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litellm/litellm/router.py

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# +-----------------------------------------------+
# | |
# | 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, BinaryIO
import random, threading, time, traceback, uuid
import litellm, openai, hashlib, json
from litellm.caching import RedisCache, InMemoryCache, DualCache
import datetime as datetime_og
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.router_strategy.lowest_tpm_rpm_v2 import LowestTPMLoggingHandler_v2
from litellm.llms.custom_httpx.azure_dall_e_2 import (
CustomHTTPTransport,
AsyncCustomHTTPTransport,
)
from litellm.utils import ModelResponse, CustomStreamWrapper, get_utc_datetime
import copy
from litellm._logging import verbose_router_logger
import logging
from litellm.types.router import Deployment, ModelInfo, LiteLLM_Params, RouterErrors
class Router:
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,
debug_level: Literal["DEBUG", "INFO"] = "INFO",
fallbacks: List = [],
context_window_fallbacks: List = [],
model_group_alias: Optional[dict] = {},
enable_pre_call_checks: bool = False,
retry_after: int = 0, # min time to wait before retrying a failed request
allowed_fails: Optional[
int
] = None, # Number of times a deployment can failbefore being added to cooldown
cooldown_time: float = 1, # (seconds) time to cooldown a deployment after failure
routing_strategy: Literal[
"simple-shuffle",
"least-busy",
"usage-based-routing",
"latency-based-routing",
] = "simple-shuffle",
routing_strategy_args: dict = {}, # just for latency-based routing
semaphore: Optional[asyncio.Semaphore] = None,
) -> 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.
num_retries (int): Number of retries for failed requests. Defaults to 0.
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"]): Routing strategy. Defaults to "simple-shuffle".
routing_strategy_args (dict): Additional args for latency-based routing. Defaults to {}.
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/<your-deployment-name-1>",
"api_key": <your-api-key>,
"api_version": <your-api-version>,
"api_base": <your-api-base>
},
},
{
"model_name": "azure-gpt-3.5-turbo", # model alias
"litellm_params": { # params for litellm completion/embedding call
"model": "azure/<your-deployment-name-2>",
"api_key": <your-api-key>,
"api_version": <your-api-version>,
"api_base": <your-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": <your-api-key>,
},
]
router = Router(model_list=model_list, fallbacks=[{"azure-gpt-3.5-turbo": "openai-gpt-3.5-turbo"}])
```
"""
if semaphore:
self.semaphore = semaphore
self.set_verbose = set_verbose
self.debug_level = debug_level
self.enable_pre_call_checks = enable_pre_call_checks
if self.set_verbose == True:
if debug_level == "INFO":
verbose_router_logger.setLevel(logging.INFO)
elif debug_level == "DEBUG":
verbose_router_logger.setLevel(logging.DEBUG)
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.
