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litellm/litellm/router.py
T
Brian Caswell 920ef665a3 inspect BadRequestError after all other policy types (#19878) 
As indicated by https://docs.litellm.ai/docs/exception_mapping,
BadRequestError is used as the base type for multiple exceptions.  As
such, it should be tested last in handling retry policies.

This updates the integration test that validates retry policies work as
expected.

Fixes #19876
2026-01-27 18:15:04 -08:00

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# +-----------------------------------------------+
# | |
# | Give Feedback / Get Help |
# | https://github.com/BerriAI/litellm/issues/new |
# | |
# +-----------------------------------------------+
#
# Thank you ! We ❤️ you! - Krrish & Ishaan
import asyncio
import copy
import enum
import hashlib
import inspect
import json
import logging
import threading
import time
import traceback
from collections import defaultdict
from functools import lru_cache
from typing import (
TYPE_CHECKING,
Any,
AsyncGenerator,
Callable,
Dict,
List,
Literal,
Optional,
Tuple,
Union,
cast,
)
import httpx
import openai
from openai import AsyncOpenAI
from pydantic import BaseModel
from typing_extensions import overload
import litellm
import litellm.litellm_core_utils
import litellm.litellm_core_utils.exception_mapping_utils
from litellm import get_secret_str
from litellm._logging import verbose_router_logger
from litellm._uuid import uuid
from litellm.caching.caching import (
DualCache,
InMemoryCache,
RedisCache,
RedisClusterCache,
)
from litellm.constants import DEFAULT_MAX_LRU_CACHE_SIZE
from litellm.integrations.custom_logger import CustomLogger
from litellm.litellm_core_utils.asyncify import run_async_function
from litellm.litellm_core_utils.core_helpers import (
_get_parent_otel_span_from_kwargs,
get_metadata_variable_name_from_kwargs,
)
from litellm.litellm_core_utils.thread_pool_executor import executor
from litellm.litellm_core_utils.coroutine_checker import coroutine_checker
from litellm.litellm_core_utils.credential_accessor import CredentialAccessor
from litellm.litellm_core_utils.dd_tracing import tracer
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging
from litellm.litellm_core_utils.sensitive_data_masker import SensitiveDataMasker
from litellm.llms.openai_like.json_loader import JSONProviderRegistry
from litellm.router_strategy.budget_limiter import RouterBudgetLimiting
from litellm.router_strategy.least_busy import LeastBusyLoggingHandler
from litellm.router_strategy.lowest_cost import LowestCostLoggingHandler
from litellm.router_strategy.lowest_latency import LowestLatencyLoggingHandler
from litellm.router_strategy.lowest_tpm_rpm import LowestTPMLoggingHandler
from litellm.router_strategy.lowest_tpm_rpm_v2 import LowestTPMLoggingHandler_v2
from litellm.router_strategy.simple_shuffle import simple_shuffle
from litellm.router_strategy.tag_based_routing import get_deployments_for_tag
from litellm.router_utils.add_retry_fallback_headers import (
add_fallback_headers_to_response,
add_retry_headers_to_response,
)
from litellm.router_utils.batch_utils import (
_get_router_metadata_variable_name,
replace_model_in_jsonl,
should_replace_model_in_jsonl,
)
from litellm.router_utils.client_initalization_utils import InitalizeCachedClient
from litellm.router_utils.clientside_credential_handler import (
get_dynamic_litellm_params,
is_clientside_credential,
)
from litellm.router_utils.common_utils import (
filter_team_based_models,
filter_web_search_deployments,
)
from litellm.router_utils.cooldown_cache import CooldownCache
from litellm.router_utils.cooldown_handlers import (
DEFAULT_COOLDOWN_TIME_SECONDS,
_async_get_cooldown_deployments,
_async_get_cooldown_deployments_with_debug_info,
_get_cooldown_deployments,
_set_cooldown_deployments,
)
