# +-----------------------------------------------+ # | | # | Give Feedback / Get Help | # | https://github.com/BerriAI/litellm/issues/new | # | | # +-----------------------------------------------+ # # Thank you ! We ❤️ you! - Krrish & Ishaan import copy, httpx from datetime import datetime from typing import Dict, List, Optional, Union, Literal, Any import random, threading, time, traceback, uuid import litellm, openai from litellm.caching import RedisCache, InMemoryCache, DualCache import logging, asyncio import inspect, concurrent from openai import AsyncOpenAI from collections import defaultdict from litellm.router_strategy.least_busy import LeastBusyLoggingHandler from litellm.router_strategy.lowest_tpm_rpm import LowestTPMLoggingHandler from litellm.router_strategy.lowest_latency import LowestLatencyLoggingHandler from litellm.llms.custom_httpx.azure_dall_e_2 import ( CustomHTTPTransport, AsyncCustomHTTPTransport, ) from litellm.utils import ModelResponse, CustomStreamWrapper import copy from litellm._logging import verbose_router_logger import logging class Router: """ 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"}]) ``` """ model_names: List = [] cache_responses: Optional[bool] = False default_cache_time_seconds: int = 1 * 60 * 60 # 1 hour num_retries: int = 0 tenacity = None leastbusy_logger: Optional[LeastBusyLoggingHandler] = None lowesttpm_logger: Optional[LowestTPMLoggingHandler] = None def __init__( self, model_list: Optional[list] = 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 ## RELIABILITY ## num_retries: int = 0, timeout: Optional[float] = None, default_litellm_params={}, # default params for Router.chat.completion.create set_verbose: bool = False, fallbacks: List = [], allowed_fails: Optional[int] = None, context_window_fallbacks: List = [], model_group_alias: Optional[dict] = {}, retry_after: int = 0, # min time to wait before retrying a failed request routing_strategy: Literal[ "simple-shuffle", "least-busy", "usage-based-routing", "latency-based-routing", ] = "simple-shuffle", routing_strategy_args: dict = {}, # just for latency-based routing ) -> None: self.set_verbose = set_verbose 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"] = "local" # default to an in-memory cache redis_cache = None cache_config = {} self.client_ttl = client_ttl if redis_url is not None or ( redis_host is not None and redis_port is not None and redis_password 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. if model_list: model_list = copy.deepcopy(model_list) self.set_model_list(model_list) self.healthy_deployments: List = self.model_list for m in model_list: self.deployment_latency_map[m["litellm_params"]["model"]] = 0 self.allowed_fails = allowed_fails or litellm.allowed_fails 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 self.num_retries = num_retries or litellm.num_retries or 0 self.timeout = timeout or litellm.request_timeout self.retry_after = retry_after self.routing_strategy = routing_strategy self.fallbacks = fallbacks or litellm.fallbacks self.context_window_fallbacks = ( context_window_fallbacks or litellm.context_window_fallbacks ) self.model_exception_map: dict = ( {} ) # dict to store model: list exceptions. self.exceptions = {"gpt-3.5": ["API KEY Error", "Rate Limit Error", "good morning error"]} 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 = ( 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 self.chat = litellm.Chat(params=default_litellm_params) # 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} ) ### ROUTING SETUP ### if routing_strategy == "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.callbacks.append(self.leastbusy_logger) # type: ignore elif routing_strategy == "usage-based-routing": self.lowesttpm_logger = LowestTPMLoggingHandler( router_cache=self.cache, model_list=self.model_list ) if isinstance(litellm.callbacks, list): litellm.callbacks.append(self.lowesttpm_logger) # type: ignore elif routing_strategy == "latency-based-routing": self.lowestlatency_logger = LowestLatencyLoggingHandler( router_cache=self.cache, model_list=self.model_list, routing_args=routing_strategy_args, ) if isinstance(litellm.callbacks, list): litellm.callbacks.append(self.lowestlatency_logger) # type: ignore ## COOLDOWNS ## if isinstance(litellm.failure_callback, list): litellm.failure_callback.append(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" ) ### 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: kwargs["model"] = model kwargs["messages"] = messages kwargs["original_function"] = self._completion timeout = kwargs.get("request_timeout", self.timeout) kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) kwargs.setdefault("metadata", {}).update({"model_group": model}) with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor: # Submit the function to the executor with a timeout future = executor.submit(self.function_with_fallbacks, **kwargs) response = future.result(timeout=timeout) # type: ignore return response except Exception as e: raise e def _completion(self, model: str, messages: List[Dict[str, str]], **kwargs): 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), ) kwargs.setdefault("metadata", {}).update( {"deployment": deployment["litellm_params"]["model"]} ) data = deployment["litellm_params"].copy() kwargs["model_info"] = deployment.get("model_info", {}) 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 ) # 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 litellm.completion( **{ **data, "messages": messages, "caching": self.cache_responses, "client": model_client, **kwargs, } ) except Exception as e: raise e async def acompletion(self, model: str, messages: List[Dict[str, str]], **kwargs): try: kwargs["model"] = model kwargs["messages"] = messages kwargs["original_function"] = self._acompletion kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) timeout = kwargs.get("request_timeout", self.timeout) kwargs.setdefault("metadata", {}).update({"model_group": model}) response = await self.async_function_with_fallbacks(**kwargs) return response except Exception as e: raise e async def _acompletion(self, model: str, messages: List[Dict[str, str]], **kwargs): model_name = None try: verbose_router_logger.debug( f"Inside _acompletion()- model: {model}; kwargs: {kwargs}" ) deployment = self.get_available_deployment( model=model, messages=messages, specific_deployment=kwargs.pop("specific_deployment", None), ) kwargs.setdefault("metadata", {}).update( {"deployment": deployment["litellm_params"]["model"]} ) kwargs["model_info"] = deployment.get("model_info", {}) data = deployment["litellm_params"].copy() model_name = 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 self.total_calls[model_name] += 1 response = await litellm.acompletion( **{ **data, "messages": messages, "caching": self.cache_responses, "client": model_client, "timeout": self.timeout, **kwargs, } ) self.success_calls[model_name] += 1 verbose_router_logger.info( f"litellm.acompletion(model={model_name})\033[32m 200 OK\033[0m" ) 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 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) timeout = kwargs.get("request_timeout", self.timeout) 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): 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), ) kwargs.setdefault("metadata", {}).update( {"deployment": deployment["litellm_params"]["model"]} ) kwargs["model_info"] = deployment.get("model_info", {}) data = deployment["litellm_params"].copy() model_name = 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 self.total_calls[model_name] += 1 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) timeout = kwargs.get("request_timeout", self.timeout) kwargs.setdefault("metadata", {}).update({"model_group": model}) response = await self.async_function_with_fallbacks(**kwargs) return response except Exception as e: raise e async def _aimage_generation(self, prompt: str, model: str, **kwargs): 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), ) kwargs.setdefault("metadata", {}).update( {"deployment": deployment["litellm_params"]["model"]} ) kwargs["model_info"] = deployment.get("model_info", {}) data = deployment["litellm_params"].copy() model_name = 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 self.total_calls[model_name] += 1 response = await litellm.aimage_generation( **{ **data, "prompt": prompt, "caching": self.cache_responses, "client": model_client, **kwargs, } ) 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 def text_completion( self, model: str, prompt: str, is_retry: Optional[bool] = False, is_fallback: Optional[bool] = False, is_async: Optional[bool] = False, **kwargs, ): try: kwargs["model"] = model kwargs["prompt"] = prompt kwargs["original_function"] = self._acompletion kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) timeout = kwargs.get("request_timeout", self.timeout) kwargs.setdefault("metadata", {}).update({"model_group": model}) messages = [{"role": "user", "content": prompt}] # 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: if self.num_retries > 0: kwargs["model"] = model kwargs["messages"] = messages kwargs["original_function"] = self.completion return self.function_with_retries(**kwargs) else: 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, ): try: kwargs["model"] = model kwargs["prompt"] = prompt kwargs["original_function"] = self._atext_completion kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) timeout = kwargs.get("request_timeout", self.timeout) kwargs.setdefault("metadata", {}).update({"model_group": model}) response = await self.async_function_with_fallbacks(**kwargs) return response except Exception as 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}" ) deployment = self.get_available_deployment( model=model, messages=[{"role": "user", "content": prompt}], specific_deployment=kwargs.pop("specific_deployment", None), ) kwargs.setdefault("metadata", {}).update( {"deployment": deployment["litellm_params"]["model"]} ) kwargs["model_info"] = deployment.get("model_info", {}) data = deployment["litellm_params"].copy() model_name = 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 self.total_calls[model_name] += 1 response = await litellm.atext_completion( **{ **data, "prompt": prompt, "caching": self.cache_responses, "client": model_client, "timeout": self.timeout, **kwargs, } ) 