self.default_deployment = None # use this to track the users default deployment, when they want to use model = *
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.cooldown_time = cooldown_time or 1
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.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, 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 ###
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 == "usage-based-routing-v2":
self.lowesttpm_logger_v2 = LowestTPMLoggingHandler_v2(
router_cache=self.cache, model_list=self.model_list
)
if isinstance(litellm.callbacks, list):
litellm.callbacks.append(self.lowesttpm_logger_v2) # 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.info(
f"Intialized router with Routing strategy: {self.routing_strategy}\n\nRouting fallbacks: {self.fallbacks}\n\nRouting context window fallbacks: {self.context_window_fallbacks}"
)
def print_deployment(self, deployment: dict):
"""
returns a copy of the deployment with the api key masked
"""
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
timeout = kwargs.get("request_timeout", self.timeout)
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 _completion(self, model: str, messages: List[Dict[str, str]], **kwargs):
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),
)
kwargs.setdefault("metadata", {}).update(
{
"deployment": deployment["litellm_params"]["model"],
"model_info": deployment.get("model_info", {}),
}
)
data = deployment["litellm_params"].copy()
kwargs["model_info"] = deployment.get("model_info", {})
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
)
# 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 = 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"
)
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
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):
"""
- 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}"
)
deployment = await self.async_get_available_deployment(
model=model,
messages=messages,
specific_deployment=kwargs.pop("specific_deployment", None),
)
# debug how often this deployment picked
self._track_deployment_metrics(deployment=deployment)
kwargs.setdefault("metadata", {}).update(
{
"deployment": deployment["litellm_params"]["model"],
"model_info": deployment.get("model_info", {}),
}
)
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 and v is not None
): # 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
timeout = (
data.get(
"timeout", None
) # timeout set on litellm_params for this deployment
or self.timeout # timeout set on router
or kwargs.get(
"timeout", None
) # this uses default_litellm_params when nothing is set
)
_response = litellm.acompletion(
**{
**data,
"messages": messages,
"caching": self.cache_responses,
"client": model_client,
"timeout": timeout,
**kwargs,
}
)
rpm_semaphore = self._get_client(
deployment=deployment, kwargs=kwargs, client_type="rpm_client"
)
if (
rpm_semaphore is not None
and isinstance(rpm_semaphore, asyncio.Semaphore)
and self.routing_strategy == "usage-based-routing-v2"
):
async with rpm_semaphore:
"""
- Check rpm limits before making the call
"""
await self.lowesttpm_logger_v2.pre_call_rpm_check(deployment)
response = await _response
else:
response = await _response
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)
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"],
"model_info": deployment.get("model_info", {}),
}
)
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 = await self.async_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"],
"model_info": deployment.get("model_info", {}),
}
)
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
async def atranscription(self, file: BinaryIO, 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
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 _atranscription(self, file: BinaryIO, model: str, **kwargs):
try:
verbose_router_logger.debug(
f"Inside _atranscription()- model: {model}; kwargs: {kwargs}"
)
deployment = await self.async_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"],
"model_info": deployment.get("model_info", {}),
}
)
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.atranscription(
**{
**data,
"file": file,
"caching": self.cache_responses,
"client": model_client,
**kwargs,
}
)
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 amoderation(self, model: str, input: str, **kwargs):
try:
kwargs["model"] = model
kwargs["input"] = input
kwargs["original_function"] = self._amoderation
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 _amoderation(self, model: str, input: str, **kwargs):
model_name = None
try:
verbose_router_logger.debug(
f"Inside _moderation()- model: {model}; kwargs: {kwargs}"
)
deployment = await self.async_get_available_deployment(
model=model,
input=input,
specific_deployment=kwargs.pop("specific_deployment", None),
)
kwargs.setdefault("metadata", {}).update(
{
"deployment": deployment["litellm_params"]["model"],
"model_info": deployment.get("model_info", {}),
}
)
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 and v is not None
): # 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
timeout = (
data.get(
"timeout", None
) # timeout set on litellm_params for this deployment
or self.timeout # timeout set on router
or kwargs.get(
"timeout", None
) # this uses default_litellm_params when nothing is set
)
response = await litellm.amoderation(
**{
**data,
"input": input,
"caching": self.cache_responses,
"client": model_client,
"timeout": timeout,
**kwargs,
}
)
self.success_calls[model_name] += 1
verbose_router_logger.info(