from litellm.router_utils.fallback_event_handlers import (
_check_non_standard_fallback_format,
get_fallback_model_group,
run_async_fallback,
)
from litellm.router_utils.get_retry_from_policy import (
get_num_retries_from_retry_policy as _get_num_retries_from_retry_policy,
)
from litellm.router_utils.handle_error import (
async_raise_no_deployment_exception,
send_llm_exception_alert,
)
from litellm.router_utils.pre_call_checks.prompt_caching_deployment_check import (
PromptCachingDeploymentCheck,
)
from litellm.router_utils.pre_call_checks.responses_api_deployment_check import (
ResponsesApiDeploymentCheck,
)
from litellm.router_utils.router_callbacks.track_deployment_metrics import (
increment_deployment_failures_for_current_minute,
increment_deployment_successes_for_current_minute,
)
from litellm.scheduler import FlowItem, Scheduler
from litellm.types.llms.openai import (
AllMessageValues,
FileTypes,
OpenAIFileObject,
OpenAIFilesPurpose,
)
from litellm.types.router import (
CONFIGURABLE_CLIENTSIDE_AUTH_PARAMS,
VALID_LITELLM_ENVIRONMENTS,
AlertingConfig,
AllowedFailsPolicy,
AssistantsTypedDict,
CredentialLiteLLMParams,
CustomRoutingStrategyBase,
Deployment,
DeploymentTypedDict,
GuardrailTypedDict,
LiteLLM_Params,
MockRouterTestingParams,
ModelGroupInfo,
OptionalPreCallChecks,
RetryPolicy,
RouterCacheEnum,
RouterGeneralSettings,
RouterModelGroupAliasItem,
RouterRateLimitError,
RouterRateLimitErrorBasic,
RoutingStrategy,
SearchToolTypedDict,
)
from litellm.types.services import ServiceTypes
from litellm.types.utils import (
CustomPricingLiteLLMParams,
GenericBudgetConfigType,
LiteLLMBatch,
)
from litellm.types.utils import ModelInfo
from litellm.types.utils import ModelInfo as ModelMapInfo
from litellm.types.utils import (
ModelResponseStream,
StandardLoggingPayload,
Usage,
)
from litellm.utils import (
CustomStreamWrapper,
EmbeddingResponse,
ModelResponse,
Rules,
function_setup,
get_llm_provider,
get_non_default_completion_params,
get_secret,
get_utc_datetime,
is_region_allowed,
)
from .router_utils.pattern_match_deployments import PatternMatchRouter
if TYPE_CHECKING:
from opentelemetry.trace import Span as _Span
from litellm.router_strategy.auto_router.auto_router import (
AutoRouter,
PreRoutingHookResponse,
)
Span = Union[_Span, Any]
else:
Span = Any
AutoRouter = Any
PreRoutingHookResponse = Any
class RoutingArgs(enum.Enum):
ttl = 60 # 1min (RPM/TPM expire key)
class Router:
model_names: set = set()
cache_responses: Optional[bool] = False
default_cache_time_seconds: int = 1 * 60 * 60 # 1 hour
tenacity = None
leastbusy_logger: Optional[LeastBusyLoggingHandler] = None
lowesttpm_logger: Optional[LowestTPMLoggingHandler] = None
optional_callbacks: Optional[List[Union[CustomLogger, Callable, str]]] = None
def __init__( # noqa: PLR0915
self,
model_list: Optional[
Union[List[DeploymentTypedDict], List[Dict[str, Any]]]
] = None,
## ASSISTANTS API ##
assistants_config: Optional[AssistantsTypedDict] = None,
## SEARCH API ##
search_tools: Optional[List[SearchToolTypedDict]] = None,
## GUARDRAIL API ##
guardrail_list: Optional[List[GuardrailTypedDict]] = None,
## CACHING ##
redis_url: Optional[str] = None,
redis_host: Optional[str] = None,
redis_port: Optional[int] = None,
redis_password: Optional[str] = None,
cache_responses: Optional[bool] = False,
cache_kwargs: dict = {}, # additional kwargs to pass to RedisCache (see caching.py)
caching_groups: Optional[
List[tuple]
] = None, # if you want to cache across model groups
client_ttl: int = 3600, # ttl for cached clients - will re-initialize after this time in seconds
## SCHEDULER ##
polling_interval: Optional[float] = None,
default_priority: Optional[int] = None,
## RELIABILITY ##
num_retries: Optional[int] = None,
max_fallbacks: Optional[
int
] = None, # max fallbacks to try before exiting the call. Defaults to 5.