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_name})\033[31m Exception {str(e)}\033[0m" ) if model_name is not None: self.fail_calls[model_name] += 1 raise e def embedding( self, model: str, input: Union[str, List], is_async: Optional[bool] = False, **kwargs, ) -> Union[List[float], None]: # pick the one that is available (lowest TPM/RPM) deployment = self.get_available_deployment( model=model, input=input, specific_deployment=kwargs.pop("specific_deployment", None), ) kwargs.setdefault("model_info", {}) kwargs.setdefault("metadata", {}).update( {"model_group": model, "deployment": deployment["litellm_params"]["model"]} ) # [TODO]: move to using async_function_with_fallbacks 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) 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 return litellm.embedding( **{ **data, "input": input, "caching": self.cache_responses, "client": model_client, **kwargs, } ) async def aembedding( self, model: str, input: Union[str, List], is_async: Optional[bool] = True, **kwargs, ) -> Union[List[float], None]: try: kwargs["model"] = model kwargs["input"] = input kwargs["original_function"] = self._aembedding kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries) timeout = kwargs.get("request_timeout", self.timeout) kwargs.setdefault("metadata", {}).update({"model_group": model}) response = await self.async_function_with_fallbacks(**kwargs) return response except Exception as e: raise e async def _aembedding(self, input: Union[str, List], model: str, **kwargs): try: verbose_router_logger.debug( f"Inside _aembedding()- model: {model}; kwargs: {kwargs}" ) deployment = self.get_available_deployment( model=model, input=input, specific_deployment=kwargs.pop("specific_deployment", None), ) kwargs.setdefault("metadata", {}).update( {"deployment": deployment["litellm_params"]["model"]} ) kwargs["model_info"] = deployment.get("model_info", {}) data = deployment["litellm_params"].copy() model_name = 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 self.total_calls[model_name] += 1 response = await litellm.aembedding( **{ **data, "input": input, "caching": self.cache_responses, "client": model_client, **kwargs, } ) 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 async def async_function_with_fallbacks(self, *args, **kwargs): """ Try calling the function_with_retries If it fails after num_retries, fall back to another model group """ model_group = kwargs.get("model") fallbacks = kwargs.get("fallbacks", self.fallbacks) context_window_fallbacks = kwargs.get( "context_window_fallbacks", self.context_window_fallbacks ) try: 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 try: if ( hasattr(e, "status_code") and e.status_code == 400 ): # don't retry a malformed request raise e verbose_router_logger.debug(f"Trying to fallback b/w models") if ( isinstance(e, litellm.ContextWindowExceededError) and context_window_fallbacks is not None ): fallback_model_group = None for ( item ) in context_window_fallbacks: # [{"gpt-3.5-turbo": ["gpt-4"]}] if list(item.keys())[0] == model_group: fallback_model_group = item[model_group] break if fallback_model_group is None: raise original_exception for mg in fallback_model_group: """ Iterate through the model groups and try calling that deployment """ try: kwargs["model"] = mg response = await self.async_function_with_retries( *args, **kwargs ) return response except Exception as e: pass elif fallbacks is not None: verbose_router_logger.debug(f"inside model fallbacks: {fallbacks}") for item in fallbacks: if list(item.keys())[0] == model_group: fallback_model_group = item[model_group] break if fallback_model_group is None: verbose_router_logger.info( f"No fallback model group found for original model_group={model_group}. Fallbacks={fallbacks}" ) raise original_exception for mg in fallback_model_group: """ Iterate through the model groups and try calling that deployment """ try: ## LOGGING kwargs = self.log_retry(kwargs=kwargs, e=original_exception) verbose_router_logger.info( f"Falling back to model_group = {mg}" ) kwargs["model"] = mg kwargs["metadata"]["model_group"] = mg response = await self.async_function_with_retries( *args, **kwargs ) return response except Exception as e: raise e except Exception as e: verbose_router_logger.debug(f"An exception occurred - {str(e)}") traceback.print_exc() raise original_exception async def async_function_with_retries(self, *args, **kwargs): 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) context_window_fallbacks = kwargs.pop( "context_window_fallbacks", self.context_window_fallbacks ) verbose_router_logger.debug( f"async function w/ retries: original_function - {original_function}" ) num_retries = kwargs.pop("num_retries") try: # if the function call is successful, no exception will be raised and we'll break out of the loop response = await original_function(*args, **kwargs) return response except Exception as e: original_exception = e ### CHECK IF RATE LIMIT / CONTEXT WINDOW ERROR w/ fallbacks available / Bad Request Error if ( isinstance(original_exception, litellm.ContextWindowExceededError) and context_window_fallbacks is None ) or ( isinstance(original_exception, openai.RateLimitError) and fallbacks