f"litellm.amoderation(model={model_name})\033[32m 200 OK\033[0m"
)
return response
except Exception as e:
verbose_router_logger.info(
f"litellm.amoderation(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 = await self.async_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"],
"model_info": deployment.get("model_info", {}),
}
)
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})\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,
) -> Union[List[float], None]:
try:
kwargs["model"] = model
kwargs["input"] = input
kwargs["original_function"] = self._embedding
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 _embedding(self, input: Union[str, List], model: str, **kwargs):
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),
)
kwargs.setdefault("metadata", {}).update(
{
"deployment": deployment["litellm_params"]["model"],
"model_info": deployment.get("model_info", {}),
}
)
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="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
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,
) -> 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 = await self.async_get_available_deployment(
model=model,
input=input,
specific_deployment=kwargs.pop("specific_deployment", None),
)
kwargs.setdefault("metadata", {}).update(
{
"deployment": deployment["litellm_params"]["model"],
"model_info": deployment.get("model_info", {}),
}
)
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:
verbose_router_logger.debug(f"Trying to fallback b/w models")
if (
hasattr(e, "status_code")
and e.status_code == 400
and not isinstance(e, litellm.ContextWindowExceededError)
): # don't retry a malformed request
raise e
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
kwargs.setdefault("metadata", {}).update(
{"model_group": mg}
) # update model_group used, if fallbacks are done
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.setdefault("metadata", {}).update(
{"model_group": mg}
) # update model_group used, if fallbacks are done
response = await self.async_function_with_fallbacks(
*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 not 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 RouterErrors.user_defined_ratelimit_error.value in str(e):
raise e # don't wait to retry if deployment hits user-defined rate-limit
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:
if (
hasattr(e, "status_code")
and e.status_code == 400
and not isinstance(e, litellm.ContextWindowExceededError)
): # don't retry a malformed request
raise e
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
kwargs.setdefault("metadata", {}).update(
{"model_group": mg}
) # update model_group used, if fallbacks are done
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
kwargs.setdefault("metadata", {}).update(
{"model_group": mg}
) # update model_group used, if fallbacks are done
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 not 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 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 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):
"""
Update deployment rpm for that minute
"""
rpm_key = deployment_id
request_count = self.cache.get_cache(key=rpm_key, 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
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
dt = get_utc_datetime()
current_minute = dt.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}"
)
cooldown_time = self.cooldown_time or 1
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=cooldown_time
)
except:
cached_value = [deployment]
# save updated value
self.cache.set_cache(
value=cached_value, key=cooldown_key, ttl=cooldown_time
)
else:
self.failed_calls.set_cache(
key=deployment, value=updated_fails, ttl=cooldown_time
)
async def _async_get_cooldown_deployments(self):
"""
Async implementation of '_get_cooldown_deployments'
"""
dt = get_utc_datetime()
current_minute = dt.strftime("%H-%M")
# get the current cooldown list for that minute
cooldown_key = f"{current_minute}:cooldown_models"
# ----------------------
# Return cooldown models
# ----------------------
cooldown_models = await self.cache.async_get_cache(key=cooldown_key) or []
verbose_router_logger.debug(f"retrieve cooldown models: {cooldown_models}")
return cooldown_models
def _get_cooldown_deployments(self):
"""
Get the list of models being cooled down for this minute
"""
dt = get_utc_datetime()
current_minute = dt.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
- Initializes Semaphore for client w/ rpm. Stores them in cache. b/c of this - https://github.com/BerriAI/litellm/issues/2994
"""
client_ttl = self.client_ttl
litellm_params = model.get("litellm_params", {})
model_name = litellm_params.get("model")
model_id = model["model_info"]["id"]
# ### IF RPM SET - initialize a semaphore ###
rpm = litellm_params.get("rpm", None)
if rpm:
semaphore = asyncio.Semaphore(rpm)
cache_key = f"{model_id}_rpm_client"
self.cache.set_cache(
key=cache_key,
value=semaphore,
local_only=True,
)
# print("STORES SEMAPHORE IN CACHE")
#### 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 == "azure_text"
or custom_llm_provider == "custom_openai"
or custom_llm_provider == "openai"
or custom_llm_provider == "text-completion-openai"
or "ft:gpt-3.5-turbo" in model_name
or model_name in litellm.open_ai_embedding_models
):
if custom_llm_provider == "azure":
if litellm.utils._is_non_openai_azure_model(model_name):
custom_llm_provider = "openai"
# remove azure prefx from model_name
model_name = model_name.replace("azure/", "")
