy (Literal["simple-shuffle", "least-busy", "usage-based-routing", "latency-based-routing", "cost-based-routing"]): Routing strategy. Defaults to "simple-shuffle". routing_strategy_args (dict): Additional args for latency-based routing. Defaults to {}. alerting_config (AlertingConfig): Slack alerting configuration. Defaults to None. provider_budget_config (ProviderBudgetConfig): Provider budget configuration. Use this to set llm_provider budget limits. example $100/day to OpenAI, $100/day to Azure, etc. Defaults to None. Returns: Router: An instance of the litellm.Router class. Example Usage: ```python from litellm import Router model_list = [ { "model_name": "azure-gpt-3.5-turbo", # model alias "litellm_params": { # params for litellm completion/embedding call "model": "azure/", "api_key": , "api_version": , "api_base": }, }, { "model_name": "azure-gpt-3.5-turbo", # model alias "litellm_params": { # params for litellm completion/embedding call "model": "azure/", "api_key": , "api_version": , "api_base": }, }, { "model_name": "openai-gpt-3.5-turbo", # model alias "litellm_params": { # params for litellm completion/embedding call "model": "gpt-3.5-turbo", "api_key": , }, ] router = Router(model_list=model_list, fallbacks=[{"azure-gpt-3.5-turbo": "openai-gpt-3.5-turbo"}]) ``` """ from litellm._service_logger import ServiceLogging self.set_verbose = set_verbose self.debug_level = debug_level self.enable_pre_call_checks = enable_pre_call_checks self.enable_tag_filtering = enable_tag_filtering litellm.suppress_debug_info = True # prevents 'Give Feedback/Get help' message from being emitted on Router - Relevant Issue: https://github.com/BerriAI/litellm/issues/5942 if self.set_verbose is True: if debug_level == "INFO": verbose_router_logger.setLevel(logging.INFO) elif debug_level == "DEBUG": verbose_router_logger.setLevel(logging.DEBUG) self.router_general_settings: RouterGeneralSettings = ( router_general_settings or RouterGeneralSettings() ) self.assistants_config = assistants_config self.deployment_names: List = ( [] ) # names of models under litellm_params. ex. azure/chatgpt-v-2 self.deployment_latency_map = {} ### CACHING ### cache_type: Literal["local", "redis", "redis-semantic", "s3", "disk"] = ( "local" # default to an in-memory cache ) redis_cache = None cache_config: Dict[str, Any] = {} self.client_ttl = client_ttl if redis_url is not None or (redis_host is not None and redis_port is not None): cache_type = "redis" if redis_url is not None: cache_config["url"] = redis_url if redis_host is not None: cache_config["host"] = redis_host if redis_port is not None: cache_config["port"] = str(redis_port) # type: ignore if redis_password is not None: cache_config["password"] = redis_password # Add additional key-value pairs from cache_kwargs cache_config.update(cache_kwargs) redis_cache = RedisCache(**cache_config) if cache_responses: if litellm.cache is None: # the cache can be initialized on the proxy server. We should not overwrite it litellm.cache = litellm.Cache(type=cache_type, **cache_config) # type: ignore self.cache_responses = cache_responses self.cache = DualCache( redis_cache=redis_cache, in_memory_cache=InMemoryCache() ) # use a dual cache (Redis+In-Memory) for tracking cooldowns, usage, etc. ### SCHEDULER ### self.scheduler = Scheduler( polling_interval=polling_interval, redis_cache=redis_cache ) self.default_priority = default_priority self.default_deployment = None # use this to track the users default deployment, when they want to use model = * self.default_max_parallel_requests = default_max_parallel_requests self.provider_default_deployment_ids: List[str] = [] self.pattern_router = PatternMatchRouter() if model_list is not None: model_list = copy.deepcopy(model_list) self.set_model_list(model_list) self.healthy_deployments: List = self.model_list # type: ignore for m in model_list: if "model" in m["litellm_params"]: self.deployment_latency_map[m["litellm_params"]["model"]] = 0 else: self.model_list: List = ( [] ) # initialize an empty list - to allow _add_deployment and delete_deployment to work if allowed_fails is not None: self.allowed_fails = allowed_fails else: self.allowed_fails = litellm.allowed_fails self.cooldown_time = cooldown_time or DEFAULT_COOLDOWN_TIME_SECONDS self.cooldown_cache = CooldownCache( cache=self.cache, default_cooldown_time=self.cooldown_time ) self.disable_cooldowns = disable_cooldowns self.failed_calls = ( InMemoryCache() ) # cache to track failed call per deployment, if num failed calls within 1 minute > allowed fails, then add it to cooldown if num_retries is not None: self.num_retries = num_retries elif litellm.num_retries is not None: self.num_retries = litellm.num_retries else: self.num_retries = openai.DEFAULT_MAX_RETRIES if max_fallbacks is not None: self.max_fallbacks = max_fallbacks elif litellm.max_fallbacks is not None: self.max_fallbacks = litellm.max_fallbacks else: self.max_fallbacks = litellm.ROUTER_MAX_FALLBACKS self.timeout = timeout or litellm.request_timeout self.stream_timeout = stream_timeout self.retry_after = retry_after self.routing_strategy = routing_strategy ## SETTING FALLBACKS ## ### validate if it's set + in correct format _fallbacks = fallbacks or litellm.fallbacks self.validate_fallbacks(fallback_param=_fallbacks) ### set fallbacks self.fallbacks = _fallbacks if default_fallbacks is not None or litellm.default_fallbacks is not None: _fallbacks = default_fallbacks or litellm.default_fallbacks if self.fallbacks is not None: self.fallbacks.append({"*": _fallbacks}) else: self.fallbacks = [{"*": _fallbacks}] self.context_window_fallbacks = ( context_window_fallbacks or litellm.context_window_fallbacks ) _content_policy_fallbacks = ( content_policy_fallbacks or litellm.content_policy_fallbacks ) self.validate_fallbacks(fallback_param=_content_policy_fallbacks) self.content_policy_fallbacks = _content_policy_fallbacks self.total_calls: defaultdict = defaultdict( int ) # dict to store total calls made to each model self.fail_calls: defaultdict = defaultdict( int ) # dict to store fail_calls made to each model self.success_calls: defaultdict = defaultdict( int ) # dict to store success_calls made to each model self.previous_models: List = ( [] ) # list to store failed calls (passed in as metadata to next call) self.model_group_alias: Dict[str, Union[str, RouterModelGroupAliasItem]] = ( model_group_alias or {} ) # dict to store aliases for router, ex. {"gpt-4": "gpt-3.5-turbo"}, all requests with gpt-4 -> get routed to gpt-3.5-turbo group # make Router.chat.completions.create compatible for openai.chat.completions.create default_litellm_params = default_litellm_params or {} self.chat = litellm.Chat(params=default_litellm_params, router_obj=self) # default litellm args self.default_litellm_params = default_litellm_params self.default_litellm_params.setdefault("timeout", timeout) self.default_litellm_params.setdefault("max_retries", 0) self.default_litellm_params.setdefault("metadata", {}).update( {"caching_groups": caching_groups} ) self.deployment_stats: dict = {} # used for debugging load balancing """ deployment_stats = { "122999-2828282-277: { "model": "gpt-3", "api_base": "http://localhost:4000", "num_requests": 20, "avg_latency": 0.001, "num_failures": 0, "num_successes": 20 } } """ ### ROUTING SETUP ### self.routing_strategy_init( routing_strategy=routing_strategy, routing_strategy_args=routing_strategy_args, ) self.access_groups = None ## USAGE TRACKING ## if isinstance(litellm._async_success_callback, list): litellm.logging_callback_manager.add_litellm_async_success_callback( self.deployment_callback_on_success ) else: litellm.logging_callback_manager.add_litellm_async_success_callback( self.deployment_callback_on_success ) if isinstance(litellm.success_callback, list): litellm.logging_callback_manager.add_litellm_success_callback( self.sync_deployment_callback_on_success ) else: litellm.success_callback = [self.sync_deployment_callback_on_success] if isinstance(litellm._async_failure_callback, list): litellm.logging_callback_manager.add_litellm_async_failure_callback( self.async_deployment_callback_on_failure ) else: litellm._async_failure_callback = [ self.async_deployment_callback_on_failure ] ## COOLDOWNS ## if isinstance(litellm.failure_callback, list): litellm.logging_callback_manager.add_litellm_failure_callback( self.deployment_callback_on_failure ) else: litellm.failure_callback = [self.deployment_callback_on_failure] verbose_router_logger.debug( f"Intialized router with Routing strategy: {self.routing_strategy}\n\n" f"Routing enable_pre_call_checks: {self.enable_pre_call_checks}\n\n" f"Routing fallbacks: {self.fallbacks}\n\n" f"Routing content fallbacks: {self.content_policy_fallbacks}\n\n" f"Routing context window fallbacks: {self.context_window_fallbacks}\n\n" f"Router Redis Caching={self.cache.redis_cache}\n" ) self.service_logger_obj = ServiceLogging() self.routing_strategy_args = routing_strategy_args self.provider_budget_config = provider_budget_config self.router_budget_logger: Optional[RouterBudgetLimiting] = None if RouterBudgetLimiting.should_init_router_budget_limiter( model_list=model_list, provider_budget_config=self.provider_budget_config ): if optional_pre_call_checks is not None: optional_pre_call_checks.append("router_budget_limiting") else: optional_pre_call_checks = ["router_budget_limiting"] self.retry_policy: Optional[RetryPolicy] = None if retry_policy is not None: if isinstance(retry_policy, dict): self.retry_policy = RetryPolicy(**retry_policy) elif isinstance(retry_policy, RetryPolicy): self.retry_policy = retry_policy verbose_router_logger.info( "\033[32mRouter Custom Retry Policy Set:\n{}\033[0m".format( self.retry_policy.model_dump(exclude_none=True) ) ) self.model_group_retry_policy: Optional[Dict[str, RetryPolicy]] = ( model_group_retry_policy ) self.allowed_fails_policy: Optional[AllowedFailsPolicy] = None if allowed_fails_policy is not None: if isinstance(allowed_fails_policy, dict): self.allowed_fails_policy = AllowedFailsPolicy(**allowed_fails_policy) elif isinstance(allowed_fails_policy, AllowedFailsPolicy): self.allowed_fails_policy = allowed_fails_policy verbose_router_logger.info( "\033[32mRouter Custom Allowed Fails Policy Set:\n{}\033[0m".format( self.allowed_fails_policy.model_dump(exclude_none=True) ) ) self.alerting_config: Optional[AlertingConfig] = alerting_config if optional_pre_call_checks is not None: self.add_optional_pre_call_checks(optional_pre_call_checks) if self.alerting_config is not None: self._initialize_alerting() self.initialize_assistants_endpoint() self.amoderation = self.factory_function( litellm.amoderation, call_type="moderation" ) def initialize_assistants_endpoint(self): ## INITIALIZE PASS THROUGH ASSISTANTS ENDPOINT ## self.acreate_assistants = self.factory_function(litellm.acreate_assistants) self.adelete_assistant = self.factory_function(litellm.adelete_assistant) self.aget_assistants = self.factory_function(litellm.aget_assistants) self.acreate_thread = self.factory_function(litellm.acreate_thread) self.aget_thread = self.factory_function(litellm.aget_thread) self.a_add_message = self.factory_function(litellm.a_add_message) self.aget_messages = self.factory_function(litellm.aget_messages) self.arun_thread = self.factory_function(litellm.arun_thread) def validate_fallbacks(self, fallback_param: Optional[List]): """ Validate the fallbacks parameter. """ if fallback_param is None: return for fallback_dict in fallback_param: if not isinstance(fallback_dict, dict): raise ValueError(f"Item '{fallback_dict}' is not a dictionary.") if len(fallback_dict) != 1: raise ValueError( f"Dictionary '{fallback_dict}' must have exactly one key, but has {len(fallback_dict)} keys." ) def add_optional_pre_call_checks( self, optional_pre_call_checks: Optional[OptionalPreCallChecks] ): if optional_pre_call_checks is not None: for pre_call_check in optional_pre_call_checks: _callback: Optional[CustomLogger] = None if pre_call_check == "prompt_caching": _callback = PromptCachingDeploymentCheck(cache=self.cache) elif pre_call_check == "router_budget_limiting": _callback = RouterBudgetLimiting( dual_cache=self.cache, provider_budget_config=self.provider_budget_config, model_list=self.model_list, ) if _callback is not None: litellm.logging_callback_manager.add_litellm_callback(_callback) def routing_strategy_init( self, routing_strategy: Union[RoutingStrategy, str], routing_strategy_args: dict ): verbose_router_logger.info(f"Routing strategy: {routing_strategy}") if ( routing_strategy == RoutingStrategy.LEAST_BUSY.value or routing_strategy == RoutingStrategy.LEAST_BUSY ): self.leastbusy_logger = LeastBusyLoggingHandler( router_cache=self.cache, model_list=self.model_list ) ## add callback if isinstance(litellm.input_callback, list): litellm.input_callback.append(self.leastbusy_logger) # type: ignore else: litellm.input_callback = [self.leastbusy_logger] # type: ignore if isinstance(litellm.callbacks, list): litellm.logging_callback_manager.add_litellm_callback(self.leastbusy_logger) # type: ignore elif ( routing_strategy == RoutingStrategy.USAGE_BASED_ROUTING.value or routing_strategy == RoutingStrategy.USAGE_BASED_ROUTING ): self.lowesttpm_logger = LowestTPMLoggingHandler( router_cache=self.cache, model_list=self.model_list, routing_args=routing_strategy_args, ) if isinstance(litellm.callbacks, list): litellm.logging_callback_manager.add_litellm_callback(self.lowesttpm_logger) # type: ignore elif ( routing_strategy == RoutingStrategy.USAGE_BASED_ROUTING_V2.value or routing_strategy == RoutingStrategy.USAGE_BASED_ROUTING_V2 ): self.lowesttpm_logger_v2 = LowestTPMLoggingHandler_v2( router_cache=self.cache, model_list=self.model_list, routing_args=routing_strategy_args, ) if isinstance(litellm.callbacks, list): litellm.logging_callback_manager.add_litellm_callback(self.lowesttpm_logger_v2) # type: ignore elif ( routing_strategy == RoutingStrategy.LATENCY_BASED.value or routing_strategy == RoutingStrategy.LATENCY_BASED ): self.lowestlatency_logger = LowestLatencyLoggingHandler( router_cache=self.cache, model_list=self.model_list, routing_args=routing_strategy_args, ) if isinstance(litellm.callbacks, list): litellm.logging_callback_manager.add_litellm_callback(self.lowestlatency_logger) # type: ignore elif ( routing_strategy == RoutingStrategy.COST_BASED.value or routing_strategy == RoutingStrategy.COST_BASED ): self.lowestcost_logger = LowestCostLoggingHandler( router_cache=self.cache, model_list=self.model_list, routing_args={}, ) if isinstance(litellm.callbacks, list): litellm.logging_callback_manager.add_litellm_callback(self.lowestcost_logger) # type: ignore else: pass def print_deployment(self, deployment: dict): """ returns a copy of the deployment with the api key masked Only returns 2 characters of the api key and masks the rest with * (10 *). """ try: _deployment_copy = copy.deepcopy(deployment) litellm_params: dict = _deployment_copy["litellm_params"] if "api_key" in litellm_params: litellm_params["api_key"] = litellm_params["api_key"][:2] + "*" * 10 return _deployment_copy except Exception as e: verbose_router_logger.debug( f"Error occurred while printing deployment - {str(e)}" ) raise e ### COMPLETION, EMBEDDING, IMG GENERATION FUNCTIONS def completion( self, model: str, messages: List[Dict[str, str]], **kwargs ) -> Union[ModelResponse, CustomStreamWrapper]: """ Example usage: response = router.completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey, how's it going?"}] """ try: verbose_router_logger.debug(f"router.completion(model={model},..)") kwargs["model"] = model kwargs["messages"] = messages kwargs["original_function"] = self._completion self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs) response = self.function_with_fallbacks(**kwargs) return response except Exception as e: raise e def _completion( self, model: str, messages: List[Dict[str, str]], **kwargs ) -> Union[ModelResponse, CustomStreamWrapper]: model_name = None try: # pick the one that is available (lowest TPM/RPM) deployment = self.get_available_deployment( model=model, messages=messages, specific_deployment=kwargs.pop("specific_deployment", None), ) self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs) data = deployment["litellm_params"].copy() model_name = data["model"] potential_model_client = self._get_client( deployment=deployment, kwargs=kwargs ) # check if provided keys == client keys # dynamic_api_key = kwargs.get("api_key", None) if ( dynamic_api_key is not None and potential_model_client is not None and dynamic_api_key != potential_model_client.api_key ): model_client = None else: model_client = potential_model_client ### DEPLOYMENT-SPECIFIC PRE-CALL CHECKS ### (e.g. update rpm pre-call. Raise error, if deployment over limit) ## only run if model group given, not model id if model not in self.get_model_ids(): self.routing_strategy_pre_call_checks(deployment=deployment) response = litellm.completion( **{ **data, "messages": messages, "caching": self.cache_responses, "client": model_client, **kwargs, } ) verbose_router_logger.info( f"litellm.completion(model={model_name})\033[32m 200 OK\033[0m" ) ## CHECK CONTENT FILTER ERROR ## if isinstance(response, ModelResponse): _should_raise = self._should_raise_content_policy_error( model=model, response=response, kwargs=kwargs ) if _should_raise: raise litellm.ContentPolicyViolationError( message="Response output was blocked.", model=model, llm_provider="", ) return response except Exception as e: verbose_router_logger.info( f"litellm.completion(model={model_name})\033[31m Exception {str(e)}\033[0m" ) raise e # fmt: off @overload async def acompletion( self, model: str, messages: List[AllMessageValues], stream: Literal[True], **kwargs ) -> CustomStreamWrapper: ... @overload async def acompletion( self, model: str, messages: List[AllMessageValues], stream: Literal[False] = False, **kwargs ) -> ModelResponse: ... @overload async def acompletion( self, model: str, messages: List[AllMessageValues], stream: Union[Literal[True], Literal[False]] = False, **kwargs ) -> Union[CustomStreamWrapper, ModelResponse]: ... # fmt: on # The actual implementation of the function async def acompletion( self, model: str, messages: List[AllMessageValues], stream: bool = False, **kwargs, ): try: kwargs["model"] = model kwargs["messages"] = messages kwargs["stream"] = stream kwargs["original_function"] = self._acompletion self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs) request_priority = kwargs.get("priority") or self.default_priority start_time = time.time() _is_prompt_management_model = self._is_prompt_management_model(model) if _is_prompt_management_model: return await self._prompt_management_factory( model=model, messages=messages, kwargs=kwargs, ) if request_priority is not None and isinstance(request_priority, int): response = await self.schedule_acompletion(**kwargs) else: response = await self.async_function_with_fallbacks(**kwargs) end_time = time.time() _duration = end_time - start_time asyncio.create_task( self.service_logger_obj.async_service_success_hook( service=ServiceTypes.ROUTER, duration=_duration, call_type="acompletion", start_time=start_time, end_time=end_time, parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs), ) ) return response except Exception as e: asyncio.create_task( send_llm_exception_alert( litellm_router_instance=self, request_kwargs=kwargs, error_traceback_str=traceback.format_exc(), original_exception=e, ) ) raise e async def _acompletion( self, model: str, messages: List[Dict[str, str]], **kwargs ) -> Union[ModelResponse, CustomStreamWrapper]: """ - Get an available deployment - call it with a semaphore over the call - semaphore specific to it's rpm - in the semaphore, make a check against