is not None ): raise original_exception ### RETRY #### check if it should retry + back-off if required if "No models available" in str(e): timeout = litellm._calculate_retry_after( remaining_retries=num_retries, max_retries=num_retries, min_timeout=self.retry_after, ) await asyncio.sleep(timeout) elif hasattr(original_exception, "status_code") and litellm._should_retry( status_code=original_exception.status_code ): if hasattr(original_exception, "response") and hasattr( original_exception.response, "headers" ): timeout = litellm._calculate_retry_after( remaining_retries=num_retries, max_retries=num_retries, response_headers=original_exception.response.headers, min_timeout=self.retry_after, ) else: timeout = litellm._calculate_retry_after( remaining_retries=num_retries, max_retries=num_retries, min_timeout=self.retry_after, ) await asyncio.sleep(timeout) else: raise original_exception ## LOGGING if num_retries > 0: kwargs = self.log_retry(kwargs=kwargs, e=original_exception) for current_attempt in range(num_retries): verbose_router_logger.debug( f"retrying request. Current attempt - {current_attempt}; num retries: {num_retries}" ) try: # if the function call is successful, no exception will be raised and we'll break out of the loop response = await original_function(*args, **kwargs) if inspect.iscoroutinefunction( response ): # async errors are often returned as coroutines response = await response return response except Exception as e: ## LOGGING kwargs = self.log_retry(kwargs=kwargs, e=e) remaining_retries = num_retries - current_attempt if "No models available" in str(e): timeout = litellm._calculate_retry_after( remaining_retries=remaining_retries, max_retries=num_retries, min_timeout=self.retry_after, ) await asyncio.sleep(timeout) elif ( hasattr(e, "status_code") and hasattr(e, "response") and litellm._should_retry(status_code=e.status_code) ): if hasattr(e.response, "headers"): timeout = litellm._calculate_retry_after( remaining_retries=remaining_retries, max_retries=num_retries, response_headers=e.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, ) await asyncio.sleep(timeout) else: raise e raise original_exception def function_with_fallbacks(self, *args, **kwargs): """ Try calling the function_with_retries If it fails after num_retries, fall back to another model group """ model_group = kwargs.get("model") fallbacks = kwargs.get("fallbacks", self.fallbacks) context_window_fallbacks = kwargs.get( "context_window_fallbacks", self.context_window_fallbacks ) try: response = self.function_with_retries(*args, **kwargs) return response except Exception as e: original_exception = e verbose_router_logger.debug(f"An exception occurs {original_exception}") try: verbose_router_logger.debug( f"Trying to fallback b/w models. Initial model group: {model_group}" ) if ( isinstance(e, litellm.ContextWindowExceededError) and context_window_fallbacks is not None ): fallback_model_group = None for ( item ) in context_window_fallbacks: # [{"gpt-3.5-turbo": ["gpt-4"]}] if list(item.keys())[0] == model_group: fallback_model_group = item[model_group] break if fallback_model_group is None: raise original_exception for mg in fallback_model_group: """ Iterate through the model groups and try calling that deployment """ try: ## LOGGING kwargs = self.log_retry(kwargs=kwargs, e=original_exception) kwargs["model"] = mg response = self.function_with_fallbacks(*args, **kwargs) return response except Exception as e: pass elif fallbacks is not None: verbose_router_logger.debug(f"inside model fallbacks: {fallbacks}") fallback_model_group = None for item in fallbacks: if list(item.keys())[0] == model_group: fallback_model_group = item[model_group] break if fallback_model_group is None: raise original_exception for mg in fallback_model_group: """ Iterate through the model groups and try calling that deployment """ try: ## LOGGING kwargs = self.log_retry(kwargs=kwargs, e=original_exception) kwargs["model"] = mg response = self.function_with_fallbacks(*args, **kwargs) return response except Exception as e: raise e except Exception as e: raise e raise original_exception def function_with_retries(self, *args, **kwargs): """ Try calling the model 3 times. Shuffle between available deployments. """ verbose_router_logger.debug( f"Inside function with retries: args - {args}; kwargs - {kwargs}" ) original_function = kwargs.pop("original_function") num_retries = kwargs.pop("num_retries") fallbacks = kwargs.pop("fallbacks", self.fallbacks) context_window_fallbacks = kwargs.pop( "context_window_fallbacks", self.context_window_fallbacks ) try: # if the function call is successful, no exception will be raised and we'll break out of the loop response = original_function(*args, **kwargs) return response except Exception as e: original_exception = e verbose_router_logger.debug( f"num retries in function with retries: {num_retries}" ) ### CHECK IF RATE LIMIT / CONTEXT WINDOW ERROR if ( isinstance(original_exception, litellm.ContextWindowExceededError) and context_window_fallbacks is None ) or ( isinstance(original_exception, openai.RateLimitError) and fallbacks is not None ): raise original_exception ## LOGGING if num_retries > 0: kwargs = self.log_retry(kwargs=kwargs, e=original_exception) ### RETRY for current_attempt in range(num_retries): verbose_router_logger.debug( f"retrying request. Current attempt - {current_attempt}; retries left: {num_retries}" ) try: # if the function call is successful, no exception will be raised and we'll break out of the loop response = original_function(*args, **kwargs) return response except Exception as e: ## LOGGING kwargs = self.log_retry(kwargs=kwargs, e=e) remaining_retries = num_retries - current_attempt if "No models available" in str(e): timeout = litellm._calculate_retry_after( remaining_retries=remaining_retries, max_retries=num_retries, min_timeout=self.retry_after, ) time.sleep(timeout) elif ( hasattr(e, "status_code") and hasattr(e, "response") and litellm._should_retry(status_code=e.status_code) ): if hasattr(e.response, "headers"): timeout = litellm._calculate_retry_after( remaining_retries=remaining_retries, max_retries=num_retries, response_headers=e.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, ) time.sleep(timeout) else: raise e raise original_exception ### HELPER FUNCTIONS def deployment_callback_on_failure( self, kwargs, # kwargs to completion completion_response, # response from completion start_time, end_time, # start/end time ): try: exception = kwargs.get("exception", None) exception_type = type(exception) exception_status = getattr(exception, "status_code", "") exception_cause = getattr(exception, "__cause__", "") exception_message = getattr(exception, "message", "") exception_str = ( str(exception_type) + "Status: " + str(exception_status) + "Message: " + str(exception_cause) + str(exception_message) + "Full exception" + str(exception) ) model_name = kwargs.get("model", None) # i.e. gpt35turbo custom_llm_provider = kwargs.get("litellm_params", {}).get( "custom_llm_provider", None ) # i.e. azure metadata = kwargs.get("litellm_params", {}).get("metadata", None) _model_info = kwargs.get("litellm_params", {}).get("model_info", {}) if isinstance(_model_info, dict): deployment_id = _model_info.get("id", None) self._set_cooldown_deployments( deployment_id ) # setting deployment_id in cooldown deployments if metadata: deployment = metadata.get("deployment", None) deployment_exceptions = self.model_exception_map.get(deployment, []) deployment_exceptions.append(exception_str) self.model_exception_map[deployment] = deployment_exceptions verbose_router_logger.debug("\nEXCEPTION FOR DEPLOYMENTS\n") verbose_router_logger.debug(self.model_exception_map) for model in self.model_exception_map: verbose_router_logger.debug( f"Model {model} had {len(self.model_exception_map[model])} exception" ) if custom_llm_provider: model_name = f"{custom_llm_provider}/{model_name}" except Exception as e: raise e 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 != "metadata": 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 self.previous_models.append(previous_model) kwargs["metadata"]["previous_models"] = self.previous_models return kwargs except Exception as e: raise e def _set_cooldown_deployments(self, deployment: Optional[str] = None): """ Add a model to the list of models being cooled down for that minute, if it exceeds the allowed fails / minute """ if deployment is None: return current_minute = datetime.now().strftime("%H-%M") # get current fails for deployment # update the number of failed calls # if it's > allowed fails # cooldown deployment current_fails = self.failed_calls.get_cache(key=deployment) or 0 updated_fails = current_fails + 1 verbose_router_logger.debug( f"Attempting to add {deployment} to cooldown list. updated_fails: {updated_fails}; self.allowed_fails: {self.allowed_fails}" ) if updated_fails > self.allowed_fails: # get the current cooldown list for that minute cooldown_key = f"{current_minute}:cooldown_models" # group cooldown models by minute to reduce number of redis calls cached_value = self.cache.get_cache(key=cooldown_key) verbose_router_logger.debug(f"adding {deployment} to cooldown models") # update value try: if deployment in cached_value: pass else: cached_value = cached_value + [deployment] # save updated value self.cache.set_cache(value=cached_value, key=cooldown_key, ttl=1) except: cached_value = [deployment] # save updated value self.cache.set_cache(value=cached_value, key=cooldown_key, ttl=1) else: self.failed_calls.set_cache(key=deployment, value=updated_fails, ttl=1) def _get_cooldown_deployments(self): """ Get the list of models being cooled down for this minute """ current_minute = datetime.now().strftime("%H-%M") # get the current cooldown list for that minute cooldown_key = f"{current_minute}:cooldown_models" # ---------------------- # Return cooldown models # ---------------------- cooldown_models = self.cache.get_cache(key=cooldown_key) or [] verbose_router_logger.debug(f"retrieve cooldown models: {cooldown_models}") return cooldown_models def set_client(self, model: dict): """ Initializes Azure/OpenAI clients. Stores them in cache, b/c of this - https://github.com/BerriAI/litellm/issues/1278 """ client_ttl = self.client_ttl litellm_params = model.get("litellm_params", {}) model_name = litellm_params.get("model") model_id = model["model_info"]["id"] #### for