# 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
# proxy support
import os
import httpx
# Check if the HTTP_PROXY and HTTPS_PROXY environment variables are set and use them accordingly.
http_proxy = os.getenv("HTTP_PROXY", None)
https_proxy = os.getenv("HTTPS_PROXY", None)
no_proxy = os.getenv("NO_PROXY", None)
# Create the proxies dictionary only if the environment variables are set.
sync_proxy_mounts = None
async_proxy_mounts = None
if http_proxy is not None and https_proxy is not None:
sync_proxy_mounts = {
"http://": httpx.HTTPTransport(proxy=httpx.Proxy(url=http_proxy)),
"https://": httpx.HTTPTransport(proxy=httpx.Proxy(url=https_proxy)),
}
async_proxy_mounts = {
"http://": httpx.AsyncHTTPTransport(
proxy=httpx.Proxy(url=http_proxy)
),
"https://": httpx.AsyncHTTPTransport(
proxy=httpx.Proxy(url=https_proxy)
),
}
# assume no_proxy is a list of comma separated urls
if no_proxy is not None and isinstance(no_proxy, str):
no_proxy_urls = no_proxy.split(",")
for url in no_proxy_urls: # set no-proxy support for specific urls
sync_proxy_mounts[url] = None # type: ignore
async_proxy_mounts[url] = None # type: ignore
organization = litellm_params.get("organization", None)
if isinstance(organization, str) and organization.startswith("os.environ/"):
organization_env_name = organization.replace("os.environ/", "")
organization = litellm.get_secret(organization_env_name)
litellm_params["organization"] = organization
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
),
mounts=async_proxy_mounts,
), # 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
),
mounts=sync_proxy_mounts,
), # 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
),
mounts=async_proxy_mounts,
), # 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
),
mounts=sync_proxy_mounts,
), # type: ignore
)
self.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
else:
_api_key = api_key
if _api_key is not None and isinstance(_api_key, str):
# only show first 5 chars of api_key
_api_key = _api_key[:8] + "*" * 15
verbose_router_logger.debug(
f"Initializing Azure OpenAI Client for {model_name}, Api Base: {str(api_base)}, Api Key:{_api_key}"
)
azure_client_params = {
"api_key": api_key,
"azure_endpoint": api_base,
"api_version": api_version,
}
from litellm.llms.azure import select_azure_base_url_or_endpoint
# this decides if we should set azure_endpoint or base_url on Azure OpenAI Client
# required to support GPT-4 vision enhancements, since base_url needs to be set on Azure OpenAI Client
azure_client_params = select_azure_base_url_or_endpoint(
azure_client_params
)
cache_key = f"{model_id}_async_client"
_client = openai.AsyncAzureOpenAI( # type: ignore
**azure_client_params,
timeout=timeout,
max_retries=max_retries,
http_client=httpx.AsyncClient(
transport=AsyncCustomHTTPTransport(),
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
mounts=async_proxy_mounts,
), # 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
**azure_client_params,
timeout=timeout,
max_retries=max_retries,
http_client=httpx.Client(
transport=CustomHTTPTransport(),
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
mounts=sync_proxy_mounts,
), # 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
**azure_client_params,
timeout=stream_timeout,
max_retries=max_retries,
http_client=httpx.AsyncClient(
transport=AsyncCustomHTTPTransport(),
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
mounts=async_proxy_mounts,
),
)
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
**azure_client_params,
timeout=stream_timeout,
max_retries=max_retries,
http_client=httpx.Client(
transport=CustomHTTPTransport(),
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
mounts=sync_proxy_mounts,
),
)
self.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
else:
_api_key = api_key
if _api_key is not None and isinstance(_api_key, str):
# only show first 5 chars of api_key
_api_key = _api_key[:8] + "*" * 15
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,
organization=organization,
http_client=httpx.AsyncClient(
transport=AsyncCustomHTTPTransport(),
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
mounts=async_proxy_mounts,
), # 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,
organization=organization,
http_client=httpx.Client(
transport=CustomHTTPTransport(),
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