it's local rpm before running """ model_name = None try: verbose_router_logger.debug( f"Inside _acompletion()- model: {model}; kwargs: {kwargs}" ) parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs) start_time = time.time() deployment = await self.async_get_available_deployment( model=model, messages=messages, specific_deployment=kwargs.pop("specific_deployment", None), request_kwargs=kwargs, ) end_time = time.time() _duration = end_time - start_time asyncio.create_task( self.service_logger_obj.async_service_success_hook( service=ServiceTypes.ROUTER, duration=_duration, call_type="async_get_available_deployment", start_time=start_time, end_time=end_time, parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs), ) ) # debug how often this deployment picked self._track_deployment_metrics( deployment=deployment, parent_otel_span=parent_otel_span ) self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs) data = deployment["litellm_params"].copy() model_name = data["model"] model_client = self._get_async_openai_model_client( deployment=deployment, kwargs=kwargs, ) self.total_calls[model_name] += 1 _response = litellm.acompletion( **{ **data, "messages": messages, "caching": self.cache_responses, "client": model_client, **kwargs, } ) logging_obj: Optional[LiteLLMLogging] = kwargs.get( "litellm_logging_obj", None ) rpm_semaphore = self._get_client( deployment=deployment, kwargs=kwargs, client_type="max_parallel_requests", ) if rpm_semaphore is not None and isinstance( rpm_semaphore, asyncio.Semaphore ): async with rpm_semaphore: """ - Check rpm limits before making the call - If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe) """ await self.async_routing_strategy_pre_call_checks( deployment=deployment, logging_obj=logging_obj, parent_otel_span=parent_otel_span, ) response = await _response else: await self.async_routing_strategy_pre_call_checks( deployment=deployment, logging_obj=logging_obj, parent_otel_span=parent_otel_span, ) response = await _response ## CHECK CONTENT FILTER ERROR ## if isinstance(response, ModelResponse): _should_raise = self._should_raise_content_policy_error( model=model, response=response, kwargs=kwargs ) if _should_raise: raise litellm.ContentPolicyViolationError( message="Response output was blocked.", model=model, llm_provider="", ) self.success_calls[model_name] += 1 verbose_router_logger.info( f"litellm.acompletion(model={model_name})\033[32m 200 OK\033[0m" ) # debug how often this deployment picked self._track_deployment_metrics( deployment=deployment, response=response, parent_otel_span=parent_otel_span, ) return response except Exception as e: verbose_router_logger.info( f"litellm.acompletion(model={model_name})\033[31m Exception {str(e)}\033[0m" ) if model_name is not None: self.fail_calls[model_name] += 1 raise e def _update_kwargs_before_fallbacks(self, model: str, kwargs: dict) -> None: """ Adds/updates to kwargs: - num_retries - litellm_trace_id - metadata """ kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) kwargs.setdefault("litellm_trace_id", str(uuid.uuid4())) kwargs.setdefault("metadata", {}).update({"model_group": model}) def _update_kwargs_with_default_litellm_params(self, kwargs: dict) -> None: """ Adds default litellm params to kwargs, if set. """ for k, v in self.default_litellm_params.items(): if ( k not in kwargs and v is not None ): # prioritize model-specific params > default router params kwargs[k] = v elif k == "metadata": kwargs[k].update(v) def _update_kwargs_with_deployment(self, deployment: dict, kwargs: dict) -> None: """ 2 jobs: - Adds selected deployment, model_info and api_base to kwargs["metadata"] (used for logging) - Adds default litellm params to kwargs, if set. """ kwargs.setdefault("metadata", {}).update( { "deployment": deployment["litellm_params"]["model"], "model_info": deployment.get("model_info", {}), "api_base": deployment.get("litellm_params", {}).get("api_base"), } ) kwargs["model_info"] = deployment.get("model_info", {}) kwargs["timeout"] = self._get_timeout( kwargs=kwargs, data=deployment["litellm_params"] ) self._update_kwargs_with_default_litellm_params(kwargs=kwargs) def _get_async_openai_model_client(self, deployment: dict, kwargs: dict): """ Helper to get AsyncOpenAI or AsyncAzureOpenAI client that was created for the deployment The same OpenAI client is re-used to optimize latency / performance in production If dynamic api key is provided: Do not re-use the client. Pass model_client=None. The OpenAI/ AzureOpenAI client will be recreated in the handler for the llm provider """ potential_model_client = self._get_client( deployment=deployment, kwargs=kwargs, client_type="async" ) # check if provided keys == client keys # dynamic_api_key = kwargs.get("api_key", None) if ( dynamic_api_key is not None and potential_model_client is not None and dynamic_api_key != potential_model_client.api_key ): model_client = None else: model_client = potential_model_client return model_client def _get_stream_timeout( self, kwargs: dict, data: dict ) -> Optional[Union[float, int]]: """Helper to get stream timeout from kwargs or deployment params""" return ( kwargs.get("stream_timeout", None) # the params dynamically set by user or data.get( "stream_timeout", None ) # timeout set on litellm_params for this deployment or self.stream_timeout # timeout set on router or self.default_litellm_params.get("stream_timeout", None) ) def _get_non_stream_timeout( self, kwargs: dict, data: dict ) -> Optional[Union[float, int]]: """Helper to get non-stream timeout from kwargs or deployment params""" timeout = ( kwargs.get("timeout", None) # the params dynamically set by user or kwargs.get("request_timeout", None) # the params dynamically set by user or data.get( "timeout", None ) # timeout set on litellm_params for this deployment or data.get( "request_timeout", None ) # timeout set on litellm_params for this deployment or self.timeout # timeout set on router or self.default_litellm_params.get("timeout", None) ) return timeout def _get_timeout(self, kwargs: dict, data: dict) -> Optional[Union[float, int]]: """Helper to get timeout from kwargs or deployment params""" timeout: Optional[Union[float, int]] = None if kwargs.get("stream", False): timeout = self._get_stream_timeout(kwargs=kwargs, data=data) if timeout is None: timeout = self._get_non_stream_timeout( kwargs=kwargs, data=data ) # default to this if no stream specific timeout set return timeout async def abatch_completion( self, models: List[str], messages: Union[List[Dict[str, str]], List[List[Dict[str, str]]]], **kwargs, ): """ Async Batch Completion. Used for 2 scenarios: 1. Batch Process 1 request to N models on litellm.Router. Pass messages as List[Dict[str, str]] to use this 2. Batch Process N requests to M models on litellm.Router. Pass messages as List[List[Dict[str, str]]] to use this Example Request for 1 request to N models: ``` response = await router.abatch_completion( models=["gpt-3.5-turbo", "groq-llama"], messages=[ {"role": "user", "content": "is litellm becoming a better product ?"} ], max_tokens=15, ) ``` Example Request for N requests to M models: ``` response = await router.abatch_completion( models=["gpt-3.5-turbo", "groq-llama"], messages=[ [{"role": "user", "content": "is litellm becoming a better product ?"}], [{"role": "user", "content": "who is this"}], ], ) ``` """ ############## Helpers for async completion ################## async def _async_completion_no_exceptions( model: str, messages: List[AllMessageValues], **kwargs ): """ Wrapper around self.async_completion that catches exceptions and returns them as a result """ try: return await self.acompletion(model=model, messages=messages, **kwargs) except Exception as e: return e async def _async_completion_no_exceptions_return_idx( model: str, messages: List[AllMessageValues], idx: int, # index of message this response corresponds to **kwargs, ): """ Wrapper around self.async_completion that catches exceptions and returns them as a result """ try: return ( await self.acompletion(model=model, messages=messages, **kwargs), idx, ) except Exception as e: return e, idx ############## Helpers for async completion ################## if isinstance(messages, list) and all(isinstance(m, dict) for m in messages): _tasks = [] for model in models: # add each task but if the task fails _tasks.append(_async_completion_no_exceptions(model=model, messages=messages, **kwargs)) # type: ignore response = await asyncio.gather(*_tasks) return response elif isinstance(messages, list) and all(isinstance(m, list) for m in messages): _tasks = [] for idx, message in enumerate(messages): for model in models: # Request Number X, Model Number Y _tasks.append( _async_completion_no_exceptions_return_idx( model=model, idx=idx, messages=message, **kwargs # type: ignore ) ) responses = await asyncio.gather(*_tasks) final_responses: List[List[Any]] = [[] for _ in range(len(messages))] for response in responses: if isinstance(response, tuple): final_responses[response[1]].append(response[0]) else: final_responses[0].append(response) return final_responses async def abatch_completion_one_model_multiple_requests( self, model: str, messages: List[List[AllMessageValues]], **kwargs ): """ Async Batch Completion - Batch Process multiple Messages to one model_group on litellm.Router Use this for sending multiple requests to 1 model Args: model (List[str]): model group messages (List[List[Dict[str, str]]]): list of messages. Each element in the list is one request **kwargs: additional kwargs Usage: response = await self.abatch_completion_one_model_multiple_requests( model="gpt-3.5-turbo", messages=[ [{"role": "user", "content": "hello"}, {"role": "user", "content": "tell me something funny"}], [{"role": "user", "content": "hello good mornign"}], ] ) """ async def _async_completion_no_exceptions( model: str, messages: List[AllMessageValues], **kwargs ): """ Wrapper around self.async_completion that catches exceptions and returns them as a result """ try: return await self.acompletion(model=model, messages=messages, **kwargs) except Exception as e: return e _tasks = [] for message_request in messages: # add each task but if the task fails _tasks.append( _async_completion_no_exceptions( model=model, messages=message_request, **kwargs ) ) response = await asyncio.gather(*_tasks) return response # fmt: off @overload async def abatch_completion_fastest_response( self, model: str, messages: List[Dict[str, str]], stream: Literal[True], **kwargs ) -> CustomStreamWrapper: ... @overload async def abatch_completion_fastest_response( self, model: str, messages: List[Dict[str, str]], stream: Literal[False] = False, **kwargs ) -> ModelResponse: ... # fmt: on async def abatch_completion_fastest_response( self, model: str, messages: List[Dict[str, str]], stream: bool = False, **kwargs, ): """ model - List of comma-separated model names. E.g. model="gpt-4, gpt-3.5-turbo" Returns fastest response from list of model names. OpenAI-compatible endpoint. """ models = [m.strip() for m in model.split(",")] async def _async_completion_no_exceptions( model: str, messages: List[Dict[str, str]], stream: bool, **kwargs: Any ) -> Union[ModelResponse, CustomStreamWrapper, Exception]: """ Wrapper around self.acompletion that catches exceptions and returns them as a result """ try: return await self.acompletion(model=model, messages=messages, stream=stream, **kwargs) # type: ignore except asyncio.CancelledError: verbose_router_logger.debug( "Received 'task.cancel'. Cancelling call w/ model={}.".format(model) ) raise except Exception as e: return e pending_tasks = [] # type: ignore async def check_response(task: asyncio.Task): nonlocal pending_tasks try: result = await task if isinstance(result, (ModelResponse, CustomStreamWrapper)): verbose_router_logger.debug( "Received successful response. Cancelling other LLM API calls." ) # If a desired response is received, cancel all other pending tasks for t in pending_tasks: t.cancel() return result except Exception: # Ignore exceptions, let the loop handle them pass finally: # Remove the task from pending tasks if it finishes try: pending_tasks.remove(task) except KeyError: pass for model in models: task = asyncio.create_task( _async_completion_no_exceptions( model=model, messages=messages, stream=stream, **kwargs ) ) pending_tasks.append(task) # Await the first task to complete successfully while pending_tasks: done, pending_tasks = await asyncio.wait( # type: ignore pending_tasks, return_when=asyncio.FIRST_COMPLETED ) for completed_task in done: result = await check_response(completed_task) if result is not None: # Return the first successful result result._hidden_params["fastest_response_batch_completion"] = True return result # If we exit the loop without returning, all tasks failed raise Exception("All tasks failed") ### SCHEDULER ### # fmt: off @overload async def schedule_acompletion( self, model: str, messages: List[AllMessageValues], priority: int, stream: Literal[False] = False, **kwargs ) -> ModelResponse: ... @overload async def schedule_acompletion( self, model: str, messages: List[AllMessageValues], priority: int, stream: Literal[True], **kwargs ) -> CustomStreamWrapper: ... # fmt: on async def schedule_acompletion( self, model: str, messages: List[AllMessageValues], priority: int, stream=False, **kwargs, ): parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs) ### FLOW ITEM ### _request_id = str(uuid.uuid4()) item = FlowItem( priority=priority, # 👈 SET PRIORITY FOR REQUEST request_id=_request_id, # 👈 SET REQUEST ID model_name="gpt-3.5-turbo", # 👈 SAME as 'Router' ) ### [fin] ### ## ADDS REQUEST TO QUEUE ## await self.scheduler.add_request(request=item) ## POLL QUEUE end_time = time.time() + self.timeout curr_time = time.time() poll_interval = self.scheduler.polling_interval # poll every 3ms make_request = False while curr_time < end_time: _healthy_deployments, _ = await self._async_get_healthy_deployments( model=model, parent_otel_span=parent_otel_span ) make_request = await self.scheduler.poll( ## POLL QUEUE ## - returns 'True' if there's healthy deployments OR if request is at top of queue id=item.request_id, model_name=item.model_name, health_deployments=_healthy_deployments, ) if make_request: ## IF TRUE -> MAKE REQUEST break else: ## ELSE -> loop till default_timeout await asyncio.sleep(poll_interval) curr_time = time.time() if make_request: try: _response = await self.acompletion( model=model, messages=messages, stream=stream, **kwargs ) _response._hidden_params.setdefault("additional_headers", {}) _response._hidden_params["additional_headers"].update( {"x-litellm-request-prioritization-used": True} ) return _response except Exception as e: setattr(e, "priority", priority) raise e else: raise litellm.Timeout( message="Request timed out while polling queue", model=model, llm_provider="openai", ) async def _schedule_factory( self, model: str, priority: int, original_function: Callable, args: Tuple[Any, ...], kwargs: Dict[str, Any], ): parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs) ### FLOW ITEM ### _request_id = str(uuid.uuid4()) item = FlowItem( priority=priority, # 👈 SET PRIORITY FOR REQUEST request_id=_request_id, # 👈 SET REQUEST ID model_name=model, # 👈 SAME as 'Router' ) ### [fin] ### ## ADDS REQUEST TO QUEUE ## await self.scheduler.add_request(request=item) ## POLL QUEUE end_time = time.time() + self.timeout curr_time = time.time() poll_interval = self.scheduler.polling_interval # poll every 3ms make_request = False while curr_time < end_time: _healthy_deployments, _ = await self._async_get_healthy_deployments( model=model, parent_otel_span=parent_otel_span ) make_request = await self.scheduler.poll( ## POLL QUEUE ## - returns 'True' if there's healthy deployments OR if request is at top of queue id=item.request_id, model_name=item.model_name, health_deployments=_healthy_deployments, ) if make_request: ## IF TRUE -> MAKE REQUEST break else: ## ELSE -> loop till default_timeout await asyncio.sleep(poll_interval) curr_time = time.time() if make_request: try: _response = await original_function(*args, **kwargs) if isinstance(_response._hidden_params, dict): _response._hidden_params.setdefault("additional_headers", {}) _response._hidden_params["additional_headers"].update( {"x-litellm-request-prioritization-used": True} ) return _response except Exception as e: setattr(e, "priority", priority) raise e else: raise litellm.Timeout( message="Request timed out while polling queue", model=model, llm_provider="openai", ) def _is_prompt_management_model(self, model: str) -> bool: model_list = self.get_model_list(model_name=model) if model_list is None: return False if len(model_list) != 1: return False litellm_model = model_list[0]["litellm_params"].get("model", None) if litellm_model is None: return False if "/" in litellm_model: split_litellm_model = litellm_model.split("/")[0] if split_litellm_model in litellm._known_custom_logger_compatible_callbacks: return True return False async def _prompt_management_factory( self, model: str, messages: List[AllMessageValues], kwargs: Dict[str, Any], ): litellm_logging_object = kwargs.get("litellm_logging_obj", None) if litellm_logging_object is None: litellm_logging_object, kwargs = function_setup( **{ "original_function": "acompletion", "rules_obj": Rules(), "start_time": get_utc_datetime(), **kwargs, } ) litellm_logging_object = cast(LiteLLMLogging, litellm_logging_object) prompt_management_deployment = self.get_available_deployment( model=model, messages=[{"role": "user", "content": "prompt"}], specific_deployment=kwargs.pop("specific_deployment", None), ) litellm_model = prompt_management_deployment["litellm_params"].get( "model", None ) prompt_id = kwargs.get("prompt_id") or prompt_management_deployment[ "litellm_params" ].get("prompt_id", None) prompt_variables = kwargs.get( "prompt_variables" ) or prompt_management_deployment["litellm_params"].get( "prompt_variables", None ) if prompt_id is None or not isinstance(prompt_id, str): raise ValueError( f"Prompt ID is not set or not a string. Got={prompt_id}, type={type(prompt_id)}" ) if prompt_variables is not None and not isinstance(prompt_variables, dict): raise ValueError( f"Prompt variables is set but not a dictionary. Got={prompt_variables}, type={type(prompt_variables)}" ) model, messages, optional_params = ( litellm_logging_object.get_chat_completion_prompt( model=litellm_model, messages=messages, non_default_params=get_non_default_completion_params(kwargs=kwargs), prompt_id=prompt_id, prompt_variables=prompt_variables, ) ) kwargs = {**kwargs, **optional_params} kwargs["model"] = model kwargs["messages"] = messages kwargs["litellm_logging_obj"] = litellm_logging_object kwargs["prompt_id"] = prompt_id kwargs["prompt_variables"] = prompt_variables _model_list = self.get_model_list(model_name=model) if _model_list is None or len(_model_list) == 0: # if direct call to model kwargs.pop("original_function") return await litellm.acompletion(**kwargs) return await self.async_function_with_fallbacks(**kwargs) def image_generation(self, prompt: str, model: str, **kwargs): try: kwargs["model"] = model kwargs["prompt"] = prompt kwargs["original_function"] = self._image_generation kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) kwargs.setdefault("metadata", {}).update({"model_group": model}) response = self.function_with_fallbacks(**kwargs) return response except Exception as e: raise e def _image_generation(self, prompt: str, model: str, **kwargs): model_name = "" try: verbose_router_logger.debug( f"Inside _image_generation()- model: {model}; kwargs: {kwargs}" ) deployment = self.get_available_deployment( model=model, messages=[{"role": "user", "content": "prompt"}], specific_deployment=kwargs.pop("specific_deployment", None), ) self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs) data = deployment["litellm_params"].copy() model_client = self._get_async_openai_model_client( deployment=deployment, kwargs=kwargs, ) self.total_calls[model_name] += 1 ### DEPLOYMENT-SPECIFIC PRE-CALL CHECKS ### (e.g. update rpm pre-call. Raise error, if deployment over limit) self.routing_strategy_pre_call_checks(deployment=deployment) response = litellm.image_generation( **{ **data, "prompt": prompt, "caching": self.cache_responses, "client": model_client, **kwargs, } ) self.success_calls[model_name] += 1 verbose_router_logger.info( f"litellm.image_generation(model={model_name})\033[32m 200 OK\033[0m" ) return response except Exception as e: verbose_router_logger.info( f"litellm.image_generation(model={model_name})\033[31m Exception {str(e)}\033[0m" ) if model_name is not None: self.fail_calls[model_name] += 1 raise e async def aimage_generation(self, prompt: str, model: str, **kwargs): try: kwargs["model"] = model kwargs["prompt"] = prompt kwargs["original_function"] = self._aimage_generation kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs) response = await self.async_function_with_fallbacks(**kwargs) return response except Exception as e: asyncio.create_task( send_llm_exception_alert( litellm_router_instance=self, request_kwargs=kwargs, error_traceback_str=traceback.format_exc(), original_exception=e, ) ) raise e async def _aimage_generation(self, prompt: str, model: str, **kwargs): model_name = model try: verbose_router_logger.debug( f"Inside _image_generation()- model: {model}; kwargs: {kwargs}" ) parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs) deployment = await self.async_get_available_deployment( model=model, messages=[{"role": "user", "content": "prompt"}], specific_deployment=kwargs.pop("specific_deployment", None), ) self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs) data = deployment["litellm_params"].copy() model_name = data["model"] model_client = self._get_async_openai_model_client( deployment=deployment, kwargs=kwargs, ) self.total_calls[model_name] += 1 response = litellm.aimage_generation( **{ **data, "prompt": prompt, "caching": self.cache_responses, "client": model_client, **kwargs, } ) ### CONCURRENCY-SAFE RPM CHECKS ### rpm_semaphore = self._get_client( deployment=deployment, kwargs=kwargs, client_type="max_parallel_requests", ) if rpm_semaphore is not None and isinstance( rpm_semaphore, asyncio.Semaphore ): async with rpm_semaphore: """ - Check rpm limits before making the call - If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe) """ await self.async_routing_strategy_pre_call_checks( deployment=deployment, parent_otel_span=parent_otel_span ) response = await response else: await self.async_routing_strategy_pre_call_checks( deployment=deployment, parent_otel_span=parent_otel_span ) response = await response self.success_calls[model_name] += 1 verbose_router_logger.info( f"litellm.aimage_generation(model={model_name})\033[32m 200 OK\033[0m" ) return response except Exception as e: verbose_router_logger.info( f"litellm.aimage_generation(model={model_name})\033[31m Exception {str(e)}\033[0m" ) if model_name is not None: self.fail_calls[model_name] += 1 raise e async def atranscription(self, file: FileTypes, model: str, **kwargs): """ Example Usage: ``` from litellm import Router client = Router(model_list = [ { "model_name": "whisper", "litellm_params": { "model": "whisper-1", }, }, ]) audio_file = open("speech.mp3", "rb") transcript = await client.atranscription( model="whisper", file=audio_file ) ``` """ try: kwargs["model"] = model kwargs["file"] = file kwargs["original_function"] = self._atranscription self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs) response = await self.async_function_with_fallbacks(**kwargs) return response except Exception as e: asyncio.create_task( send_llm_exception_alert( litellm_router_instance=self, request_kwargs=kwargs, error_traceback_str=traceback.format_exc(), original_exception=e, ) ) raise e async def _atranscription(self, file: FileTypes, model: str, **kwargs): model_name = model try: verbose_router_logger.debug( f"Inside _atranscription()- model: {model}; kwargs: {kwargs}" ) parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs) deployment = await self.async_get_available_deployment( model=model, messages=[{"role": "user", "content": "prompt"}], specific_deployment=kwargs.pop("specific_deployment", None), ) self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs) data = deployment["litellm_params"].copy() model_client = self._get_async_openai_model_client( deployment=deployment, kwargs=kwargs, ) self.total_calls[model_name] += 1 response = litellm.atranscription( **{ **data, "file": file, "caching": self.cache_responses, "client": model_client, **kwargs, } ) ### CONCURRENCY-SAFE RPM CHECKS ### rpm_semaphore = self._get_client( deployment=deployment, kwargs=kwargs, client_type="max_parallel_requests", ) if rpm_semaphore is not None and isinstance( rpm_semaphore, asyncio.Semaphore ): async with rpm_semaphore: """ - Check rpm limits before making the call - If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe) """ await self.async_routing_strategy_pre_call_checks( deployment=deployment, parent_otel_span=parent_otel_span ) response = await response else: await self.async_routing_strategy_pre_call_checks( deployment=deployment, parent_otel_span=parent_otel_span ) response = await response self.success_calls[model_name] += 1 verbose_router_logger.info( f"litellm.atranscription(model={model_name})\033[32m 200 OK\033[0m" ) return response except Exception as e: verbose_router_logger.info( f"litellm.atranscription(model={model_name})\033[31m Exception {str(e)}\033[0m" ) if model_name is not None: self.fail_calls[model_name] += 1 raise e async def aspeech(self, model: str, input: str, voice: str, **kwargs): """ Example Usage: ``` from litellm import Router client = Router(model_list = [ { "model_name": "tts", "litellm_params": { "model": "tts-1", }, }, ]) async with client.aspeech( model="tts", voice="alloy", input="the quick brown fox jumped over the lazy dogs", api_base=None, api_key=None, organization=None, project=None, max_retries=1, timeout=600, client=None, optional_params={}, ) as response: response.stream_to_file(speech_file_path) ``` """ try: kwargs["input"] = input kwargs["voice"] = voice deployment = await self.async_get_available_deployment( model=model, messages=[{"role": "user", "content": "prompt"}], specific_deployment=kwargs.pop("specific_deployment", None), ) self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs) data = deployment["litellm_params"].copy() data["model"] for k, v in self.default_litellm_params.items(): if ( k not in kwargs ): # prioritize model-specific params > default router params kwargs[k] = v elif k == "metadata": kwargs[k].update(v) potential_model_client = self._get_client( deployment=deployment, kwargs=kwargs, client_type="async" ) # check if provided keys == client keys # dynamic_api_key = kwargs.get("api_key", None) if ( dynamic_api_key is not None and potential_model_client is not None and dynamic_api_key != potential_model_client.api_key ): model_client = None else: model_client = potential_model_client response = await litellm.aspeech( **{ **data, "client": model_client, **kwargs, } ) return response except Exception as e: asyncio.create_task( send_llm_exception_alert( litellm_router_instance=self, request_kwargs=kwargs, error_traceback_str=traceback.format_exc(), original_exception=e, ) ) raise e async def arerank(self, model: str, **kwargs): try: kwargs["model"] = model kwargs["input"] = input kwargs["original_function"] = self._arerank self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs) response = await self.async_function_with_fallbacks(**kwargs) return response except Exception as e: asyncio.create_task( send_llm_exception_alert( litellm_router_instance=self, request_kwargs=kwargs, error_traceback_str=traceback.format_exc(), original_exception=e, ) ) raise e async def _arerank(self, model: str, **kwargs): model_name = None try: verbose_router_logger.debug( f"Inside _rerank()- model: {model}; kwargs: {kwargs}" ) deployment = await self.async_get_available_deployment( model=model, specific_deployment=kwargs.pop("specific_deployment", None), ) self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs) data = deployment["litellm_params"].copy() model_name = data["model"] model_client = self._get_async_openai_model_client( deployment=deployment, kwargs=kwargs, ) self.total_calls[model_name] += 1 response = await litellm.arerank( **{ **data, "caching": self.cache_responses, "client": model_client, **kwargs, } ) self.success_calls[model_name] += 1 verbose_router_logger.info( f"litellm.arerank(model={model_name})\033[32m 200 OK\033[0m" ) return response except Exception as e: verbose_router_logger.info( f"litellm.arerank(model={model_name})\033[31m Exception {str(e)}\033[0m" ) if model_name is not None: self.fail_calls[model_name] += 1 raise e async def _arealtime(self, model: str, **kwargs): messages = [{"role": "user", "content": "dummy-text"}] try: kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs) # pick the one that is available (lowest TPM/RPM) deployment = await self.async_get_available_deployment( model=model, messages=messages, specific_deployment=kwargs.pop("specific_deployment", None), ) data = deployment["litellm_params"].copy() for k, v in self.default_litellm_params.items(): if ( k not in kwargs ): # prioritize model-specific params > default router params kwargs[k] = v elif k == "metadata": kwargs[k].update(v) return await litellm._arealtime(**{**data, "caching": self.cache_responses, **kwargs}) # type: ignore except Exception as e: if self.num_retries > 0: kwargs["model"] = model kwargs["messages"] = messages kwargs["original_function"] = self._arealtime return await self.async_function_with_retries(**kwargs) else: raise e def text_completion( self, model: str, prompt: str, is_retry: Optional[bool] = False, is_fallback: Optional[bool] = False, is_async: Optional[bool] = False, **kwargs, ): messages = [{"role": "user", "content": prompt}] try: kwargs["model"] = model kwargs["prompt"] = prompt kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) kwargs.setdefault("metadata", {}).update({"model_group": model}) # pick the one that is available (lowest TPM/RPM) deployment = self.get_available_deployment( model=model, messages=messages, specific_deployment=kwargs.pop("specific_deployment", None), ) data = deployment["litellm_params"].copy() for k, v in self.default_litellm_params.items(): if ( k not in kwargs ): # prioritize model-specific params > default router params kwargs[k] = v elif k == "metadata": kwargs[k].update(v) # call via litellm.completion() return litellm.text_completion(**{**data, "prompt": prompt, "caching": self.cache_responses, **kwargs}) # type: ignore except Exception as e: raise e async def atext_completion( self, model: str, prompt: str, is_retry: Optional[bool] = False, is_fallback: Optional[bool] = False, is_async: Optional[bool] = False, **kwargs, ): if kwargs.get("priority", None) is not None: return await self._schedule_factory( model=model, priority=kwargs.pop("priority"), original_function=self.atext_completion, args=(model, prompt), kwargs=kwargs, ) try: kwargs["model"] = model kwargs["prompt"] = prompt kwargs["original_function"] = self._atext_completion self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs) response = await self.async_function_with_fallbacks(**kwargs) return response except Exception as e: asyncio.create_task( send_llm_exception_alert( litellm_router_instance=self, request_kwargs=kwargs, error_traceback_str=traceback.format_exc(), original_exception=e, ) ) raise e async def _atext_completion(self, model: str, prompt: str, **kwargs): try: verbose_router_logger.debug( f"Inside _atext_completion()- model: {model}; kwargs: {kwargs}" ) parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs) deployment = await self.async_get_available_deployment( model=model, messages=[{"role": "user", "content": prompt}], specific_deployment=kwargs.pop("specific_deployment", None), ) self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs) data = deployment["litellm_params"].copy() model_name = data["model"] model_client = self._get_async_openai_model_client( deployment=deployment, kwargs=kwargs, ) self.total_calls[model_name] += 1 response = litellm.atext_completion( **{ **data, "prompt": prompt, "caching": self.cache_responses, "client": model_client, **kwargs, } ) rpm_semaphore = self._get_client( deployment=deployment, kwargs=kwargs, client_type="max_parallel_requests", ) if rpm_semaphore is not None and isinstance( rpm_semaphore, asyncio.Semaphore ): async with rpm_semaphore: """ - Check rpm limits before making the call - If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe) """ await self.async_routing_strategy_pre_call_checks( deployment=deployment, parent_otel_span=parent_otel_span ) response = await response else: await self.async_routing_strategy_pre_call_checks( deployment=deployment, parent_otel_span=parent_otel_span ) response = await response self.success_calls[model_name] += 1 verbose_router_logger.info( f"litellm.atext_completion(model={model_name})\033[32m 200 OK\033[0m" ) return response except Exception as e: verbose_router_logger.info( f"litellm.atext_completion(model={model})\033[31m Exception {str(e)}\033[0m" ) if model is not None: self.fail_calls[model] += 1 raise e async def aadapter_completion( self, adapter_id: str, model: str, is_retry: Optional[bool] = False, is_fallback: Optional[bool] = False, is_async: Optional[bool] = False, **kwargs, ): try: kwargs["model"] = model kwargs["adapter_id"] = adapter_id kwargs["original_function"] = self._aadapter_completion kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) kwargs.setdefault("metadata", {}).update({"model_group": model}) response = await self.async_function_with_fallbacks(**kwargs) return response except Exception as e: asyncio.create_task( send_llm_exception_alert( litellm_router_instance=self, request_kwargs=kwargs, error_traceback_str=traceback.format_exc(), original_exception=e, ) ) raise e async def _aadapter_completion(self, adapter_id: str, model: str, **kwargs): try: verbose_router_logger.debug( f"Inside _aadapter_completion()- model: {model}; kwargs: {kwargs}" ) parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs) deployment = await self.async_get_available_deployment( model=model, messages=[{"role": "user", "content": "default text"}], specific_deployment=kwargs.pop("specific_deployment", None), ) self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs) data = deployment["litellm_params"].copy() model_name = data["model"] model_client = self._get_async_openai_model_client( deployment=deployment, kwargs=kwargs, ) self.total_calls[model_name] += 1 response = litellm.aadapter_completion( **{ **data, "adapter_id": adapter_id, "caching": self.cache_responses, "client": model_client, **kwargs, } ) rpm_semaphore = self._get_client( deployment=deployment, kwargs=kwargs, client_type="max_parallel_requests", ) if rpm_semaphore is not None and isinstance( rpm_semaphore, asyncio.Semaphore ): async with rpm_semaphore: """ - Check rpm limits before making the call - If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe) """ await self.async_routing_strategy_pre_call_checks( deployment=deployment, parent_otel_span=parent_otel_span ) response = await response # type: ignore else: await self.async_routing_strategy_pre_call_checks( deployment=deployment, parent_otel_span=parent_otel_span ) response = await response # type: ignore self.success_calls[model_name] += 1 verbose_router_logger.info( f"litellm.aadapter_completion(model={model_name})\033[32m 200 OK\033[0m" ) return response except Exception as e: verbose_router_logger.info( f"litellm.aadapter_completion(model={model})\033[31m Exception {str(e)}\033[0m" ) if model is not None: self.fail_calls[model] += 1 raise e def embedding( self, model: str, input: Union[str, List], is_async: Optional[bool] = False, **kwargs, ) -> EmbeddingResponse: try: kwargs["model"] = model kwargs["input"] = input kwargs["original_function"] = self._embedding kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) kwargs.setdefault("metadata", {}).update({"model_group": model}) response = self.function_with_fallbacks(**kwargs) return response except Exception as e: raise e def _embedding(self, input: Union[str, List], model: str, **kwargs): model_name = None try: verbose_router_logger.debug( f"Inside embedding()- model: {model}; kwargs: {kwargs}" ) deployment = self.get_available_deployment( model=model, input=input, specific_deployment=kwargs.pop("specific_deployment", None), ) self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs) data = deployment["litellm_params"].copy() model_name = data["model"] potential_model_client = self._get_client( deployment=deployment, kwargs=kwargs, client_type="sync" ) # check if provided keys == client keys # dynamic_api_key = kwargs.get("api_key", None) if ( dynamic_api_key is not None and potential_model_client is not None and dynamic_api_key != potential_model_client.api_key ): model_client = None else: model_client = potential_model_client self.total_calls[model_name] += 1 ### DEPLOYMENT-SPECIFIC PRE-CALL CHECKS ### (e.g. update rpm pre-call. Raise error, if deployment over limit) self.routing_strategy_pre_call_checks(deployment=deployment) response = litellm.embedding( **{ **data, "input": input, "caching": self.cache_responses, "client": model_client, **kwargs, } ) self.success_calls[model_name] += 1 verbose_router_logger.info( f"litellm.embedding(model={model_name})\033[32m 200 OK\033[0m" ) return response except Exception as e: verbose_router_logger.info( f"litellm.embedding(model={model_name})\033[31m Exception {str(e)}\033[0m" ) if model_name is not None: self.fail_calls[model_name] += 1 raise e async def aembedding( self, model: str, input: Union[str, List], is_async: Optional[bool] = True, **kwargs, ) -> EmbeddingResponse: try: kwargs["model"] = model kwargs["input"] = input kwargs["original_function"] = self._aembedding self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs) response = await self.async_function_with_fallbacks(**kwargs) return response except Exception as e: asyncio.create_task( send_llm_exception_alert( litellm_router_instance=self, request_kwargs=kwargs, error_traceback_str=traceback.format_exc(), original_exception=e, ) ) raise e async def _aembedding(self, input: Union[str, List], model: str, **kwargs): model_name = None try: verbose_router_logger.debug( f"Inside _aembedding()- model: {model}; kwargs: {kwargs}" ) parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs) deployment = await self.async_get_available_deployment( model=model, input=input, specific_deployment=kwargs.pop("specific_deployment", None), ) self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs) data = deployment["litellm_params"].copy() model_name = data["model"] model_client = self._get_async_openai_model_client( deployment=deployment, kwargs=kwargs, ) self.total_calls[model_name] += 1 response = litellm.aembedding( **{ **data, "input": input, "caching": self.cache_responses, "client": model_client, **kwargs, } ) ### CONCURRENCY-SAFE RPM CHECKS ### rpm_semaphore = self._get_client( deployment=deployment, kwargs=kwargs, client_type="max_parallel_requests", ) if rpm_semaphore is not None and isinstance( rpm_semaphore, asyncio.Semaphore ): async with rpm_semaphore: """ - Check rpm limits before making the call - If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe) """ await