OpenAI / Azure we need to initalize the Client for High Traffic ######## custom_llm_provider = litellm_params.get("custom_llm_provider") custom_llm_provider = custom_llm_provider or model_name.split("/", 1)[0] or "" default_api_base = None default_api_key = None if custom_llm_provider in litellm.openai_compatible_providers: _, custom_llm_provider, api_key, api_base = litellm.get_llm_provider( model=model_name ) default_api_base = api_base default_api_key = api_key if ( model_name in litellm.open_ai_chat_completion_models or custom_llm_provider in litellm.openai_compatible_providers or custom_llm_provider == "azure" or custom_llm_provider == "custom_openai" or custom_llm_provider == "openai" or "ft:gpt-3.5-turbo" in model_name or model_name in litellm.open_ai_embedding_models ): # glorified / complicated reading of configs # user can pass vars directly or they can pas os.environ/AZURE_API_KEY, in which case we will read the env # we do this here because we init clients for Azure, OpenAI and we need to set the right key api_key = litellm_params.get("api_key") or default_api_key if api_key and api_key.startswith("os.environ/"): api_key_env_name = api_key.replace("os.environ/", "") api_key = litellm.get_secret(api_key_env_name) litellm_params["api_key"] = api_key api_base = litellm_params.get("api_base") base_url = litellm_params.get("base_url") api_base = ( api_base or base_url or default_api_base ) # allow users to pass in `api_base` or `base_url` for azure if api_base and api_base.startswith("os.environ/"): api_base_env_name = api_base.replace("os.environ/", "") api_base = litellm.get_secret(api_base_env_name) litellm_params["api_base"] = api_base api_version = litellm_params.get("api_version") if api_version and api_version.startswith("os.environ/"): api_version_env_name = api_version.replace("os.environ/", "") api_version = litellm.get_secret(api_version_env_name) litellm_params["api_version"] = api_version timeout = litellm_params.pop("timeout", None) if isinstance(timeout, str) and timeout.startswith("os.environ/"): timeout_env_name = timeout.replace("os.environ/", "") timeout = litellm.get_secret(timeout_env_name) litellm_params["timeout"] = timeout stream_timeout = litellm_params.pop( "stream_timeout", timeout ) # if no stream_timeout is set, default to timeout if isinstance(stream_timeout, str) and stream_timeout.startswith( "os.environ/" ): stream_timeout_env_name = stream_timeout.replace("os.environ/", "") stream_timeout = litellm.get_secret(stream_timeout_env_name) litellm_params["stream_timeout"] = stream_timeout max_retries = litellm_params.pop("max_retries", 2) if isinstance(max_retries, str) and max_retries.startswith("os.environ/"): max_retries_env_name = max_retries.replace("os.environ/", "") max_retries = litellm.get_secret(max_retries_env_name) litellm_params["max_retries"] = max_retries if "azure" in model_name: if api_base is None: raise ValueError( f"api_base is required for Azure OpenAI. Set it on your config. Model - {model}" ) if api_version is None: api_version = "2023-07-01-preview" if "gateway.ai.cloudflare.com" in api_base: if not api_base.endswith("/"): api_base += "/" azure_model = model_name.replace("azure/", "") api_base += f"{azure_model}" cache_key = f"{model_id}_async_client" _client = openai.AsyncAzureOpenAI( api_key=api_key, base_url=api_base, api_version=api_version, timeout=timeout, max_retries=max_retries, http_client=httpx.AsyncClient( transport=AsyncCustomHTTPTransport(), limits=httpx.Limits( max_connections=1000, max_keepalive_connections=100 ), ), # type: ignore ) self.cache.set_cache( key=cache_key, value=_client, ttl=client_ttl, local_only=True, ) # cache for 1 hr cache_key = f"{model_id}_client" _client = openai.AzureOpenAI( # type: ignore api_key=api_key, base_url=api_base, api_version=api_version, timeout=timeout, max_retries=max_retries, http_client=httpx.Client( transport=CustomHTTPTransport(), limits=httpx.Limits( max_connections=1000, max_keepalive_connections=100 ), ), # type: ignore ) self.cache.set_cache( key=cache_key, value=_client, ttl=client_ttl, local_only=True, ) # cache for 1 hr # streaming clients can have diff timeouts cache_key = f"{model_id}_stream_async_client" _client = openai.AsyncAzureOpenAI( # type: ignore api_key=api_key, base_url=api_base, api_version=api_version, timeout=stream_timeout, max_retries=max_retries, http_client=httpx.AsyncClient( transport=AsyncCustomHTTPTransport(), limits=httpx.Limits( max_connections=1000, max_keepalive_connections=100 ), ), # type: ignore ) self.cache.set_cache( key=cache_key, value=_client, ttl=client_ttl, local_only=True, ) # cache for 1 hr cache_key = f"{model_id}_stream_client" _client = openai.AzureOpenAI( # type: ignore api_key=api_key, base_url=api_base, api_version=api_version, timeout=stream_timeout, max_retries=max_retries, http_client=httpx.Client( transport=CustomHTTPTransport(), limits=httpx.Limits( max_connections=1000, max_keepalive_connections=100 ), ), # type: ignore ) self.cache.set_cache( key=cache_key, value=_client, ttl=client_ttl, local_only=True, ) # cache for 1 hr else: verbose_router_logger.debug( f"Initializing Azure OpenAI Client for {model_name}, Api Base: {str(api_base)}, Api Key:{api_key}" ) cache_key = f"{model_id}_async_client" _client = openai.AsyncAzureOpenAI( # type: ignore api_key=api_key, azure_endpoint=api_base, api_version=api_version, timeout=timeout, max_retries=max_retries, http_client=httpx.AsyncClient( transport=AsyncCustomHTTPTransport(), limits=httpx.Limits( max_connections=1000, max_keepalive_connections=100 ), ), # type: ignore ) self.cache.set_cache( key=cache_key, value=_client, ttl=client_ttl, local_only=True, ) # cache for 1 hr cache_key = f"{model_id}_client" _client = openai.AzureOpenAI( # type: ignore api_key=api_key, azure_endpoint=api_base, api_version=api_version, timeout=timeout, max_retries=max_retries, http_client=httpx.Client( transport=CustomHTTPTransport(), limits=httpx.Limits( max_connections=1000, max_keepalive_connections=100 ), ), # type: ignore ) self.cache.set_cache( key=cache_key, value=_client, ttl=client_ttl, local_only=True, ) # cache for 1 hr # streaming clients should have diff timeouts cache_key = f"{model_id}_stream_async_client" _client = openai.AsyncAzureOpenAI( # type: ignore api_key=api_key, azure_endpoint=api_base, api_version=api_version, timeout=stream_timeout, max_retries=max_retries, http_client=httpx.AsyncClient( transport=AsyncCustomHTTPTransport(), limits=httpx.Limits( max_connections=1000, max_keepalive_connections=100 ), ), ) self.cache.set_cache( key=cache_key, value=_client, ttl=client_ttl, local_only=True, ) # cache for 1 hr cache_key = f"{model_id}_stream_client" _client = openai.AzureOpenAI( # type: ignore api_key=api_key, azure_endpoint=api_base, api_version=api_version, timeout=stream_timeout, max_retries=max_retries, http_client=httpx.Client( transport=CustomHTTPTransport(), limits=httpx.Limits( max_connections=1000, max_keepalive_connections=100 ), ), ) self.cache.set_cache( key=cache_key, value=_client, ttl=client_ttl, local_only=True, ) # cache for 1 hr else: verbose_router_logger.debug( f"Initializing OpenAI Client for {model_name}, Api Base:{str(api_base)}, Api Key:{api_key}" ) cache_key = f"{model_id}_async_client" _client = openai.AsyncOpenAI( # type: ignore api_key=api_key, base_url=api_base, timeout=timeout, max_retries=max_retries, http_client=httpx.AsyncClient( transport=AsyncCustomHTTPTransport(), limits=httpx.Limits( max_connections=1000, max_keepalive_connections=100 ), ), # type: ignore ) self.cache.set_cache( key=cache_key, value=_client, ttl=client_ttl, local_only=True, ) # cache for 1 hr cache_key = f"{model_id}_client" _client = openai.OpenAI( # type: ignore api_key=api_key, base_url=api_base, timeout=timeout, max_retries=max_retries, http_client=httpx.Client( transport=CustomHTTPTransport(), limits=httpx.Limits( max_connections=1000, max_keepalive_connections=100 ), ), # type: ignore ) self.cache.set_cache( key=cache_key, value=_client, ttl=client_ttl, local_only=True, ) # cache for 1 hr # streaming clients should have diff timeouts cache_key = f"{model_id}_stream_async_client" _client = openai.AsyncOpenAI( # type: ignore api_key=api_key, base_url=api_base, timeout=stream_timeout, max_retries=max_retries, http_client=httpx.AsyncClient( transport=AsyncCustomHTTPTransport(), limits=httpx.Limits( max_connections=1000, max_keepalive_connections=100 ), ), # type: ignore ) self.cache.set_cache( key=cache_key, value=_client, ttl=client_ttl, local_only=True, ) # cache for 1 hr # streaming clients should have diff timeouts cache_key = f"{model_id}_stream_client" _client = openai.OpenAI( # type: ignore api_key=api_key, base_url=api_base, timeout=stream_timeout, max_retries=max_retries, http_client=httpx.Client( transport=CustomHTTPTransport(), limits=httpx.Limits( max_connections=1000, max_keepalive_connections=100 ), ), # type: ignore ) self.cache.set_cache( key=cache_key, value=_client, ttl=client_ttl, local_only=True, ) # cache for 1 hr def set_model_list(self, model_list: list): self.model_list = copy.deepcopy(model_list) # we add api_base/api_key each model so load balancing between azure/gpt on api_base1 and api_base2 works import os for model in self.model_list: #### MODEL ID INIT ######## model_info = model.get("model_info", {}) model_info["id"] = model_info.get("id", str(uuid.uuid4())) model["model_info"] = model_info #### DEPLOYMENT NAMES INIT ######## self.deployment_names.append(model["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 ( model["litellm_params"].get("rpm") is None and model.get("rpm") is not None ): model["litellm_params"]["rpm"] = model.get("rpm") if ( model["litellm_params"].get("tpm") is None and model.get("tpm") is not None ): model["litellm_params"]["tpm"] = model.get("tpm") #### VALIDATE MODEL ######## # check if model provider in supported providers ( _model, custom_llm_provider, dynamic_api_key, api_base, ) = litellm.get_llm_provider( model=model["litellm_params"]["model"], custom_llm_provider=model["litellm_params"].get( "custom_llm_provider", None ), ) if custom_llm_provider not in litellm.provider_list: raise Exception(f"Unsupported provider - {custom_llm_provider}") self.set_client(model=model) verbose_router_logger.debug(f"\nInitialized Model List {self.model_list}") self.model_names = [m["model_name"] for m in model_list] def