timeout: Optional[float] = None,
stream_timeout: Optional[float] = None,
default_litellm_params: Optional[
dict
] = None, # default params for Router.chat.completion.create
default_max_parallel_requests: Optional[int] = None,
set_verbose: bool = False,
debug_level: Literal["DEBUG", "INFO"] = "INFO",
default_fallbacks: Optional[
List[str]
] = None, # generic fallbacks, works across all deployments
fallbacks: List = [],
context_window_fallbacks: List = [],
content_policy_fallbacks: List = [],
model_group_alias: Optional[
Dict[str, Union[str, RouterModelGroupAliasItem]]
] = {},
enable_pre_call_checks: bool = False,
enable_tag_filtering: bool = False,
tag_filtering_match_any: bool = True,
retry_after: int = 0, # min time to wait before retrying a failed request
retry_policy: Optional[
Union[RetryPolicy, dict]
] = None, # set custom retries for different exceptions
model_group_retry_policy: Dict[
str, RetryPolicy
] = {}, # set custom retry policies based on model group
allowed_fails: Optional[
int
] = None, # Number of times a deployment can failbefore being added to cooldown
allowed_fails_policy: Optional[
AllowedFailsPolicy
] = None, # set custom allowed fails policy
cooldown_time: Optional[
float
] = None, # (seconds) time to cooldown a deployment after failure
disable_cooldowns: Optional[bool] = None,
routing_strategy: Literal[
"simple-shuffle",
"least-busy",
"usage-based-routing",
"latency-based-routing",
"cost-based-routing",
"usage-based-routing-v2",
] = "simple-shuffle",
optional_pre_call_checks: Optional[OptionalPreCallChecks] = None,
routing_strategy_args: dict = {}, # just for latency-based
provider_budget_config: Optional[GenericBudgetConfigType] = None,
alerting_config: Optional[AlertingConfig] = None,
router_general_settings: Optional[
RouterGeneralSettings
] = RouterGeneralSettings(),
ignore_invalid_deployments: bool = False,
) -> None:
"""
Initialize the Router class with the given parameters for caching, reliability, and routing strategy.
Args:
model_list (Optional[list]): List of models to be used. Defaults to None.
redis_url (Optional[str]): URL of the Redis server. Defaults to None.
redis_host (Optional[str]): Hostname of the Redis server. Defaults to None.
redis_port (Optional[int]): Port of the Redis server. Defaults to None.
redis_password (Optional[str]): Password of the Redis server. Defaults to None.
cache_responses (Optional[bool]): Flag to enable caching of responses. Defaults to False.
cache_kwargs (dict): Additional kwargs to pass to RedisCache. Defaults to {}.
caching_groups (Optional[List[tuple]]): List of model groups for caching across model groups. Defaults to None.
client_ttl (int): Time-to-live for cached clients in seconds. Defaults to 3600.
polling_interval: (Optional[float]): frequency of polling queue. Only for '.scheduler_acompletion()'. Default is 3ms.
default_priority: (Optional[int]): the default priority for a request. Only for '.scheduler_acompletion()'. Default is None.
num_retries (Optional[int]): Number of retries for failed requests. Defaults to 2.
timeout (Optional[float]): Timeout for requests. Defaults to None.
default_litellm_params (dict): Default parameters for Router.chat.completion.create. Defaults to {}.
set_verbose (bool): Flag to set verbose mode. Defaults to False.
debug_level (Literal["DEBUG", "INFO"]): Debug level for logging. Defaults to "INFO".
fallbacks (List): List of fallback options. Defaults to [].
context_window_fallbacks (List): List of context window fallback options. Defaults to [].
enable_pre_call_checks (boolean): Filter out deployments which are outside context window limits for a given prompt
model_group_alias (Optional[dict]): Alias for model groups. Defaults to {}.
retry_after (int): Minimum time to wait before retrying a failed request. Defaults to 0.
allowed_fails (Optional[int]): Number of allowed fails before adding to cooldown. Defaults to None.
cooldown_time (float): Time to cooldown a deployment after failure in seconds. Defaults to 1.
routing_strategy (Literal["simple-shuffle", "least-busy", "usage-based-routing", "latency-based-routing", "cost-based-routing"]): Routing strategy. Defaults to "simple-shuffle".
routing_strategy_args (dict): Additional args for latency-based routing. Defaults to {}.
alerting_config (AlertingConfig): Slack alerting configuration. Defaults to None.
provider_budget_config (ProviderBudgetConfig): Provider budget configuration. Use this to set llm_provider budget limits. example $100/day to OpenAI, $100/day to Azure, etc. Defaults to None.
ignore_invalid_deployments (bool): Ignores invalid deployments, and continues with other deployments. Default is to raise an error.