self.async_routing_strategy_pre_call_checks( deployment=deployment, parent_otel_span=parent_otel_span ) response = await response else: await self.async_routing_strategy_pre_call_checks( deployment=deployment, parent_otel_span=parent_otel_span ) response = await response self.success_calls[model_name] += 1 verbose_router_logger.info( f"litellm.aembedding(model={model_name})\033[32m 200 OK\033[0m" ) return response except Exception as e: verbose_router_logger.info( f"litellm.aembedding(model={model_name})\033[31m Exception {str(e)}\033[0m" ) if model_name is not None: self.fail_calls[model_name] += 1 raise e #### FILES API #### async def acreate_file( self, model: str, **kwargs, ) -> FileObject: try: kwargs["model"] = model kwargs["original_function"] = self._acreate_file kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs) response = await self.async_function_with_fallbacks(**kwargs) return response except Exception as e: asyncio.create_task( send_llm_exception_alert( litellm_router_instance=self, request_kwargs=kwargs, error_traceback_str=traceback.format_exc(), original_exception=e, ) ) raise e async def _acreate_file( self, model: str, **kwargs, ) -> FileObject: try: verbose_router_logger.debug( f"Inside _atext_completion()- model: {model}; kwargs: {kwargs}" ) parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs) deployment = await self.async_get_available_deployment( model=model, messages=[{"role": "user", "content": "files-api-fake-text"}], specific_deployment=kwargs.pop("specific_deployment", None), ) self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs) data = deployment["litellm_params"].copy() model_name = data["model"] model_client = self._get_async_openai_model_client( deployment=deployment, kwargs=kwargs, ) self.total_calls[model_name] += 1 ## REPLACE MODEL IN FILE WITH SELECTED DEPLOYMENT ## stripped_model, custom_llm_provider, _, _ = get_llm_provider( model=data["model"] ) kwargs["file"] = replace_model_in_jsonl( file_content=kwargs["file"], new_model_name=stripped_model ) response = litellm.acreate_file( **{ **data, "custom_llm_provider": custom_llm_provider, "caching": self.cache_responses, "client": model_client, **kwargs, } ) rpm_semaphore = self._get_client( deployment=deployment, kwargs=kwargs, client_type="max_parallel_requests", ) if rpm_semaphore is not None and isinstance( rpm_semaphore, asyncio.Semaphore ): async with rpm_semaphore: """ - Check rpm limits before making the call - If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe) """ await self.async_routing_strategy_pre_call_checks( deployment=deployment, parent_otel_span=parent_otel_span ) response = await response # type: ignore else: await self.async_routing_strategy_pre_call_checks( deployment=deployment, parent_otel_span=parent_otel_span ) response = await response # type: ignore self.success_calls[model_name] += 1 verbose_router_logger.info( f"litellm.acreate_file(model={model_name})\033[32m 200 OK\033[0m" ) return response # type: ignore except Exception as e: verbose_router_logger.exception( f"litellm.acreate_file(model={model}, {kwargs})\033[31m Exception {str(e)}\033[0m" ) if model is not None: self.fail_calls[model] += 1 raise e async def acreate_batch( self, model: str, **kwargs, ) -> Batch: try: kwargs["model"] = model kwargs["original_function"] = self._acreate_batch kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs) response = await self.async_function_with_fallbacks(**kwargs) return response except Exception as e: asyncio.create_task( send_llm_exception_alert( litellm_router_instance=self, request_kwargs=kwargs, error_traceback_str=traceback.format_exc(), original_exception=e, ) ) raise e async def _acreate_batch( self, model: str, **kwargs, ) -> Batch: try: verbose_router_logger.debug( f"Inside _acreate_batch()- model: {model}; kwargs: {kwargs}" ) parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs) deployment = await self.async_get_available_deployment( model=model, messages=[{"role": "user", "content": "files-api-fake-text"}], specific_deployment=kwargs.pop("specific_deployment", None), ) metadata_variable_name = _get_router_metadata_variable_name( function_name="_acreate_batch" ) kwargs.setdefault(metadata_variable_name, {}).update( { "deployment": deployment["litellm_params"]["model"], "model_info": deployment.get("model_info", {}), "api_base": deployment.get("litellm_params", {}).get("api_base"), } ) kwargs["model_info"] = deployment.get("model_info", {}) data = deployment["litellm_params"].copy() model_name = data["model"] self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs) model_client = self._get_async_openai_model_client( deployment=deployment, kwargs=kwargs, ) self.total_calls[model_name] += 1 ## SET CUSTOM PROVIDER TO SELECTED DEPLOYMENT ## _, custom_llm_provider, _, _ = get_llm_provider(model=data["model"]) response = litellm.acreate_batch( **{ **data, "custom_llm_provider": custom_llm_provider, "caching": self.cache_responses, "client": model_client, **kwargs, } ) rpm_semaphore = self._get_client( deployment=deployment, kwargs=kwargs, client_type="max_parallel_requests", ) if rpm_semaphore is not None and isinstance( rpm_semaphore, asyncio.Semaphore ): async with rpm_semaphore: """ - Check rpm limits before making the call - If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe) """ await self.async_routing_strategy_pre_call_checks( deployment=deployment, parent_otel_span=parent_otel_span ) response = await response # type: ignore else: await self.async_routing_strategy_pre_call_checks( deployment=deployment, parent_otel_span=parent_otel_span ) response = await response # type: ignore self.success_calls[model_name] += 1 verbose_router_logger.info( f"litellm.acreate_file(model={model_name})\033[32m 200 OK\033[0m" ) return response # type: ignore except Exception as e: verbose_router_logger.exception( f"litellm._acreate_batch(model={model}, {kwargs})\033[31m Exception {str(e)}\033[0m" ) if model is not None: self.fail_calls[model] += 1 raise e async def aretrieve_batch( self, **kwargs, ) -> Batch: """ Iterate through all models in a model group to check for batch Future Improvement - cache the result. """ try: filtered_model_list = self.get_model_list() if filtered_model_list is None: raise Exception("Router not yet initialized.") receieved_exceptions = [] async def try_retrieve_batch(model_name): try: # Update kwargs with the current model name or any other model-specific adjustments ## SET CUSTOM PROVIDER TO SELECTED DEPLOYMENT ## _, custom_llm_provider, _, _ = get_llm_provider( # type: ignore model=model_name["litellm_params"]["model"] ) new_kwargs = copy.deepcopy(kwargs) new_kwargs.pop("custom_llm_provider", None) return await litellm.aretrieve_batch( custom_llm_provider=custom_llm_provider, **new_kwargs # type: ignore ) except Exception as e: receieved_exceptions.append(e) return None # Check all models in parallel results = await asyncio.gather( *[try_retrieve_batch(model) for model in filtered_model_list], return_exceptions=True, ) # Check for successful responses and handle exceptions for result in results: if isinstance(result, Batch): return result # If no valid Batch response was found, raise the first encountered exception if receieved_exceptions: raise receieved_exceptions[0] # Raising the first exception encountered # If no exceptions were encountered, raise a generic exception raise Exception( "Unable to find batch in any model. Received errors - {}".format( receieved_exceptions ) ) except Exception as e: asyncio.create_task( send_llm_exception_alert( litellm_router_instance=self, request_kwargs=kwargs, error_traceback_str=traceback.format_exc(), original_exception=e, ) ) raise e async def alist_batches( self, model: str, **kwargs, ): """ Return all the batches across all deployments of a model group. """ filtered_model_list = self.get_model_list(model_name=model) if filtered_model_list is None: raise Exception("Router not yet initialized.") async def try_retrieve_batch(model: DeploymentTypedDict): try: # Update kwargs with the current model name or any other model-specific adjustments return await litellm.alist_batches( **{**model["litellm_params"], **kwargs} ) except Exception: return None # Check all models in parallel results = await asyncio.gather( *[try_retrieve_batch(model) for model in filtered_model_list] ) final_results = { "object": "list", "data": [], "first_id": None, "last_id": None, "has_more": False, } for result in results: if result is not None: ## check batch id if final_results["first_id"] is None and hasattr(result, "first_id"): final_results["first_id"] = getattr(result, "first_id") final_results["last_id"] = getattr(result, "last_id") final_results["data"].extend(result.data) # type: ignore ## check 'has_more' if getattr(result, "has_more", False) is True: final_results["has_more"] = True return final_results #### PASSTHROUGH API #### async def _pass_through_moderation_endpoint_factory( self, original_function: Callable, **kwargs, ): if kwargs.get("model") and self.get_model_list(model_name=kwargs["model"]): deployment = await self.async_get_available_deployment( model=kwargs["model"] ) kwargs["model"] = deployment["litellm_params"]["model"] return await original_function(**kwargs) def factory_function( self, original_function: Callable, call_type: Literal["assistants", "moderation"] = "assistants", ): async def new_function( custom_llm_provider: Optional[Literal["openai", "azure"]] = None, client: Optional["AsyncOpenAI"] = None, **kwargs, ): if call_type == "assistants": return await self._pass_through_assistants_endpoint_factory( original_function=original_function, custom_llm_provider=custom_llm_provider, client=client, **kwargs, ) elif call_type == "moderation": return await self._pass_through_moderation_endpoint_factory( # type: ignore original_function=original_function, **kwargs, ) return new_function async def _pass_through_assistants_endpoint_factory( self, original_function: Callable, custom_llm_provider: Optional[Literal["openai", "azure"]] = None, client: Optional[AsyncOpenAI] = None, **kwargs, ): """Internal helper function to pass through the assistants endpoint""" if custom_llm_provider is None: if self.assistants_config is not None: custom_llm_provider = self.assistants_config["custom_llm_provider"] kwargs.update(self.assistants_config["litellm_params"]) else: raise Exception( "'custom_llm_provider' must be set. Either via:\n `Router(assistants_config={'custom_llm_provider': ..})` \nor\n `router.arun_thread(custom_llm_provider=..)`" ) return await original_function( # type: ignore custom_llm_provider=custom_llm_provider, client=client, **kwargs ) #### [END] ASSISTANTS API #### async def async_function_with_fallbacks(self, *args, **kwargs): # noqa: PLR0915 """ Try calling the function_with_retries If it fails after num_retries, fall back to another model group """ model_group: Optional[str] = kwargs.get("model") disable_fallbacks: Optional[bool] = kwargs.pop("disable_fallbacks", False) fallbacks: Optional[List] = kwargs.get("fallbacks", self.fallbacks) context_window_fallbacks: Optional[List] = kwargs.get( "context_window_fallbacks", self.context_window_fallbacks ) content_policy_fallbacks: Optional[List] = kwargs.get( "content_policy_fallbacks", self.content_policy_fallbacks ) mock_timeout = kwargs.pop("mock_timeout", None) try: self._handle_mock_testing_fallbacks( kwargs=kwargs, model_group=model_group, fallbacks=fallbacks, context_window_fallbacks=context_window_fallbacks, content_policy_fallbacks=content_policy_fallbacks, ) if mock_timeout is not None: response = await self.async_function_with_retries( *args, **kwargs, mock_timeout=mock_timeout ) else: response = await self.async_function_with_retries(*args, **kwargs) verbose_router_logger.debug(f"Async Response: {response}") return response except Exception as e: verbose_router_logger.debug(f"Traceback{traceback.format_exc()}") original_exception = e fallback_model_group = None original_model_group: Optional[str] = kwargs.get("model") # type: ignore fallback_failure_exception_str = "" if disable_fallbacks is True or original_model_group is None: raise e input_kwargs = { "litellm_router": self, "original_exception": original_exception, **kwargs, } if "max_fallbacks" not in input_kwargs: input_kwargs["max_fallbacks"] = self.max_fallbacks if "fallback_depth" not in input_kwargs: input_kwargs["fallback_depth"] = 0 try: verbose_router_logger.info("Trying to fallback b/w models") # check if client-side fallbacks are used (e.g. fallbacks = ["gpt-3.5-turbo", "claude-3-haiku"] or fallbacks=[{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "Hey, how's it going?"}]}] is_non_standard_fallback_format = _check_non_standard_fallback_format( fallbacks=fallbacks ) if is_non_standard_fallback_format: input_kwargs.update( { "fallback_model_group": fallbacks, "original_model_group": original_model_group, } ) response = await run_async_fallback( *args, **input_kwargs, ) return response if isinstance(e, litellm.ContextWindowExceededError): if context_window_fallbacks is not None: fallback_model_group: Optional[List[str]] = ( self._get_fallback_model_group_from_fallbacks( fallbacks=context_window_fallbacks, model_group=model_group, ) ) if fallback_model_group is None: raise original_exception input_kwargs.update( { "fallback_model_group": fallback_model_group, "original_model_group": original_model_group, } ) response = await run_async_fallback( *args, **input_kwargs, ) return response else: error_message = "model={}. context_window_fallbacks={}. fallbacks={}.\n\nSet 'context_window_fallback' - https://docs.litellm.ai/docs/routing#fallbacks".format( model_group, context_window_fallbacks, fallbacks ) verbose_router_logger.info( msg="Got 'ContextWindowExceededError'. No context_window_fallback set. Defaulting \ to fallbacks, if available.{}".format( error_message ) ) e.message += "\n{}".format(error_message) elif isinstance(e, litellm.ContentPolicyViolationError): if content_policy_fallbacks is not None: fallback_model_group: Optional[List[str]] = ( self._get_fallback_model_group_from_fallbacks( fallbacks=content_policy_fallbacks, model_group=model_group, ) ) if fallback_model_group is None: raise original_exception input_kwargs.update( { "fallback_model_group": fallback_model_group, "original_model_group": original_model_group, } ) response = await run_async_fallback( *args, **input_kwargs, ) return response else: error_message = "model={}. content_policy_fallback={}. fallbacks={}.\n\nSet 'content_policy_fallback' - https://docs.litellm.ai/docs/routing#fallbacks".format( model_group, content_policy_fallbacks, fallbacks ) verbose_router_logger.info( msg="Got 'ContentPolicyViolationError'. No content_policy_fallback set. Defaulting \ to fallbacks, if available.{}".format( error_message ) ) e.message += "\n{}".format(error_message) if fallbacks is not None and model_group is not None: verbose_router_logger.debug(f"inside model fallbacks: {fallbacks}") fallback_model_group, generic_fallback_idx = ( get_fallback_model_group( fallbacks=fallbacks, # if fallbacks = [{"gpt-3.5-turbo": ["claude-3-haiku"]}] model_group=cast(str, model_group), ) ) ## if none, check for generic fallback if ( fallback_model_group is None and generic_fallback_idx is not None ): fallback_model_group = fallbacks[generic_fallback_idx]["*"] if fallback_model_group is None: verbose_router_logger.info( f"No fallback model group found for original model_group={model_group}. Fallbacks={fallbacks}" ) if hasattr(original_exception, "message"): original_exception.message += f"No fallback model group found for original model_group={model_group}. Fallbacks={fallbacks}" # type: ignore raise original_exception input_kwargs.update( { "fallback_model_group": fallback_model_group, "original_model_group": original_model_group, } ) response = await run_async_fallback( *args, **input_kwargs, ) return response except Exception as new_exception: traceback.print_exc() parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs) verbose_router_logger.error( "litellm.router.py::async_function_with_fallbacks() - Error occurred while trying to do fallbacks - {}\n{}\n\nDebug Information:\nCooldown Deployments={}".format( str(new_exception), traceback.format_exc(), await _async_get_cooldown_deployments_with_debug_info( litellm_router_instance=self, parent_otel_span=parent_otel_span, ), ) ) fallback_failure_exception_str = str(new_exception) if hasattr(original_exception, "message"): # add the available fallbacks to the exception original_exception.message += "\nReceived Model Group={}\nAvailable Model Group Fallbacks={}".format( # type: ignore model_group, fallback_model_group, ) if len(fallback_failure_exception_str) > 0: original_exception.message += ( # type: ignore "\nError doing the fallback: {}".format( fallback_failure_exception_str ) ) raise original_exception def _handle_mock_testing_fallbacks( self, kwargs: dict, model_group: Optional[str] = None, fallbacks: Optional[List] = None, context_window_fallbacks: Optional[List] = None, content_policy_fallbacks: Optional[List] = None, ): """ Helper function to raise a litellm Error for mock testing purposes. Raises: litellm.InternalServerError: when `mock_testing_fallbacks=True` passed in request params litellm.ContextWindowExceededError: when `mock_testing_context_fallbacks=True` passed in request params litellm.ContentPolicyViolationError: when `mock_testing_content_policy_fallbacks=True` passed in request params """ mock_testing_fallbacks = kwargs.pop("mock_testing_fallbacks", None) mock_testing_context_fallbacks = kwargs.pop( "mock_testing_context_fallbacks", None ) mock_testing_content_policy_fallbacks = kwargs.pop( "mock_testing_content_policy_fallbacks", None ) if mock_testing_fallbacks is not None and mock_testing_fallbacks is True: raise litellm.InternalServerError( model=model_group, llm_provider="", message=f"This is a mock exception for model={model_group}, to trigger a fallback. Fallbacks={fallbacks}", ) elif ( mock_testing_context_fallbacks is not None and mock_testing_context_fallbacks is True ): raise litellm.ContextWindowExceededError( model=model_group, llm_provider="", message=f"This is a mock exception for model={model_group}, to trigger a fallback. \ Context_Window_Fallbacks={context_window_fallbacks}", ) elif ( mock_testing_content_policy_fallbacks is not None and mock_testing_content_policy_fallbacks is True ): raise litellm.ContentPolicyViolationError( model=model_group, llm_provider="", message=f"This is a mock exception for model={model_group}, to trigger a fallback. \ Context_Policy_Fallbacks={content_policy_fallbacks}", ) async def async_function_with_retries(self, *args, **kwargs): # noqa: PLR0915 verbose_router_logger.debug( f"Inside async function with retries: args - {args}; kwargs - {kwargs}" ) original_function = kwargs.pop("original_function") fallbacks = kwargs.pop("fallbacks", self.fallbacks) parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs) context_window_fallbacks = kwargs.pop( "context_window_fallbacks", self.context_window_fallbacks ) content_policy_fallbacks = kwargs.pop( "content_policy_fallbacks", self.content_policy_fallbacks ) model_group: Optional[str] = kwargs.get("model") num_retries = kwargs.pop("num_retries") ## ADD MODEL GROUP SIZE TO METADATA - used for model_group_rate_limit_error tracking _metadata: dict = kwargs.get("metadata") or {} if "model_group" in _metadata and isinstance(_metadata["model_group"], str): model_list = self.get_model_list(model_name=_metadata["model_group"]) if model_list is not None: _metadata.update({"model_group_size": len(model_list)}) verbose_router_logger.debug( f"async function w/ retries: original_function - {original_function}, num_retries - {num_retries}" ) try: self._handle_mock_testing_rate_limit_error( model_group=model_group, kwargs=kwargs ) # if the function call is successful, no exception will be raised and we'll break out of the loop response = await self.make_call(original_function, *args, **kwargs) response = add_retry_headers_to_response( response=response, attempted_retries=0, max_retries=None ) return response except Exception as e: current_attempt = None original_exception = e deployment_num_retries = getattr(e, "num_retries", None) if deployment_num_retries is not None and isinstance( deployment_num_retries, int ): num_retries = deployment_num_retries """ Retry Logic """ _healthy_deployments, _all_deployments = ( await self._async_get_healthy_deployments( model=kwargs.get("model") or "", parent_otel_span=parent_otel_span, ) ) # raises an exception if this error should not be retries self.should_retry_this_error( error=e, healthy_deployments=_healthy_deployments, all_deployments=_all_deployments, context_window_fallbacks=context_window_fallbacks, regular_fallbacks=fallbacks, content_policy_fallbacks=content_policy_fallbacks, ) if ( self.retry_policy is not None or self.model_group_retry_policy is not None ): # get num_retries from retry policy _retry_policy_retries = self.get_num_retries_from_retry_policy( exception=original_exception, model_group=kwargs.get("model") ) if _retry_policy_retries is not None: num_retries = _retry_policy_retries ## LOGGING if num_retries > 0: kwargs = self.log_retry(kwargs=kwargs, e=original_exception) else: raise verbose_router_logger.info( f"Retrying request with num_retries: {num_retries}" ) # decides how long to sleep before retry retry_after = self._time_to_sleep_before_retry( e=original_exception, remaining_retries=num_retries, num_retries=num_retries, healthy_deployments=_healthy_deployments, all_deployments=_all_deployments, ) await asyncio.sleep(retry_after) for current_attempt in range(num_retries): try: # if the function call is successful, no exception will be raised and we'll break out of the loop response = await self.make_call(original_function, *args, **kwargs) if inspect.iscoroutinefunction( response ): # async errors are often returned as coroutines response = await response response = add_retry_headers_to_response( response=response, attempted_retries=current_attempt + 1, max_retries=num_retries, ) return response except Exception as e: ## LOGGING kwargs = self.log_retry(kwargs=kwargs, e=e) remaining_retries = num_retries - current_attempt _model: Optional[str] = kwargs.get("model") # type: ignore if _model is not None: _healthy_deployments, _ = ( await self._async_get_healthy_deployments( model=_model, parent_otel_span=parent_otel_span, ) ) else: _healthy_deployments = [] _timeout = self._time_to_sleep_before_retry( e=original_exception, remaining_retries=remaining_retries, num_retries=num_retries, healthy_deployments=_healthy_deployments, all_deployments=_all_deployments, ) await asyncio.sleep(_timeout) if type(original_exception) in litellm.LITELLM_EXCEPTION_TYPES: setattr(original_exception, "max_retries", num_retries) setattr(original_exception, "num_retries", current_attempt) raise original_exception async def make_call(self, original_function: Any, *args, **kwargs): """ Handler for making a call to the .completion()/.embeddings()/etc. functions. """ model_group = kwargs.get("model") response = original_function(*args, **kwargs) if inspect.iscoroutinefunction(response) or inspect.isawaitable(response): response = await response ## PROCESS RESPONSE HEADERS response = await self.set_response_headers( response=response, model_group=model_group ) return response def _handle_mock_testing_rate_limit_error( self, kwargs: dict, model_group: Optional[str] = None ): """ Helper function to raise a mock litellm.RateLimitError error for testing purposes. Raises: litellm.RateLimitError error when `mock_testing_rate_limit_error=True` passed in request params """ mock_testing_rate_limit_error: Optional[bool] = kwargs.pop( "mock_testing_rate_limit_error", None ) available_models = self.get_model_list(model_name=model_group) num_retries: Optional[int] = None if available_models is not None and len(available_models) == 1: num_retries = cast( Optional[int], available_models[0]["litellm_params"].get("num_retries") ) if ( mock_testing_rate_limit_error is not None and mock_testing_rate_limit_error is True ): verbose_router_logger.info( f"litellm.router.py::_mock_rate_limit_error() - Raising mock RateLimitError for model={model_group}" ) raise litellm.RateLimitError( model=model_group, llm_provider="", message=f"This is a mock exception for model={model_group}, to trigger a rate limit error.", num_retries=num_retries, ) def should_retry_this_error( self, error: Exception, healthy_deployments: Optional[List] = None, all_deployments: Optional[List] = None, context_window_fallbacks: Optional[List] = None, content_policy_fallbacks: Optional[List] = None, regular_fallbacks: Optional[List] = None, ): """ 1. raise an exception for ContextWindowExceededError if context_window_fallbacks is not None 2. raise an exception for ContentPolicyViolationError if content_policy_fallbacks is not None 2. raise an exception for RateLimitError if - there are no fallbacks - there are no healthy deployments in the same model group """ _num_healthy_deployments = 0 if healthy_deployments is not None and isinstance(healthy_deployments, list): _num_healthy_deployments = len(healthy_deployments) _num_all_deployments = 0 if all_deployments is not None and isinstance(all_deployments, list): _num_all_deployments = len(all_deployments) ### CHECK IF RATE LIMIT / CONTEXT WINDOW ERROR / CONTENT POLICY VIOLATION ERROR w/ fallbacks available / Bad Request Error if ( isinstance(error, litellm.ContextWindowExceededError) and context_window_fallbacks is not None ): raise error if ( isinstance(error, litellm.ContentPolicyViolationError) and content_policy_fallbacks is not None ): raise error if isinstance(error, litellm.NotFoundError): raise error # Error we should only retry if there are other deployments if isinstance(error, openai.RateLimitError): if ( _num_healthy_deployments <= 0 # if no healthy deployments and regular_fallbacks is not None # and fallbacks available and len(regular_fallbacks) > 0 ): raise error # then raise the error if isinstance(error, openai.AuthenticationError): """ - if other deployments available -> retry - else -> raise error """ if ( _num_all_deployments <= 1 ): # if there is only 1 deployment for this model group then don't retry raise error # then raise error # Do not retry if there are no healthy deployments # just raise the error if _num_healthy_deployments <= 0: # if no healthy deployments raise error return True def function_with_fallbacks(self, *args, **kwargs): """ Sync wrapper for async_function_with_fallbacks Wrapped to reduce code duplication and prevent bugs. """ return run_async_function(self.async_function_with_fallbacks, *args, **kwargs) def _get_fallback_model_group_from_fallbacks( self, fallbacks: List[Dict[str, List[str]]], model_group: Optional[str] = None, ) -> Optional[List[str]]: """ Returns the list of fallback models to use for a given model group If no fallback model group is found, returns None Example: fallbacks = [{"gpt-3.5-turbo": ["gpt-4"]}, {"gpt-4o": ["gpt-3.5-turbo"]}] model_group = "gpt-3.5-turbo" returns: ["gpt-4"] """ if model_group is None: return None fallback_model_group: Optional[List[str]] = None for item in fallbacks: # [{"gpt-3.5-turbo": ["gpt-4"]}] if list(item.keys())[0] == model_group: fallback_model_group = item[model_group] break return fallback_model_group def _time_to_sleep_before_retry( self, e: Exception, remaining_retries: int, num_retries: int, healthy_deployments: Optional[List] = None, all_deployments: Optional[List] = None, ) -> Union[int, float]: """ Calculate back-off, then retry It should instantly retry only when: 1. there are healthy deployments in the same model group 2. there are fallbacks for the completion call """ ## base case - single deployment if all_deployments is not None and len(all_deployments) == 1: pass elif ( healthy_deployments is not None and isinstance(healthy_deployments, list) and len(healthy_deployments) > 0 ): return 0 response_headers: Optional[httpx.Headers] = None if hasattr(e, "response") and hasattr(e.response, "headers"): # type: ignore response_headers = e.response.headers # type: ignore if hasattr(e, "litellm_response_headers"): response_headers = e.litellm_response_headers # type: ignore if response_headers is not None: timeout = litellm._calculate_retry_after( remaining_retries=remaining_retries, max_retries=num_retries, response_headers=response_headers, min_timeout=self.retry_after, ) else: timeout = litellm._calculate_retry_after( remaining_retries=remaining_retries, max_retries=num_retries, min_timeout=self.retry_after, ) return timeout ### HELPER FUNCTIONS async def deployment_callback_on_success( self, kwargs, # kwargs to completion completion_response, # response from completion start_time, end_time, # start/end time ): """ Track remaining tpm/rpm quota for model in model_list """ try: standard_logging_object: Optional[StandardLoggingPayload] = kwargs.get( "standard_logging_object", None ) if standard_logging_object is None: raise ValueError("standard_logging_object is None") if kwargs["litellm_params"].get("metadata") is None: pass else: deployment_name = kwargs["litellm_params"]["metadata"].get( "deployment", None ) # stable name - works for wildcard routes as well model_group = standard_logging_object.get("model_group", None) id = standard_logging_object.get("model_id", None) if model_group is None or id is None: return elif isinstance(id, int): id = str(id) parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs) total_tokens: float = standard_logging_object.get("total_tokens", 0) # ------------ # Setup values # ------------ dt = get_utc_datetime() current_minute = dt.strftime( "%H-%M" ) # use the same timezone regardless of system clock tpm_key = RouterCacheEnum.TPM.value.format( id=id, current_minute=current_minute, model=deployment_name ) # ------------ # Update usage # ------------ # update cache ## TPM await self.cache.async_increment_cache( key=tpm_key, value=total_tokens, parent_otel_span=parent_otel_span, ttl=RoutingArgs.ttl.value, ) ## RPM rpm_key = RouterCacheEnum.RPM.value.format( id=id, current_minute=current_minute, model=deployment_name ) await self.cache.async_increment_cache( key=rpm_key, value=1, parent_otel_span=parent_otel_span, ttl=RoutingArgs.ttl.value, ) increment_deployment_successes_for_current_minute( litellm_router_instance=self, deployment_id=id, ) return tpm_key except Exception as e: verbose_router_logger.exception( "litellm.router.Router::deployment_callback_on_success(): Exception occured - {}".format( str(e) ) ) pass def sync_deployment_callback_on_success( self, kwargs, # kwargs to completion completion_response, # response from completion start_time, end_time, # start/end time ) -> Optional[str]: """ Tracks the number of successes for a deployment in the current minute (using in-memory cache) Returns: - key: str - The key used to increment the cache - None: if no key is found """ id = None if kwargs["litellm_params"].get("metadata") is None: pass else: model_group = kwargs["litellm_params"]["metadata"].get("model_group", None) model_info = kwargs["litellm_params"].get("model_info", {}) or {} id = model_info.get("id", None) if model_group is None or id is None: return None elif isinstance(id, int): id = str(id) if id is not None: key = increment_deployment_successes_for_current_minute( litellm_router_instance=self, deployment_id=id, ) return key return None def deployment_callback_on_failure( self, kwargs, # kwargs to completion completion_response, # response from completion start_time, end_time, # start/end time ) -> bool: """ 2 jobs: - Tracks the number of failures for a deployment in the current minute (using in-memory cache) - Puts the deployment in cooldown if it exceeds the allowed fails / minute Returns: - True if the deployment should be put in cooldown - False if the deployment should not be put in cooldown """ try: exception = kwargs.get("exception", None) exception_status = getattr(exception, "status_code", "") _model_info = kwargs.get("litellm_params", {}).get("model_info", {}) exception_headers = litellm.litellm_core_utils.exception_mapping_utils._get_response_headers( original_exception=exception ) _time_to_cooldown = kwargs.get("litellm_params", {}).get( "cooldown_time", self.cooldown_time ) if exception_headers is not None: _time_to_cooldown = ( litellm.utils._get_retry_after_from_exception_header( response_headers=exception_headers ) ) if _time_to_cooldown is None or _time_to_cooldown < 0: # if the response headers did not read it -> set to default cooldown time _time_to_cooldown = self.cooldown_time if isinstance(_model_info, dict): deployment_id = _model_info.get("id", None) increment_deployment_failures_for_current_minute( litellm_router_instance=self, deployment_id=deployment_id, ) result = _set_cooldown_deployments( litellm_router_instance=self, exception_status=exception_status, original_exception=exception, deployment=deployment_id, time_to_cooldown=_time_to_cooldown, ) # setting deployment_id in cooldown deployments return result else: return False except Exception as e: raise e async def async_deployment_callback_on_failure( self, kwargs, completion_response: Optional[Any], start_time, end_time ): """ Update RPM usage for a deployment """ deployment_name = kwargs["litellm_params"]["metadata"].get( "deployment", None ) # handles wildcard routes - by giving the original name sent to `litellm.completion` model_group = kwargs["litellm_params"]["metadata"].get("model_group", None) model_info = kwargs["litellm_params"].get("model_info", {}) or {} id = model_info.get("id", None) if model_group is None or id is None: return elif isinstance(id, int): id = str(id) parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs) dt = get_utc_datetime() current_minute = dt.strftime( "%H-%M" ) # use the same timezone regardless of system clock ## RPM rpm_key = RouterCacheEnum.RPM.value.format( id=id, current_minute=current_minute, model=deployment_name ) await self.cache.async_increment_cache( key=rpm_key, value=1, parent_otel_span=parent_otel_span, ttl=RoutingArgs.ttl.value, ) def log_retry(self, kwargs: dict, e: Exception) -> dict: """ When a retry or fallback happens, log the details of the just failed model call - similar to Sentry breadcrumbing """ try: # Log failed model as the previous model previous_model = { "exception_type": type(e).__name__, "exception_string": str(e), } for ( k, v, ) in ( kwargs.items() ): # log everything in kwargs except the old previous_models value - prevent nesting if k not in ["metadata", "messages", "original_function"]: previous_model[k] = v elif k == "metadata" and isinstance(v, dict): previous_model["metadata"] = {} # type: ignore for metadata_k, metadata_v in kwargs["metadata"].items(): if metadata_k != "previous_models": previous_model[k][metadata_k] = metadata_v # type: ignore # check current size of self.previous_models, if it's larger than 3, remove the first element if len(self.previous_models) > 3: self.previous_models.pop(0) self.previous_models.append(previous_model) kwargs["metadata"]["previous_models"] = self.previous_models return kwargs except Exception as e: raise e def _update_usage( self, deployment_id: str, parent_otel_span: Optional[Span] ) -> int: """ Update deployment rpm for that minute Returns: - int: request count """ rpm_key = deployment_id request_count = self.cache.get_cache( key=rpm_key, parent_otel_span=parent_otel_span, local_only=True ) if request_count is None: request_count = 1 self.cache.set_cache( key=rpm_key, value=request_count, local_only=True, ttl=60 ) # only store for 60s else: request_count += 1 self.cache.set_cache( key=rpm_key, value=request_count, local_only=True ) # don't change existing ttl return request_count def _has_default_fallbacks(self) -> bool: if self.fallbacks is None: return False for fallback in self.fallbacks: if isinstance(fallback, dict): if "*" in fallback: return True return False def _should_raise_content_policy_error( self, model: str, response: ModelResponse, kwargs: dict ) -> bool: """ Determines if a content policy error should be raised. Only raised if a fallback is available. Else, original response is returned. """ if response.choices[0].finish_reason != "content_filter": return False content_policy_fallbacks = kwargs.get( "content_policy_fallbacks", self.content_policy_fallbacks ) ### ONLY RAISE ERROR IF CP FALLBACK AVAILABLE ### if content_policy_fallbacks is not None: fallback_model_group = None for item in content_policy_fallbacks: # [{"gpt-3.5-turbo": ["gpt-4"]}] if list(item.keys())[0] == model: fallback_model_group = item[model] break if fallback_model_group is not None: return True elif self._has_default_fallbacks(): # default fallbacks set return True verbose_router_logger.info( "Content Policy Error occurred. No available fallbacks. Returning original response. model={}, content_policy_fallbacks={}".format( model, content_policy_fallbacks ) ) return False def _get_healthy_deployments(self, model: str, parent_otel_span: Optional[Span]): _all_deployments: list = [] try: _, _all_deployments = self._common_checks_available_deployment( # type: ignore model=model, ) if isinstance(_all_deployments, dict): return [] except Exception: pass unhealthy_deployments = _get_cooldown_deployments( litellm_router_instance=self, parent_otel_span=parent_otel_span ) healthy_deployments: list = [] for deployment in _all_deployments: if deployment["model_info"]["id"] in unhealthy_deployments: continue else: healthy_deployments.append(deployment) return healthy_deployments, _all_deployments async def _async_get_healthy_deployments( self, model: str, parent_otel_span: Optional[Span] ) -> Tuple[List[Dict], List[Dict]]: """ Returns Tuple of: - Tuple[List[Dict], List[Dict]]: 1. healthy_deployments: list of healthy deployments 2. all_deployments: list of all deployments """ _all_deployments: list = [] try: _, _all_deployments = self._common_checks_available_deployment( # type: ignore model=model, ) if isinstance(_all_deployments, dict): return [], _all_deployments except Exception: pass unhealthy_deployments = await _async_get_cooldown_deployments( litellm_router_instance=self, parent_otel_span=parent_otel_span ) healthy_deployments: list = [] for deployment in _all_deployments: if deployment["model_info"]["id"] in unhealthy_deployments: continue else: healthy_deployments.append(deployment) return healthy_deployments, _all_deployments def routing_strategy_pre_call_checks(self, deployment: dict): """ Mimics 'async_routing_strategy_pre_call_checks' Ensures consistent update rpm implementation for 'usage-based-routing-v2' Returns: - None Raises: - Rate Limit Exception - If the deployment is over it's tpm/rpm limits """ for _callback in litellm.callbacks: if isinstance(_callback, CustomLogger): _callback.pre_call_check(deployment) async def async_routing_strategy_pre_call_checks( self, deployment: dict, parent_otel_span: Optional[Span], logging_obj: Optional[LiteLLMLogging] = None, ): """ For usage-based-routing-v2, enables running rpm checks before the call is made, inside the semaphore. -> makes the calls concurrency-safe, when rpm limits are set for a deployment Returns: - None Raises: - Rate Limit Exception - If the deployment is over it's tpm/rpm limits """ for _callback in litellm.callbacks: if isinstance(_callback, CustomLogger): try: await _callback.async_pre_call_check(deployment, parent_otel_span) except litellm.RateLimitError as e: ## LOG FAILURE EVENT if logging_obj is not None: asyncio.create_task( logging_obj.async_failure_handler( exception=e, traceback_exception=traceback.format_exc(), end_time=time.time(), ) ) ## LOGGING threading.Thread( target=logging_obj.failure_handler, args=(e, traceback.format_exc()), ).start() # log response _set_cooldown_deployments( litellm_router_instance=self, exception_status=e.status_code, original_exception=e, deployment=deployment["model_info"]["id"], time_to_cooldown=self.cooldown_time, ) raise e except Exception as e: ## LOG FAILURE EVENT if logging_obj is not None: asyncio.create_task( logging_obj.async_failure_handler( exception=e, traceback_exception=traceback.format_exc(), end_time=time.time(), ) ) ## LOGGING threading.Thread( target=logging_obj.failure_handler, args=(e, traceback.format_exc()), ).start() # log response raise e async def async_callback_filter_deployments( self, model: str, healthy_deployments: List[dict], messages: Optional[List[AllMessageValues]], parent_otel_span: Optional[Span], request_kwargs: Optional[dict] = None, logging_obj: Optional[LiteLLMLogging] = None, ): """ For usage-based-routing-v2, enables running rpm checks before the call is made, inside the semaphore. -> makes the calls concurrency-safe, when rpm limits are set for a deployment Returns: - None Raises: - Rate Limit Exception - If the deployment is over it's tpm/rpm limits """ returned_healthy_deployments = healthy_deployments for _callback in litellm.callbacks: if isinstance(_callback, CustomLogger): try: returned_healthy_deployments = ( await _callback.async_filter_deployments( model=model, healthy_deployments=returned_healthy_deployments, messages=messages, request_kwargs=request_kwargs, parent_otel_span=parent_otel_span, ) ) except Exception as e: ## LOG FAILURE EVENT if logging_obj is not None: asyncio.create_task( logging_obj.async_failure_handler( exception=e, traceback_exception=traceback.format_exc(), end_time=time.time(), ) ) ## LOGGING threading.Thread( target=logging_obj.failure_handler, args=(e, traceback.format_exc()), ).start() # log response raise e return returned_healthy_deployments def _generate_model_id(self, model_group: str, litellm_params: dict): """ Helper function to consistently generate the same id for a deployment - create a string from all the litellm params - hash - use hash as id """ concat_str = model_group for k, v in litellm_params.items(): if isinstance(k, str): concat_str += k elif isinstance(k, dict): concat_str += json.dumps(k) else: concat_str += str(k) if isinstance(v, str): concat_str += v elif isinstance(v, dict): concat_str += json.dumps(v) else: concat_str += str(v) hash_object = hashlib.sha256(concat_str.encode()) return hash_object.hexdigest() def _create_deployment( self, deployment_info: dict, _model_name: str, _litellm_params: dict, _model_info: dict, ) -> Optional[Deployment]: """ Create a deployment object and add it to the model list If the deployment is not active for the current environment, it is ignored Returns: - Deployment: The deployment object - None: If the deployment is not active for the current environment (if 'supported_environments' is set in litellm_params) """ deployment = Deployment( **deployment_info, model_name=_model_name, litellm_params=LiteLLM_Params(**_litellm_params), model_info=_model_info, ) ## REGISTER MODEL INFO IN LITELLM MODEL COST MAP _model_name = deployment.litellm_params.model if deployment.litellm_params.custom_llm_provider is not None: _model_name = ( deployment.litellm_params.custom_llm_provider + "/" + _model_name ) litellm.register_model( model_cost={ _model_name: _model_info, } ) ## Check if LLM Deployment is allowed for this deployment if self.deployment_is_active_for_environment(deployment=deployment) is not True: verbose_router_logger.warning( f"Ignoring deployment {deployment.model_name} as it is not active for environment {deployment.model_info['supported_environments']}" ) return None deployment = self._add_deployment(deployment=deployment) model = deployment.to_json(exclude_none=True) self.model_list.append(model) return deployment def deployment_is_active_for_environment(self, deployment: Deployment) -> bool: """ Function to check if a llm deployment is active for a given environment. Allows using the same config.yaml across multople environments Requires `LITELLM_ENVIRONMENT` to be set in .env. Valid values for environment: - development - staging - production Raises: - ValueError: If LITELLM_ENVIRONMENT is not set in .env or not one of the valid values - ValueError: If supported_environments is not set in model_info or not one of the valid values """ if ( deployment.model_info is None or "supported_environments" not in deployment.model_info or deployment.model_info["supported_environments"] is None ): return True litellm_environment = get_secret_str(secret_name="LITELLM_ENVIRONMENT") if litellm_environment is None: raise ValueError( "Set 'supported_environments' for model but not 'LITELLM_ENVIRONMENT' set in .env" ) if litellm_environment not in VALID_LITELLM_ENVIRONMENTS: raise ValueError( f"LITELLM_ENVIRONMENT must be one of {VALID_LITELLM_ENVIRONMENTS}. but set as: {litellm_environment}" ) for _env in deployment.model_info["supported_environments"]: if _env not in VALID_LITELLM_ENVIRONMENTS: raise ValueError( f"supported_environments must be one of {VALID_LITELLM_ENVIRONMENTS}. but set as: {_env} for deployment: {deployment}" ) if litellm_environment in deployment.model_info["supported_environments"]: return True return False def set_model_list(self, model_list: list): original_model_list = copy.deepcopy(model_list) self.model_list = [] # we add api_base/api_key each model so load balancing between azure/gpt on api_base1 and api_base2 works for model in original_model_list: _model_name = model.pop("model_name") _litellm_params = model.pop("litellm_params") ## check if litellm params in os.environ if isinstance(_litellm_params, dict): for k, v in _litellm_params.items(): if isinstance(v, str) and v.startswith("os.environ/"): _litellm_params[k] = get_secret(v) _model_info: dict = model.pop("model_info", {}) # check if model info has id if "id" not in _model_info: _id = self._generate_model_id(_model_name, _litellm_params) _model_info["id"] = _id if _litellm_params.get("organization", None) is not None and isinstance( _litellm_params["organization"], list ): # Addresses https://github.com/BerriAI/litellm/issues/3949 for org in _litellm_params["organization"]: _litellm_params["organization"] = org self._create_deployment( deployment_info=model, _model_name=_model_name, _litellm_params=_litellm_params, _model_info=_model_info, ) else: self._create_deployment( deployment_info=model, _model_name=_model_name, _litellm_params=_litellm_params, _model_info=_model_info, ) verbose_router_logger.debug( f"\nInitialized Model List {self.get_model_names()}" ) self.model_names = [m["model_name"] for m in model_list] def _add_deployment(self, deployment: Deployment) -> Deployment: import os #### DEPLOYMENT NAMES INIT ######## self.deployment_names.append(deployment.litellm_params.model) ############ Users can either pass tpm/rpm as a litellm_param or a router param ########### # for get_available_deployment, we use the litellm_param["rpm"] # in this snippet we also set rpm to be a litellm_param if ( deployment.litellm_params.rpm is None and getattr(deployment, "rpm", None) is not None ): deployment.litellm_params.rpm = getattr(deployment, "rpm") if ( deployment.litellm_params.tpm is None and getattr(deployment, "tpm", None) is not None ): deployment.litellm_params.tpm = getattr(deployment, "tpm") #### VALIDATE MODEL ######## # check if model provider in supported providers ( _model, custom_llm_provider, dynamic_api_key, api_base, ) = litellm.get_llm_provider( model=deployment.litellm_params.model, custom_llm_provider=deployment.litellm_params.get( "custom_llm_provider", None ), ) # Check if user is trying to use model_name == "*" # this is a catch all model for their specific api key # if deployment.model_name == "*": # if deployment.litellm_params.model == "*": # # user wants to pass through all requests to litellm.acompletion for unknown deployments # self.router_general_settings.pass_through_all_models = True # else: # self.default_deployment = deployment.to_json(exclude_none=True) # Check if user is using provider specific wildcard routing # example model_name = "databricks/*" or model_name = "anthropic/*" if "*" in deployment.model_name: # store this as a regex pattern - all deployments matching this pattern will be sent to this deployment # Store deployment.model_name as a regex pattern self.pattern_router.add_pattern( deployment.model_name, deployment.to_json(exclude_none=True) ) if deployment.model_info.id: self.provider_default_deployment_ids.append(deployment.model_info.id) # Azure GPT-Vision Enhancements, users can pass os.environ/ data_sources = deployment.litellm_params.get("dataSources", []) or [] for data_source in data_sources: params = data_source.get("parameters", {}) for param_key in ["endpoint", "key"]: # if endpoint or key set for Azure GPT Vision Enhancements, check if it's an env var if param_key in params and params[param_key].startswith("os.environ/"): env_name = params[param_key].replace("os.environ/", "") params[param_key] = os.environ.get(env_name, "") # done reading model["litellm_params"] if custom_llm_provider not in litellm.provider_list: raise Exception(f"Unsupported provider - {custom_llm_provider}") # init OpenAI, Azure clients InitalizeOpenAISDKClient.set_client( litellm_router_instance=self, model=deployment.to_json(exclude_none=True) ) self._initialize_deployment_for_pass_through( deployment=deployment, custom_llm_provider=custom_llm_provider, model=deployment.litellm_params.model, ) return deployment def _initialize_deployment_for_pass_through( self, deployment: Deployment, custom_llm_provider: str, model: str ): """ Optional: Initialize deployment for pass-through endpoints if `deployment.litellm_params.use_in_pass_through` is True Each provider uses diff .env vars for pass-through endpoints, this helper uses the deployment credentials to set the .env vars for pass-through endpoints """ if deployment.litellm_params.use_in_pass_through is True: if custom_llm_provider == "vertex_ai": from litellm.proxy.vertex_ai_endpoints.vertex_endpoints import ( vertex_pass_through_router, ) if ( deployment.litellm_params.vertex_project is None or deployment.litellm_params.vertex_location is None or deployment.litellm_params.vertex_credentials is None ): raise ValueError( "vertex_project, vertex_location, and vertex_credentials must be set in litellm_params for pass-through endpoints" ) vertex_pass_through_router.add_vertex_credentials( project_id=deployment.litellm_params.vertex_project, location=deployment.litellm_params.vertex_location, vertex_credentials=deployment.litellm_params.vertex_credentials, ) else: verbose_router_logger.error( f"Unsupported provider - {custom_llm_provider} for pass-through endpoints" ) pass pass def add_deployment(self, deployment: Deployment) -> Optional[Deployment]: """ Parameters: - deployment: Deployment - the deployment to be added to the Router Returns: - The added deployment - OR None (if deployment already exists) """ # check if deployment already exists if deployment.model_info.id in self.get_model_ids(): return None # add to model list _deployment = deployment.to_json(exclude_none=True) self.model_list.append(_deployment) # initialize client self._add_deployment(deployment=deployment) # add to model names self.model_names.append(deployment.model_name) return deployment def upsert_deployment(self, deployment: Deployment) -> Optional[Deployment]: """ Add or update deployment Parameters: - deployment: Deployment - the deployment to be added to the Router Returns: - The added/updated deployment """ # check if deployment already exists _deployment_model_id = deployment.model_info.id or "" _deployment_on_router: Optional[Deployment] = self.get_deployment( model_id=_deployment_model_id ) if _deployment_on_router is not None: # deployment with this model_id exists on the router if deployment.litellm_params == _deployment_on_router.litellm_params: # No need to update return None # if there is a new litellm param -> then update the deployment # remove the previous deployment removal_idx: Optional[int] = None for idx, model in enumerate(self.model_list): if model["model_info"]["id"] == deployment.model_info.id: removal_idx = idx if removal_idx is not None: self.model_list.pop(removal_idx) # if the model_id is not in router self.add_deployment(deployment=deployment) return deployment def delete_deployment(self, id: str) -> Optional[Deployment]: """ Parameters: - id: str - the id of the deployment to be deleted Returns: - The deleted deployment - OR None (if deleted deployment not found) """ deployment_idx = None for idx, m in enumerate(self.model_list): if m["model_info"]["id"] == id: deployment_idx = idx try: if deployment_idx is not None: item = self.model_list.pop(deployment_idx) return item else: return None except Exception: return None def get_deployment(self, model_id: str) -> Optional[Deployment]: """ Returns -> Deployment or None Raise Exception -> if model found in invalid format """ for model in self.model_list: if "model_info" in model and "id" in model["model_info"]: if model_id == model["model_info"]["id"]: if isinstance(model, dict): return Deployment(**model) elif isinstance(model, Deployment): return model else: raise Exception("Model invalid format - {}".format(type(model))) return None def get_deployment_by_model_group_name( self, model_group_name: str ) -> Optional[Deployment]: """ Returns -> Deployment or None Raise Exception -> if model found in invalid format """ for model in self.model_list: if model["model_name"] == model_group_name: if isinstance(model, dict): return Deployment(**model) elif isinstance(model, Deployment): return model else: raise Exception("Model Name invalid - {}".format(type(model))) return None @overload def get_router_model_info( self, deployment: dict, received_model_name: str, id: None = None ) -> ModelMapInfo: pass @overload def get_router_model_info( self, deployment: None, received_model_name: str, id: str ) -> ModelMapInfo: pass def get_router_model_info( self, deployment: Optional[dict], received_model_name: str, id: Optional[str] = None, ) -> ModelMapInfo: """ For a given model id, return the model info (max tokens, input cost, output cost, etc.). Augment litellm info with additional params set in `model_info`. For azure models, ignore the `model:`. Only set max tokens, cost values if base_model is set. Returns - ModelInfo - If found -> typed dict with max tokens, input cost, etc. Raises: - ValueError -> If model is not mapped yet """ if id is not None: _deployment = self.get_deployment(model_id=id) if _deployment is not None: deployment = _deployment.model_dump(exclude_none=True) if deployment is None: raise ValueError("Deployment not found") ## GET BASE MODEL base_model = deployment.get("model_info", {}).get("base_model", None) if base_model is None: base_model = deployment.get("litellm_params", {}).get("base_model", None) model = base_model ## GET PROVIDER _model, custom_llm_provider, _, _ = litellm.get_llm_provider( model=deployment.get("litellm_params", {}).get("model", ""), litellm_params=LiteLLM_Params(**deployment.get("litellm_params", {})), ) ## SET MODEL TO 'model=' - if base_model is None + not azure if custom_llm_provider == "azure" and base_model is None: verbose_router_logger.error( "Could not identify azure model. Set azure 'base_model' for accurate max tokens, cost tracking, etc.- https://docs.litellm.ai/docs/proxy/cost_tracking#spend-tracking-for-azure-openai-models" ) elif custom_llm_provider != "azure": model = _model potential_models = self.pattern_router.route(received_model_name) if "*" in model and potential_models is not None: # if wildcard route for potential_model in potential_models: try: if potential_model.get("model_info", {}).get( "id" ) == deployment.get("model_info", {}).get("id"): model = potential_model.get("litellm_params", {}).get( "model" ) break except Exception: pass ## GET LITELLM MODEL INFO - raises exception, if model is not mapped if not model.startswith("{}/".format(custom_llm_provider)): model_info_name = "{}/{}".format(custom_llm_provider, model) else: model_info_name = model model_info = litellm.get_model_info(model=model_info_name) ## CHECK USER SET MODEL INFO user_model_info = deployment.get("model_info", {}) model_info.update(user_model_info) return model_info def get_model_info(self, id: str) -> Optional[dict]: """ For a given model id, return the model info Returns - dict: the model in list with 'model_name', 'litellm_params', Optional['model_info'] - None: could not find deployment in list """ for model in self.model_list: if "model_info" in model and "id" in model["model_info"]: if id == model["model_info"]["id"]: return model return None def get_model_group(self, id: str) -> Optional[List]: """ Return list of all models in the same model group as that model id """ model_info = self.get_model_info(id=id) if model_info is None: return None model_name = model_info["model_name"] return self.get_model_list(model_name=model_name) def _set_model_group_info( # noqa: PLR0915 self, model_group: str, user_facing_model_group_name: str ) -> Optional[ModelGroupInfo]: """ For a given model group name, return the combined model info Returns: - ModelGroupInfo if able to construct a model group - None if error constructing model group info """ model_group_info: Optional[ModelGroupInfo] = None total_tpm: Optional[int] = None total_rpm: Optional[int] = None configurable_clientside_auth_params: CONFIGURABLE_CLIENTSIDE_AUTH_PARAMS = None model_list = self.get_model_list(model_name=model_group) if model_list is None: return None for model in model_list: is_match = False if ( "model_name" in model and model["model_name"] == model_group ): # exact match is_match = True elif ( "model_name" in model and self.pattern_router.route(model_group) is not None ): # wildcard model is_match = True if not is_match: continue # model in model group found # litellm_params = LiteLLM_Params(**model["litellm_params"]) # type: ignore # get configurable clientside auth params configurable_clientside_auth_params = ( litellm_params.configurable_clientside_auth_params ) # get model tpm _deployment_tpm: Optional[int] = None if _deployment_tpm is None: _deployment_tpm = model.get("tpm", None) # type: ignore if _deployment_tpm is None: _deployment_tpm = model.get("litellm_params", {}).get("tpm", None) # type: ignore if _deployment_tpm is None: _deployment_tpm = model.get("model_info", {}).get("tpm", None) # type: ignore # get model rpm _deployment_rpm: Optional[int] = None if _deployment_rpm is None: _deployment_rpm = model.get("rpm", None) # type: ignore if _deployment_rpm is None: _deployment_rpm = model.get("litellm_params", {}).get("rpm", None) # type: ignore if _deployment_rpm is None: _deployment_rpm = model.get("model_info", {}).get("rpm", None) # type: ignore # get model info try: model_info = litellm.get_model_info(model=litellm_params.model) except Exception: model_info = None # get llm provider litellm_model, llm_provider = "", "" try: litellm_model, llm_provider, _, _ = litellm.get_llm_provider( model=litellm_params.model, custom_llm_provider=litellm_params.custom_llm_provider, ) except litellm.exceptions.BadRequestError as e: verbose_router_logger.error( "litellm.router.py::get_model_group_info() - {}".format(str(e)) ) if model_info is None: supported_openai_params = litellm.get_supported_openai_params( model=litellm_model, custom_llm_provider=llm_provider ) if supported_openai_params is None: supported_openai_params = [] model_info = ModelMapInfo( key=model_group, max_tokens=None, max_input_tokens=None, max_output_tokens=None, input_cost_per_token=0, output_cost_per_token=0, litellm_provider=llm_provider, mode="chat", supported_openai_params=supported_openai_params, supports_system_messages=None, ) if model_group_info is None: model_group_info = ModelGroupInfo( model_group=user_facing_model_group_name, providers=[llm_provider], **model_info # type: ignore ) else: # if max_input_tokens > curr # if max_output_tokens > curr # if input_cost_per_token > curr # if output_cost_per_token > curr # supports_parallel_function_calling == True # supports_vision == True # supports_function_calling == True if llm_provider not in model_group_info.providers: model_group_info.providers.append(llm_provider) if ( model_info.get("max_input_tokens", None) is not None and model_info["max_input_tokens"] is not None and ( model_group_info.max_input_tokens is None or model_info["max_input_tokens"] > model_group_info.max_input_tokens ) ): model_group_info.max_input_tokens = model_info["max_input_tokens"] if ( model_info.get("max_output_tokens", None) is not None and model_info["max_output_tokens"] is not None and ( model_group_info.max_output_tokens is None or model_info["max_output_tokens"] > model_group_info.max_output_tokens ) ): model_group_info.max_output_tokens = model_info["max_output_tokens"] if model_info.get("input_cost_per_token", None) is not None and ( model_group_info.input_cost_per_token is None or model_info["input_cost_per_token"] > model_group_info.input_cost_per_token ): model_group_info.input_cost_per_token = model_info[ "input_cost_per_token" ] if model_info.get("output_cost_per_token", None) is not None and ( model_group_info.output_cost_per_token is None or model_info["output_cost_per_token"] > model_group_info.output_cost_per_token ): model_group_info.output_cost_per_token = model_info[ "output_cost_per_token" ] if ( model_info.get("supports_parallel_function_calling", None) is not None and model_info["supports_parallel_function_calling"] is True # type: ignore ): model_group_info.supports_parallel_function_calling = True if ( model_info.get("supports_vision", None) is not None and model_info["supports_vision"] is True # type: ignore ): model_group_info.supports_vision = True if ( model_info.get("supports_function_calling", None) is not None and model_info["supports_function_calling"] is True # type: ignore ): model_group_info.supports_function_calling = True if ( model_info.get("supported_openai_params", None) is not None and model_info["supported_openai_params"] is not None ): model_group_info.supported_openai_params = model_info[ "supported_openai_params" ] if model_info.get("tpm", None) is not None and _deployment_tpm is None: _deployment_tpm = model_info.get("tpm") if model_info.get("rpm", None) is not None and _deployment_rpm is None: _deployment_rpm = model_info.get("rpm") if _deployment_tpm is not None: if total_tpm is None: total_tpm = 0 total_tpm += _deployment_tpm # type: ignore if _deployment_rpm is not None: if total_rpm is None: total_rpm = 0 total_rpm += _deployment_rpm # type: ignore if model_group_info is not None: ## UPDATE WITH TOTAL TPM/RPM FOR MODEL GROUP if total_tpm is not None: model_group_info.tpm = total_tpm if total_rpm is not None: model_group_info.rpm = total_rpm ## UPDATE WITH CONFIGURABLE CLIENTSIDE AUTH PARAMS FOR MODEL GROUP if configurable_clientside_auth_params is not None: model_group_info.configurable_clientside_auth_params = ( configurable_clientside_auth_params ) return model_group_info def get_model_group_info(self, model_group: str) -> Optional[ModelGroupInfo]: """ For a given model group name, return the combined model info Returns: - ModelGroupInfo if able to construct a model group - None if error constructing model group info or hidden model group """ ## Check if model group alias if model_group in self.model_group_alias: item = self.model_group_alias[model_group] if isinstance(item, str): _router_model_group = item elif isinstance(item, dict): if item["hidden"] is True: return None else: _router_model_group = item["model"] else: return None return self._set_model_group_info( model_group=_router_model_group, user_facing_model_group_name=model_group, ) ## Check if actual model return self._set_model_group_info( model_group=model_group, user_facing_model_group_name=model_group ) async def get_model_group_usage( self, model_group: str ) -> Tuple[Optional[int], Optional[int]]: """ Returns current tpm/rpm usage for model group Parameters: - model_group: str - the received model name from the user (can be a wildcard route). Returns: - usage: Tuple[tpm, rpm] """ dt = get_utc_datetime() current_minute = dt.strftime( "%H-%M" ) # use the same timezone regardless of system clock tpm_keys: List[str] = [] rpm_keys: List[str] = [] model_list = self.get_model_list(model_name=model_group) if model_list is None: # no matching deployments return None, None for model in model_list: id: Optional[str] = model.get("model_info", {}).get("id") # type: ignore litellm_model: Optional[str] = model["litellm_params"].get( "model" ) # USE THE MODEL SENT TO litellm.completion() - consistent with how global_router cache is written. if id is None or litellm_model is None: continue tpm_keys.append( RouterCacheEnum.TPM.value.format( id=id, model=litellm_model, current_minute=current_minute, ) ) rpm_keys.append( RouterCacheEnum.RPM.value.format( id=id, model=litellm_model, current_minute=current_minute, ) ) combined_tpm_rpm_keys = tpm_keys + rpm_keys combined_tpm_rpm_values = await self.cache.async_batch_get_cache( keys=combined_tpm_rpm_keys ) if combined_tpm_rpm_values is None: return None, None tpm_usage_list: Optional[List] = combined_tpm_rpm_values[: len(tpm_keys)] rpm_usage_list: Optional[List] = combined_tpm_rpm_values[len(tpm_keys) :] ## TPM tpm_usage: Optional[int] = None if tpm_usage_list is not None: for t in tpm_usage_list: if isinstance(t, int): if tpm_usage is None: tpm_usage = 0 tpm_usage += t ## RPM rpm_usage: Optional[int] = None if rpm_usage_list is not None: for t in rpm_usage_list: if isinstance(t, int): if rpm_usage is None: rpm_usage = 0 rpm_usage += t return tpm_usage, rpm_usage @lru_cache(maxsize=64) def _cached_get_model_group_info( self, model_group: str ) -> Optional[ModelGroupInfo]: """ Cached version of get_model_group_info, uses @lru_cache wrapper This is a speed optimization, since set_response_headers makes a call to get_model_group_info on every request """ return self.get_model_group_info(model_group) async def get_remaining_model_group_usage(self, model_group: str) -> Dict[str, int]: model_group_info = self._cached_get_model_group_info(model_group) if model_group_info is not None and model_group_info.tpm is not None: tpm_limit = model_group_info.tpm else: tpm_limit = None if model_group_info is not None and model_group_info.rpm is not None: rpm_limit = model_group_info.rpm else: rpm_limit = None if tpm_limit is None and rpm_limit is None: return {} current_tpm, current_rpm = await self.get_model_group_usage(model_group) returned_dict = {} if tpm_limit is not None: returned_dict["x-ratelimit-remaining-tokens"] = tpm_limit - ( current_tpm or 0 ) returned_dict["x-ratelimit-limit-tokens"] = tpm_limit if rpm_limit is not None: returned_dict["x-ratelimit-remaining-requests"] = rpm_limit - ( current_rpm or 0 ) returned_dict["x-ratelimit-limit-requests"] = rpm_limit return returned_dict async def set_response_headers( self, response: Any, model_group: Optional[str] = None ) -> Any: """ Add the most accurate rate limit headers for a given model response. ## TODO: add model group rate limit headers # - if healthy_deployments > 1, return model group rate limit headers # - else return the model's rate limit headers """ if ( isinstance(response, BaseModel) and hasattr(response, "_hidden_params") and isinstance(response._hidden_params, dict) # type: ignore ): response._hidden_params.setdefault("additional_headers", {}) # type: ignore response._hidden_params["additional_headers"][ # type: ignore "x-litellm-model-group" ] = model_group additional_headers = response._hidden_params["additional_headers"] # type: ignore if ( "x-ratelimit-remaining-tokens" not in additional_headers and "x-ratelimit-remaining-requests" not in additional_headers and model_group is not None ): remaining_usage = await self.get_remaining_model_group_usage( model_group ) for header, value in remaining_usage.items(): if value is not None: additional_headers[header] = value return response def get_model_ids(self, model_name: Optional[str] = None) -> List[str]: """ if 'model_name' is none, returns all. Returns list of model id's. """ ids = [] for model in self.model_list: if "model_info" in model and "id" in model["model_info"]: id = model["model_info"]["id"] if model_name is not None and model["model_name"] == model_name: ids.append(id) elif model_name is None: ids.append(id) return ids def _get_all_deployments( self, model_name: str, model_alias: Optional[str] = None ) -> List[DeploymentTypedDict]: """ Return all deployments of a model name Used for accurate 'get_model_list'. """ returned_models: List[DeploymentTypedDict] = [] for model in self.model_list: if model_name is not None and model["model_name"] == model_name: if model_alias is not None: alias_model = copy.deepcopy(model) alias_model["model_name"] = model_alias returned_models.append(alias_model) else: returned_models.append(model) return returned_models def get_model_names(self) -> List[str]: """ Returns all possible model names for router. Includes model_group_alias models too. """ model_list = self.get_model_list() if model_list is None: return [] model_names = [] for m in model_list: model_names.append(m["model_name"]) return model_names def get_model_list_from_model_alias( self, model_name: Optional[str] = None ) -> List[DeploymentTypedDict]: """ Helper function to get model list from model alias. Used by `.get_model_list` to get model list from model alias. """ returned_models: List[DeploymentTypedDict] = [] for model_alias, model_value in self.model_group_alias.items(): if model_name is not None and model_alias != model_name: continue if isinstance(model_value, str): _router_model_name: str = model_value elif isinstance(model_value, dict): _model_value = RouterModelGroupAliasItem(**model_value) # type: ignore if _model_value["hidden"] is True: continue else: _router_model_name = _model_value["model"] else: continue returned_models.extend( self._get_all_deployments( model_name=_router_model_name, model_alias=model_alias ) ) return returned_models def get_model_list( self, model_name: Optional[str] = None ) -> Optional[List[DeploymentTypedDict]]: """ Includes router model_group_alias'es as well """ if hasattr(self, "model_list"): returned_models: List[DeploymentTypedDict] = [] if model_name is not None: returned_models.extend(self._get_all_deployments(model_name=model_name)) if hasattr(self, "model_group_alias"): returned_models.extend( self.get_model_list_from_model_alias(model_name=model_name) ) if len(returned_models) == 0: # check if wildcard route potential_wildcard_models = self.pattern_router.route(model_name) if potential_wildcard_models is not None: returned_models.extend( [DeploymentTypedDict(**m) for m in potential_wildcard_models] # type: ignore ) if model_name is None: returned_models += self.model_list return returned_models return returned_models return None def get_model_access_groups( self, model_name: Optional[str] = None, model_access_group: Optional[str] = None ) -> Dict[str, List[str]]: """ If model_name is provided, only return access groups for that model. Parameters: - model_name: Optional[str] - the received model name from the user (can be a wildcard route). If set, will only return access groups for that model. - model_access_group: Optional[str] - the received model access group from the user. If set, will only return models for that access group. """ from collections import defaultdict access_groups = defaultdict(list) model_list = self.get_model_list(model_name=model_name) if model_list: for m in model_list: for group in m.get("model_info", {}).get("access_groups", []): if model_access_group is not None: if group == model_access_group: model_name = m["model_name"] access_groups[group].append(model_name) else: model_name = m["model_name"] access_groups[group].append(model_name) return access_groups def _is_model_access_group_for_wildcard_route( self, model_access_group: str ) -> bool: """ Return True if model access group is a wildcard route """ # GET ACCESS GROUPS access_groups = self.get_model_access_groups( model_access_group=model_access_group ) if len(access_groups) == 0: return False models = access_groups.get(model_access_group, []) for model in models: # CHECK IF MODEL ACCESS GROUP IS A WILDCARD ROUTE if self.pattern_router.route(request=model) is not None: return True return False def get_settings(self): """ Get router settings method, returns a dictionary of the settings and their values. For example get the set values for routing_strategy_args, routing_strategy, allowed_fails, cooldown_time, num_retries, timeout, max_retries, retry_after """ _all_vars = vars(self) _settings_to_return = {} vars_to_include = [ "routing_strategy_args", "routing_strategy", "allowed_fails", "cooldown_time", "num_retries", "timeout", "max_retries", "retry_after", "fallbacks", "context_window_fallbacks", "model_group_retry_policy", ] for var in vars_to_include: if var in _all_vars: _settings_to_return[var] = _all_vars[var] if ( var == "routing_strategy_args" and self.routing_strategy == "latency-based-routing" ): _settings_to_return[var] = self.lowestlatency_logger.routing_args.json() return _settings_to_return def update_settings(self, **kwargs): """ Update the router settings. """ # only the following settings are allowed to be configured _allowed_settings = [ "routing_strategy_args", "routing_strategy", "allowed_fails", "cooldown_time", "num_retries", "timeout", "max_retries", "retry_after", "fallbacks", "context_window_fallbacks", "model_group_retry_policy", ] _int_settings = [ "timeout", "num_retries", "retry_after", "allowed_fails", "cooldown_time", ] _existing_router_settings = self.get_settings() for var in kwargs: if var in _allowed_settings: if var in _int_settings: _casted_value = int(kwargs[var]) setattr(self, var, _casted_value) else: # only run routing strategy init if it has changed if ( var == "routing_strategy" and _existing_router_settings["routing_strategy"] != kwargs[var] ): self.routing_strategy_init( routing_strategy=kwargs[var], routing_strategy_args=kwargs.get( "routing_strategy_args", {} ), ) setattr(self, var, kwargs[var]) else: verbose_router_logger.debug("Setting {} is not allowed".format(var)) verbose_router_logger.debug(f"Updated Router settings: {self.get_settings()}") def _get_client(self, deployment, kwargs, client_type=None): """ Returns the appropriate client based on the given deployment, kwargs, and client_type. Parameters: deployment (dict): The deployment dictionary containing the clients. kwargs (dict): The keyword arguments passed to the function. client_type (str): The type of client to return. Returns: The appropriate client based on the given client_type and kwargs. """ model_id = deployment["model_info"]["id"] parent_otel_span: Optional[Span] = _get_parent_otel_span_from_kwargs(kwargs) if client_type == "max_parallel_requests": cache_key = "{}_max_parallel_requests_client".format(model_id) client = self.cache.get_cache( key=cache_key, local_only=True, parent_otel_span=parent_otel_span ) return client elif client_type == "async": if kwargs.get("stream") is True: cache_key = f"{model_id}_stream_async_client" client = self.cache.get_cache( key=cache_key, local_only=True, parent_otel_span=parent_otel_span ) if client is None: """ Re-initialize the client """ InitalizeOpenAISDKClient.set_client( litellm_router_instance=self, model=deployment ) client = self.cache.get_cache( key=cache_key, local_only=True, parent_otel_span=parent_otel_span, ) return client else: cache_key = f"{model_id}_async_client" client = self.cache.get_cache( key=cache_key, local_only=True, parent_otel_span=parent_otel_span ) if client is None: """ Re-initialize the client """ InitalizeOpenAISDKClient.set_client( litellm_router_instance=self, model=deployment ) client = self.cache.get_cache( key=cache_key, local_only=True, parent_otel_span=parent_otel_span, ) return client else: if kwargs.get("stream") is True: cache_key = f"{model_id}_stream_client" client = self.cache.get_cache( key=cache_key, parent_otel_span=parent_otel_span ) if client is None: """ Re-initialize the client """ InitalizeOpenAISDKClient.set_client( litellm_router_instance=self, model=deployment ) client = self.cache.get_cache( key=cache_key, parent_otel_span=parent_otel_span ) return client else: cache_key = f"{model_id}_client" client = self.cache.get_cache( key=cache_key, parent_otel_span=parent_otel_span ) if client is None: """ Re-initialize the client """ InitalizeOpenAISDKClient.set_client( litellm_router_instance=self, model=deployment ) client = self.cache.get_cache( key=cache_key, parent_otel_span=parent_otel_span ) return client def _pre_call_checks( # noqa: PLR0915 self, model: str, healthy_deployments: List, messages: List[Dict[str, str]], request_kwargs: Optional[dict] = None, ): """ Filter out model in model group, if: - model context window < message length. For azure openai models, requires 'base_model' is set. - https://docs.litellm.ai/docs/proxy/cost_tracking#spend-tracking-for-azure-openai-models - filter models above rpm limits - if region given, filter out models not in that region / unknown region - [TODO] function call and model doesn't support function calling """ verbose_router_logger.debug( f"Starting Pre-call checks for deployments in model={model}" ) _returned_deployments = copy.deepcopy(healthy_deployments) invalid_model_indices = [] try: input_tokens = litellm.token_counter(messages=messages) except Exception as e: verbose_router_logger.error( "litellm.router.py::_pre_call_checks: failed to count tokens. Returning initial list of deployments. Got - {}".format( str(e) ) ) return _returned_deployments _context_window_error = False _potential_error_str = "" _rate_limit_error = False parent_otel_span = _get_parent_otel_span_from_kwargs(request_kwargs) ## get model group RPM ## dt = get_utc_datetime() current_minute = dt.strftime("%H-%M") rpm_key = f"{model}:rpm:{current_minute}" model_group_cache = ( self.cache.get_cache( key=rpm_key, local_only=True, parent_otel_span=parent_otel_span ) or {} ) # check the in-memory cache used by lowest_latency and usage-based routing. Only check the local cache. for idx, deployment in enumerate(_returned_deployments): # see if we have the info for this model try: base_model = deployment.get("model_info", {}).get("base_model", None) if base_model is None: base_model = deployment.get("litellm_params", {}).get( "base_model", None ) model_info = self.get_router_model_info( deployment=deployment, received_model_name=model ) model = base_model or deployment.get("litellm_params", {}).get( "model", None ) if ( isinstance(model_info, dict) and model_info.get("max_input_tokens", None) is not None ): if ( isinstance(model_info["max_input_tokens"], int) and input_tokens > model_info["max_input_tokens"] ): invalid_model_indices.append(idx) _context_window_error = True _potential_error_str += ( "Model={}, Max Input Tokens={}, Got={}".format( model, model_info["max_input_tokens"], input_tokens ) ) continue except Exception as e: verbose_router_logger.exception("An error occurs - {}".format(str(e))) _litellm_params = deployment.get("litellm_params", {}) model_id = deployment.get("model_info", {}).get("id", "") ## RPM CHECK ## ### get local router cache ### current_request_cache_local = ( self.cache.get_cache( key=model_id, local_only=True, parent_otel_span=parent_otel_span ) or 0 ) ### get usage based cache ### if ( isinstance(model_group_cache, dict) and self.routing_strategy != "usage-based-routing-v2" ): model_group_cache[model_id] = model_group_cache.get(model_id, 0) current_request = max( current_request_cache_local, model_group_cache[model_id] ) if ( isinstance(_litellm_params, dict) and _litellm_params.get("rpm", None) is not None ): if ( isinstance(_litellm_params["rpm"], int) and _litellm_params["rpm"] <= current_request ): invalid_model_indices.append(idx) _rate_limit_error = True continue ## REGION CHECK ## if ( request_kwargs is not None and request_kwargs.get("allowed_model_region") is not None ): allowed_model_region = request_kwargs.get("allowed_model_region") if allowed_model_region is not None: if not is_region_allowed( litellm_params=LiteLLM_Params(**_litellm_params), allowed_model_region=allowed_model_region, ): invalid_model_indices.append(idx) continue ## INVALID PARAMS ## -> catch 'gpt-3.5-turbo-16k' not supporting 'response_format' param if request_kwargs is not None and litellm.drop_params is False: # get supported params model, custom_llm_provider, _, _ = litellm.get_llm_provider( model=model, litellm_params=LiteLLM_Params(**_litellm_params) ) supported_openai_params = litellm.get_supported_openai_params( model=model, custom_llm_provider=custom_llm_provider ) if supported_openai_params is None: continue else: # check the non-default openai params in request kwargs non_default_params = litellm.utils.get_non_default_params( passed_params=request_kwargs ) special_params = ["response_format"] # check if all params are supported for k, v in non_default_params.items(): if k not in supported_openai_params and k in special_params: # if not -> invalid model verbose_router_logger.debug( f"INVALID MODEL INDEX @ REQUEST KWARG FILTERING, k={k}" ) invalid_model_indices.append(idx) if len(invalid_model_indices) == len(_returned_deployments): """ - no healthy deployments available b/c context window checks or rate limit error - First check for rate limit errors (if this is true, it means the model passed the context window check but failed the rate limit check) """ if _rate_limit_error is True: # allow generic fallback logic to take place raise RouterRateLimitErrorBasic( model=model, ) elif _context_window_error is True: raise litellm.ContextWindowExceededError( message="litellm._pre_call_checks: Context Window exceeded for given call. No models have context window large enough for this call.