get_model_names(self): return self.model_names 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"] if client_type == "async": if kwargs.get("stream") == True: cache_key = f"{model_id}_stream_async_client" client = self.cache.get_cache(key=cache_key, local_only=True) if client is None: """ Re-initialize the client """ self.set_client(model=deployment) client = self.cache.get_cache(key=cache_key, local_only=True) return client else: cache_key = f"{model_id}_async_client" client = self.cache.get_cache(key=cache_key, local_only=True) if client is None: """ Re-initialize the client """ self.set_client(model=deployment) client = self.cache.get_cache(key=cache_key, local_only=True) return client else: if kwargs.get("stream") == True: cache_key = f"{model_id}_stream_client" client = self.cache.get_cache(key=cache_key) if client is None: """ Re-initialize the client """ self.set_client(model=deployment) client = self.cache.get_cache(key=cache_key) return client else: cache_key = f"{model_id}_client" client = self.cache.get_cache(key=cache_key) if client is None: """ Re-initialize the client """ self.set_client(model=deployment) client = self.cache.get_cache(key=cache_key) return client 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, ): """ 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 if specific_deployment == True: # users can also specify a specific deployment name. At this point we should check if they are just trying to call a specific deployment for deployment in self.model_list: deployment_model = deployment.get("litellm_params").get("model") if deployment_model == model: # User Passed a specific deployment name on their config.yaml, example azure/chat-gpt-v-2 # return the first deployment where the `model` matches the specificed deployment name return deployment raise ValueError( f"LiteLLM Router: Trying to call specific deployment, but Model:{model} does not exist in Model List: {self.model_list}" ) # check if aliases set on litellm model alias map if model in self.model_group_alias: verbose_router_logger.debug( f"Using a model alias. Got Request for {model}, sending requests to {self.model_group_alias.get(model)}" ) model = self.model_group_alias[model] ## get healthy deployments ### get all deployments healthy_deployments = [m for m in self.model_list if m["model_name"] == model] if len(healthy_deployments) == 0: # check if the user sent in a deployment name instead healthy_deployments = [ m for m in self.model_list if m["litellm_params"]["model"] == model ] verbose_router_logger.debug( f"initial list of deployments: {healthy_deployments}" ) # filter out the deployments currently cooling down deployments_to_remove = [] # cooldown_deployments is a list of model_id's cooling down, cooldown_deployments = ["16700539-b3cd-42f4-b426-6a12a1bb706a", "16700539-b3cd-42f4-b426-7899"] cooldown_deployments = self._get_cooldown_deployments() 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) verbose_router_logger.debug( f"healthy deployments: length {len(healthy_deployments)} {healthy_deployments}" ) if len(healthy_deployments) == 0: raise ValueError("No models available") 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 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 ) elif self.routing_strategy == "simple-shuffle": # if users pass rpm or tpm, we do a random weighted pick - based on rpm/tpm ############## Check if we can do a RPM/TPM based weighted pick ################# rpm = healthy_deployments[0].get("litellm_params").get("rpm", None) if rpm is not None: # use weight-random pick if rpms provided rpms = [m["litellm_params"].get("rpm", 0) for m in healthy_deployments] verbose_router_logger.debug(f"\nrpms {rpms}") total_rpm = sum(rpms) weights = [rpm / total_rpm for rpm in rpms] verbose_router_logger.debug(f"\n weights {weights}") # Perform weighted random pick selected_index = random.choices(range(len(rpms)), weights=weights)[0] verbose_router_logger.debug(f"\n selected index, {selected_index}") deployment = healthy_deployments[selected_index] return deployment or deployment[0] ############## Check if we can do a RPM/TPM based weighted pick ################# tpm = healthy_deployments[0].get("litellm_params").get("tpm", None) if tpm is not None: # use weight-random pick if rpms provided tpms = [m["litellm_params"].get("tpm", 0) for m in healthy_deployments] verbose_router_logger.debug(f"\ntpms {tpms}") total_tpm = sum(tpms) weights = [tpm / total_tpm for tpm in tpms] verbose_router_logger.debug(f"\n weights {weights}") # Perform weighted random pick selected_index = random.choices(range(len(tpms)), weights=weights)[0] verbose_router_logger.debug(f"\n selected index, {selected_index}") deployment = healthy_deployments[selected_index] return deployment or deployment[0] ############## No RPM/TPM passed, we do a random pick ################# item = random.choice(healthy_deployments) return item or item[0] 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 ) 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, messages=messages, input=input, ) if deployment is None: raise ValueError("No models available.") return 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.flush_cache()