mounts=sync_proxy_mounts,
), # 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,
organization=organization,
http_client=httpx.AsyncClient(
transport=AsyncCustomHTTPTransport(),
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
mounts=async_proxy_mounts,
), # 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,
organization=organization,
http_client=httpx.Client(
transport=CustomHTTPTransport(),
limits=httpx.Limits(
max_connections=1000, max_keepalive_connections=100
),
mounts=sync_proxy_mounts,
), # type: ignore
)
self.cache.set_cache(
key=cache_key,
value=_client,
ttl=client_ttl,
local_only=True,
) # cache for 1 hr
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 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
import os
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] = litellm.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
deployment = Deployment(
**model,
model_name=_model_name,
litellm_params=_litellm_params,
model_info=_model_info,
)
deployment = self._add_deployment(deployment=deployment)
model = deployment.to_json(exclude_none=True)
self.model_list.append(model)
verbose_router_logger.debug(f"\nInitialized Model List {self.model_list}")
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 == "*":
self.default_deployment = deployment.to_json(exclude_none=True)
# Azure GPT-Vision Enhancements, users can pass os.environ/
data_sources = deployment.litellm_params.get("dataSources", [])
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
self.set_client(model=deployment.to_json(exclude_none=True))
return deployment
def add_deployment(self, deployment: Deployment):
# check if deployment already exists
if deployment.model_info.id in self.get_model_ids():
return
# 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
def get_deployment(self, model_id: str):
for model in self.model_list:
if "model_info" in model and "id" in model["model_info"]:
if model_id == model["model_info"]["id"]:
return model
return None
def get_model_ids(self):
ids = []
for model in self.model_list:
if "model_info" in model and "id" in model["model_info"]:
id = model["model_info"]["id"]
ids.append(id)
return ids
def get_model_names(self):
return self.model_names
def get_model_list(self):
return self.model_list
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 == "rpm_client":
cache_key = "{}_rpm_client".format(model_id)
client = self.cache.get_cache(key=cache_key, local_only=True)
return client
elif 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 _pre_call_checks(
self,
model: str,
healthy_deployments: List,
messages: List[Dict[str, str]],
):
"""
Filter out model in model group, if:
- model context window < message length
- filter models above rpm limits
- [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:
return _returned_deployments
_context_window_error = False
_rate_limit_error = False
## 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) 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 = base_model or deployment.get("litellm_params", {}).get(
"model", None
)
model_info = litellm.get_model_info(model=model)
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
continue
except Exception as e:
verbose_router_logger.debug("An error occurs - {}".format(str(e)))
## RPM CHECK ##
_litellm_params = deployment.get("litellm_params", {})
model_id = deployment.get("model_info", {}).get("id", "")
### get local router cache ###
current_request_cache_local = (
self.cache.get_cache(key=model_id, local_only=True) or 0
)
### get usage based cache ###
if isinstance(model_group_cache, dict):
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
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 == True: # allow generic fallback logic to take place
raise ValueError(
f"No deployments available for selected model, passed model={model}"
)
elif _context_window_error == True:
raise litellm.ContextWindowExceededError(
message="Context Window exceeded for given call",
model=model,
llm_provider="",
response=httpx.Response(
status_code=400,
request=httpx.Request("GET", "https://example.com"),
),
)
if len(invalid_model_indices) > 0:
for idx in reversed(invalid_model_indices):
_returned_deployments.pop(idx)
return _returned_deployments
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,
):
"""
Common checks for 'get_available_deployment' across sync + async call.