\n{}".format( _potential_error_str ), model=model, llm_provider="", ) if len(invalid_model_indices) > 0: for idx in reversed(invalid_model_indices): _returned_deployments.pop(idx) ## ORDER FILTERING ## -> if user set 'order' in deployments, return deployments with lowest order (e.g. order=1 > order=2) if len(_returned_deployments) > 0: _returned_deployments = litellm.utils._get_order_filtered_deployments( _returned_deployments ) return _returned_deployments def _get_model_from_alias(self, model: str) -> Optional[str]: """ Get the model from the alias. Returns: - str, the litellm model name - None, if model is not in model group alias """ if model not in self.model_group_alias: return None _item = self.model_group_alias[model] if isinstance(_item, str): model = _item else: model = _item["model"] return model def _get_deployment_by_litellm_model(self, model: str) -> List: """ Get the deployment by litellm model. """ return [m for m in self.model_list if m["litellm_params"]["model"] == model] def _common_checks_available_deployment( self, model: str, messages: Optional[List[Dict[str, str]]] = None, input: Optional[Union[str, List]] = None, specific_deployment: Optional[bool] = False, ) -> Tuple[str, Union[List, Dict]]: """ Common checks for 'get_available_deployment' across sync + async call. If 'healthy_deployments' returned is None, this means the user chose a specific deployment Returns - str, the litellm model name - List, if multiple models chosen - Dict, if specific model chosen """ # check if aliases set on litellm model alias map if specific_deployment is True: return model, self._get_deployment_by_litellm_model(model=model) elif model in self.get_model_ids(): deployment = self.get_deployment(model_id=model) if deployment is not None: deployment_model = deployment.litellm_params.model return deployment_model, deployment.model_dump(exclude_none=True) raise ValueError( f"LiteLLM Router: Trying to call specific deployment, but Model ID :{model} does not exist in \ Model ID List: {self.get_model_ids}" ) _model_from_alias = self._get_model_from_alias(model=model) if _model_from_alias is not None: model = _model_from_alias if model not in self.model_names: # check if provider/ specific wildcard routing use pattern matching pattern_deployments = self.pattern_router.get_deployments_by_pattern( model=model, ) if pattern_deployments: return model, pattern_deployments # check if default deployment is set if self.default_deployment is not None: updated_deployment = copy.deepcopy( self.default_deployment ) # self.default_deployment updated_deployment["litellm_params"]["model"] = model return model, updated_deployment ## get healthy deployments ### get all deployments healthy_deployments = self._get_all_deployments(model_name=model) if len(healthy_deployments) == 0: # check if the user sent in a deployment name instead healthy_deployments = self._get_deployment_by_litellm_model(model=model) verbose_router_logger.debug( f"initial list of deployments: {healthy_deployments}" ) if len(healthy_deployments) == 0: raise litellm.BadRequestError( message="You passed in model={}. There is no 'model_name' with this string ".format( model ), model=model, llm_provider="", ) if litellm.model_alias_map and model in litellm.model_alias_map: model = litellm.model_alias_map[ model ] # update the model to the actual value if an alias has been passed in return model, healthy_deployments async def async_get_available_deployment( self, model: str, messages: Optional[List[Dict[str, str]]] = None, input: Optional[Union[str, List]] = None, specific_deployment: Optional[bool] = False, request_kwargs: Optional[Dict] = None, ): """ Async implementation of 'get_available_deployments'. Allows all cache calls to be made async => 10x perf impact (8rps -> 100 rps). """ if ( self.routing_strategy != "usage-based-routing-v2" and self.routing_strategy != "simple-shuffle" and self.routing_strategy != "cost-based-routing" and self.routing_strategy != "latency-based-routing" and self.routing_strategy != "least-busy" ): # prevent regressions for other routing strategies, that don't have async get available deployments implemented. return self.get_available_deployment( model=model, messages=messages, input=input, specific_deployment=specific_deployment, request_kwargs=request_kwargs, ) try: parent_otel_span = _get_parent_otel_span_from_kwargs(request_kwargs) model, healthy_deployments = self._common_checks_available_deployment( model=model, messages=messages, input=input, specific_deployment=specific_deployment, ) # type: ignore if isinstance(healthy_deployments, dict): return healthy_deployments cooldown_deployments = await _async_get_cooldown_deployments( litellm_router_instance=self, parent_otel_span=parent_otel_span ) verbose_router_logger.debug( f"async cooldown deployments: {cooldown_deployments}" ) verbose_router_logger.debug(f"cooldown_deployments: {cooldown_deployments}") healthy_deployments = self._filter_cooldown_deployments( healthy_deployments=healthy_deployments, cooldown_deployments=cooldown_deployments, ) healthy_deployments = await self.async_callback_filter_deployments( model=model, healthy_deployments=healthy_deployments, messages=( cast(List[AllMessageValues], messages) if messages is not None else None ), request_kwargs=request_kwargs, parent_otel_span=parent_otel_span, ) if self.enable_pre_call_checks and messages is not None: healthy_deployments = self._pre_call_checks( model=model, healthy_deployments=cast(List[Dict], healthy_deployments), messages=messages, request_kwargs=request_kwargs, ) # check if user wants to do tag based routing healthy_deployments = await get_deployments_for_tag( # type: ignore llm_router_instance=self, model=model, request_kwargs=request_kwargs, healthy_deployments=healthy_deployments, ) if len(healthy_deployments) == 0: exception = await async_raise_no_deployment_exception( litellm_router_instance=self, model=model, parent_otel_span=parent_otel_span, ) raise exception start_time = time.time() if ( self.routing_strategy == "usage-based-routing-v2" and self.lowesttpm_logger_v2 is not None ): deployment = ( await self.lowesttpm_logger_v2.async_get_available_deployments( model_group=model, healthy_deployments=healthy_deployments, # type: ignore messages=messages, input=input, ) ) elif ( self.routing_strategy == "cost-based-routing" and self.lowestcost_logger is not None ): deployment = ( await self.lowestcost_logger.async_get_available_deployments( model_group=model, healthy_deployments=healthy_deployments, # type: ignore messages=messages, input=input, ) ) elif ( self.routing_strategy == "latency-based-routing" and self.lowestlatency_logger is not None ): deployment = ( await self.lowestlatency_logger.async_get_available_deployments( model_group=model, healthy_deployments=healthy_deployments, # type: ignore messages=messages, input=input, request_kwargs=request_kwargs, ) ) elif self.routing_strategy == "simple-shuffle": return simple_shuffle( llm_router_instance=self, healthy_deployments=healthy_deployments, model=model, ) elif ( self.routing_strategy == "least-busy" and self.leastbusy_logger is not None ): deployment = ( await self.leastbusy_logger.async_get_available_deployments( model_group=model, healthy_deployments=healthy_deployments, # type: ignore ) ) else: deployment = None if deployment is None: exception = await async_raise_no_deployment_exception( litellm_router_instance=self, model=model, parent_otel_span=parent_otel_span, ) raise exception verbose_router_logger.info( f"get_available_deployment for model: {model}, Selected deployment: {self.print_deployment(deployment)} for model: {model}" ) end_time = time.time() _duration = end_time - start_time asyncio.create_task( self.service_logger_obj.async_service_success_hook( service=ServiceTypes.ROUTER, duration=_duration, call_type=".async_get_available_deployments", parent_otel_span=parent_otel_span, start_time=start_time, end_time=end_time, ) ) return deployment except Exception as e: traceback_exception = traceback.format_exc() # if router rejects call -> log to langfuse/otel/etc. if request_kwargs is not None: logging_obj = request_kwargs.get("litellm_logging_obj", None) if logging_obj is not None: ## LOGGING threading.Thread( target=logging_obj.failure_handler, args=(e, traceback_exception), ).start() # log response # Handle any exceptions that might occur during streaming asyncio.create_task( logging_obj.async_failure_handler(e, traceback_exception) # type: ignore ) raise e def get_available_deployment( self, model: str, messages: Optional[List[Dict[str, str]]] = None, input: Optional[Union[str, List]] = None, specific_deployment: Optional[bool] = False, request_kwargs: Optional[Dict] = None, ): """ Returns the deployment based on routing strategy """ # users need to explicitly call a specific deployment, by setting `specific_deployment = True` as completion()/embedding() kwarg # When this was no explicit we had several issues with fallbacks timing out model, healthy_deployments = self._common_checks_available_deployment( model=model, messages=messages, input=input, specific_deployment=specific_deployment, ) if isinstance(healthy_deployments, dict): return healthy_deployments parent_otel_span: Optional[Span] = _get_parent_otel_span_from_kwargs( request_kwargs ) cooldown_deployments = _get_cooldown_deployments( litellm_router_instance=self, parent_otel_span=parent_otel_span ) healthy_deployments = self._filter_cooldown_deployments( healthy_deployments=healthy_deployments, cooldown_deployments=cooldown_deployments, ) # filter pre-call checks if self.enable_pre_call_checks and messages is not None: healthy_deployments = self._pre_call_checks( model=model, healthy_deployments=healthy_deployments, messages=messages, request_kwargs=request_kwargs, ) if len(healthy_deployments) == 0: model_ids = self.get_model_ids(model_name=model) _cooldown_time = self.cooldown_cache.get_min_cooldown( model_ids=model_ids, parent_otel_span=parent_otel_span ) _cooldown_list = _get_cooldown_deployments( litellm_router_instance=self, parent_otel_span=parent_otel_span ) raise RouterRateLimitError( model=model, cooldown_time=_cooldown_time, enable_pre_call_checks=self.enable_pre_call_checks, cooldown_list=_cooldown_list, ) if self.routing_strategy == "least-busy" and self.leastbusy_logger is not None: deployment = self.leastbusy_logger.get_available_deployments( model_group=model, healthy_deployments=healthy_deployments # type: ignore ) elif self.routing_strategy == "simple-shuffle": # if users pass rpm or tpm, we do a random weighted pick - based on rpm/tpm ############## Check 'weight' param set for weighted pick ################# return simple_shuffle( llm_router_instance=self, healthy_deployments=healthy_deployments, model=model, ) elif ( self.routing_strategy == "latency-based-routing" and self.lowestlatency_logger is not None ): deployment = self.lowestlatency_logger.get_available_deployments( model_group=model, healthy_deployments=healthy_deployments, # type: ignore request_kwargs=request_kwargs, ) elif ( self.routing_strategy == "usage-based-routing" and self.lowesttpm_logger is not None ): deployment = self.lowesttpm_logger.get_available_deployments( model_group=model, healthy_deployments=healthy_deployments, # type: ignore messages=messages, input=input, ) elif ( self.routing_strategy == "usage-based-routing-v2" and self.lowesttpm_logger_v2 is not None ): deployment = self.lowesttpm_logger_v2.get_available_deployments( model_group=model, healthy_deployments=healthy_deployments, # type: ignore messages=messages, input=input, ) else: deployment = None if deployment is None: verbose_router_logger.info( f"get_available_deployment for model: {model}, No deployment available" ) model_ids = self.get_model_ids(model_name=model) _cooldown_time = self.cooldown_cache.get_min_cooldown( model_ids=model_ids, parent_otel_span=parent_otel_span ) _cooldown_list = _get_cooldown_deployments( litellm_router_instance=self, parent_otel_span=parent_otel_span ) raise RouterRateLimitError( model=model, cooldown_time=_cooldown_time, enable_pre_call_checks=self.enable_pre_call_checks, cooldown_list=_cooldown_list, ) verbose_router_logger.info( f"get_available_deployment for model: {model}, Selected deployment: {self.print_deployment(deployment)} for model: {model}" ) return deployment def _filter_cooldown_deployments( self, healthy_deployments: List[Dict], cooldown_deployments: List[str] ) -> List[Dict]: """ Filters out the deployments currently cooling down from the list of healthy deployments Args: healthy_deployments: List of healthy deployments cooldown_deployments: List of model_ids cooling down. cooldown_deployments is a list of model_id's cooling down, cooldown_deployments = ["16700539-b3cd-42f4-b426-6a12a1bb706a", "16700539-b3cd-42f4-b426-7899"] Returns: List of healthy deployments """ # filter out the deployments currently cooling down deployments_to_remove = [] verbose_router_logger.debug(f"cooldown deployments: {cooldown_deployments}") # Find deployments in model_list whose model_id is cooling down for deployment in healthy_deployments: deployment_id = deployment["model_info"]["id"] if deployment_id in cooldown_deployments: deployments_to_remove.append(deployment) # remove unhealthy deployments from healthy deployments for deployment in deployments_to_remove: healthy_deployments.remove(deployment) return healthy_deployments def _track_deployment_metrics( self, deployment, parent_otel_span: Optional[Span], response=None ): """ Tracks successful requests rpm usage. """ try: model_id = deployment.get("model_info", {}).get("id", None) if response is None: # update self.deployment_stats if model_id is not None: self._update_usage( model_id, parent_otel_span ) # update in-memory cache for tracking except Exception as e: verbose_router_logger.error(f"Error in _track_deployment_metrics: {str(e)}") def get_num_retries_from_retry_policy( self, exception: Exception, model_group: Optional[str] = None ): return _get_num_retries_from_retry_policy( exception=exception, model_group=model_group, model_group_retry_policy=self.model_group_retry_policy, retry_policy=self.retry_policy, ) def get_allowed_fails_from_policy(self, exception: Exception): """ BadRequestErrorRetries: Optional[int] = None AuthenticationErrorRetries: Optional[int] = None TimeoutErrorRetries: Optional[int] = None RateLimitErrorRetries: Optional[int] = None ContentPolicyViolationErrorRetries: Optional[int] = None """ # if we can find the exception then in the retry policy -> return the number of retries allowed_fails_policy: Optional[AllowedFailsPolicy] = self.allowed_fails_policy if allowed_fails_policy is None: return None if ( isinstance(exception, litellm.BadRequestError) and allowed_fails_policy.BadRequestErrorAllowedFails is not None ): return allowed_fails_policy.BadRequestErrorAllowedFails if ( isinstance(exception, litellm.AuthenticationError) and allowed_fails_policy.AuthenticationErrorAllowedFails is not None ): return allowed_fails_policy.AuthenticationErrorAllowedFails if ( isinstance(exception, litellm.Timeout) and allowed_fails_policy.TimeoutErrorAllowedFails is not None ): return allowed_fails_policy.TimeoutErrorAllowedFails if ( isinstance(exception, litellm.RateLimitError) and allowed_fails_policy.RateLimitErrorAllowedFails is not None ): return allowed_fails_policy.RateLimitErrorAllowedFails if ( isinstance(exception, litellm.ContentPolicyViolationError) and allowed_fails_policy.ContentPolicyViolationErrorAllowedFails is not None ): return allowed_fails_policy.ContentPolicyViolationErrorAllowedFails def _initialize_alerting(self): from litellm.integrations.SlackAlerting.slack_alerting import SlackAlerting if self.alerting_config is None: return router_alerting_config: AlertingConfig = self.alerting_config _slack_alerting_logger = SlackAlerting( alerting_threshold=router_alerting_config.alerting_threshold, alerting=["slack"], default_webhook_url=router_alerting_config.webhook_url, ) self.slack_alerting_logger = _slack_alerting_logger litellm.logging_callback_manager.add_litellm_callback(_slack_alerting_logger) # type: ignore litellm.logging_callback_manager.add_litellm_success_callback( _slack_alerting_logger.response_taking_too_long_callback ) verbose_router_logger.info( "\033[94m\nInitialized Alerting for litellm.Router\033[0m\n" ) def set_custom_routing_strategy( self, CustomRoutingStrategy: CustomRoutingStrategyBase ): """ Sets get_available_deployment and async_get_available_deployment on an instanced of litellm.Router Use this to set your custom routing strategy Args: CustomRoutingStrategy: litellm.router.CustomRoutingStrategyBase """ setattr( self, "get_available_deployment", CustomRoutingStrategy.get_available_deployment, ) setattr( self, "async_get_available_deployment", CustomRoutingStrategy.async_get_available_deployment, ) def flush_cache(self): litellm.cache = None self.cache.flush_cache() def reset(self): ## clean up on close litellm.success_callback = [] litellm._async_success_callback = [] litellm.failure_callback = [] litellm._async_failure_callback = [] self.retry_policy = None self.flush_cache()