If 'healthy_deployments' returned is None, this means the user chose a specific deployment
"""
# check if aliases set on litellm model alias map
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, None
raise ValueError(
f"LiteLLM Router: Trying to call specific deployment, but Model:{model} does not exist in Model List: {self.model_list}"
)
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]
if model not in self.model_names and self.default_deployment is not None:
updated_deployment = copy.deepcopy(
self.default_deployment
) # self.default_deployment
updated_deployment["litellm_params"]["model"] = model
return updated_deployment, None
## 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}"
)
verbose_router_logger.debug(
f"healthy deployments: length {len(healthy_deployments)} {healthy_deployments}"
)
if len(healthy_deployments) == 0:
raise ValueError(f"No healthy deployment available, passed model={model}")
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,
):
"""
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"
): # 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,
)
model, healthy_deployments = self._common_checks_available_deployment(
model=model,
messages=messages,
input=input,
specific_deployment=specific_deployment,
)
if healthy_deployments is None:
return model
# 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 = await self._async_get_cooldown_deployments()
verbose_router_logger.debug(
f"async 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)
# 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
)
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,
messages=messages,
input=input,
)
if deployment is None:
verbose_router_logger.info(
f"get_available_deployment for model: {model}, No deployment available"
)
raise ValueError(
f"No deployments available for selected model, passed model={model}"
)
verbose_router_logger.info(
f"get_available_deployment for model: {model}, Selected deployment: {self.print_deployment(deployment)} for model: {model}"
)
return deployment
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
model, healthy_deployments = self._common_checks_available_deployment(
model=model,
messages=messages,
input=input,
specific_deployment=specific_deployment,
)
if healthy_deployments is None:
return model
# 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)
# 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
)
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]
verbose_router_logger.info(
f"get_available_deployment for model: {model}, Selected deployment: {self.print_deployment(deployment) or deployment[0]} for model: {model}"
)
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]
verbose_router_logger.info(
f"get_available_deployment for model: {model}, Selected deployment: {self.print_deployment(deployment) or deployment[0]} for model: {model}"
)
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,
)
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,
messages=messages,
input=input,
)
if deployment is None:
verbose_router_logger.info(
f"get_available_deployment for model: {model}, No deployment available"
)
raise ValueError(
f"No deployments available for selected model, passed model={model}"
)
verbose_router_logger.info(
f"get_available_deployment for model: {model}, Selected deployment: {self.print_deployment(deployment)} for model: {model}"
)
return deployment
def _track_deployment_metrics(self, deployment, response=None):
try:
litellm_params = deployment["litellm_params"]
api_base = litellm_params.get("api_base", "")
model = litellm_params.get("model", "")
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) # update in-memory cache for tracking
if model_id in self.deployment_stats:
# only update num_requests
self.deployment_stats[model_id]["num_requests"] += 1
else:
self.deployment_stats[model_id] = {
"api_base": api_base,
"model": model,
"num_requests": 1,
}
else:
# check response_ms and update num_successes
response_ms = response.get("_response_ms", 0)
if model_id is not None:
if model_id in self.deployment_stats:
# check if avg_latency exists
if "avg_latency" in self.deployment_stats[model_id]:
# update avg_latency
self.deployment_stats[model_id]["avg_latency"] = (
self.deployment_stats[model_id]["avg_latency"]
+ response_ms
) / self.deployment_stats[model_id]["num_successes"]
else:
self.deployment_stats[model_id]["avg_latency"] = response_ms
# check if num_successes exists
if "num_successes" in self.deployment_stats[model_id]:
self.deployment_stats[model_id]["num_successes"] += 1
else:
self.deployment_stats[model_id]["num_successes"] = 1
else:
self.deployment_stats[model_id] = {
"api_base": api_base,
"model": model,
"num_successes": 1,
"avg_latency": response_ms,
}
if self.set_verbose == True and self.debug_level == "DEBUG":
from pprint import pformat
# Assuming self.deployment_stats is your dictionary
formatted_stats = pformat(self.deployment_stats)
# Assuming verbose_router_logger is your logger
verbose_router_logger.info(
"self.deployment_stats: \n%s", formatted_stats
)
except Exception as e:
verbose_router_logger.error(f"Error in _track_deployment_metrics: {str(e)}")
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()
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