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"}])
```
"""
self.set_verbose = set_verbose
self.ignore_invalid_deployments = ignore_invalid_deployments
self.debug_level = debug_level
self.enable_pre_call_checks = enable_pre_call_checks
self.enable_tag_filtering = enable_tag_filtering
self.tag_filtering_match_any = tag_filtering_match_any
from litellm._service_logger import ServiceLogging
self.service_logger_obj: ServiceLogging = ServiceLogging()
litellm.suppress_debug_info = True # prevents 'Give Feedback/Get help' message from being emitted on Router - Relevant Issue: https://github.com/BerriAI/litellm/issues/5942
if self.set_verbose is True:
if debug_level == "INFO":
verbose_router_logger.setLevel(logging.INFO)
elif debug_level == "DEBUG":
verbose_router_logger.setLevel(logging.DEBUG)
self.router_general_settings: RouterGeneralSettings = (
router_general_settings or RouterGeneralSettings()
)
self.assistants_config = assistants_config
self.search_tools = search_tools or []
self.guardrail_list = guardrail_list or []
self.deployment_names: List = (
[]
) # names of models under litellm_params. ex. azure/chatgpt-v-2
self.deployment_latency_map = {}
### CACHING ###
cache_type: Literal[
"local", "redis", "redis-semantic", "s3", "disk"
] = "local" # default to an in-memory cache
redis_cache = None
cache_config: Dict[str, Any] = {}
self.client_ttl = client_ttl
if redis_url is not None or (redis_host is not None and redis_port is not None):
cache_type = "redis"
if redis_url is not None:
cache_config["url"] = redis_url
if redis_host is not None:
cache_config["host"] = redis_host
if redis_port is not None:
cache_config["port"] = str(redis_port) # type: ignore
if redis_password is not None:
cache_config["password"] = redis_password
# Add additional key-value pairs from cache_kwargs
cache_config.update(cache_kwargs)
redis_cache = self._create_redis_cache(cache_config)
if cache_responses:
if litellm.cache is None:
# the cache can be initialized on the proxy server. We should not overwrite it
litellm.cache = litellm.Cache(type=cache_type, **cache_config) # type: ignore
self.cache_responses = cache_responses
self.cache = DualCache(
redis_cache=redis_cache, in_memory_cache=InMemoryCache()
) # use a dual cache (Redis+In-Memory) for tracking cooldowns, usage, etc.
### SCHEDULER ###
self.scheduler = Scheduler(
polling_interval=polling_interval, redis_cache=redis_cache
)
self.default_priority = default_priority
self.default_deployment = None # use this to track the users default deployment, when they want to use model = *
self.default_max_parallel_requests = default_max_parallel_requests
self.provider_default_deployment_ids: List[str] = []
self.pattern_router = PatternMatchRouter()
self.team_pattern_routers: Dict[
str, PatternMatchRouter
] = {} # {"TEAM_ID": PatternMatchRouter}
self.auto_routers: Dict[str, "AutoRouter"] = {}
# Initialize model_group_alias early since it's used in set_model_list
self.model_group_alias: Dict[str, Union[str, RouterModelGroupAliasItem]] = (
model_group_alias or {}
) # dict to store aliases for router, ex. {"gpt-4": "gpt-3.5-turbo"}, all requests with gpt-4 -> get routed to gpt-3.5-turbo group
# Initialize model ID to deployment index mapping for O(1) lookups
self.model_id_to_deployment_index_map: Dict[str, int] = {}
# Initialize model name to deployment indices mapping for O(1) lookups
# Maps model_name -> list of indices in model_list
self.model_name_to_deployment_indices: Dict[str, List[int]] = {}
if model_list is not None:
# set_model_list will build indices automatically
self.set_model_list(model_list)
self.healthy_deployments: List = self.model_list # type: ignore
for m in model_list:
if "model" in m["litellm_params"]:
self.deployment_latency_map[m["litellm_params"]["model"]] = 0
else:
self.model_list: List = (
[]
) # initialize an empty list - to allow _add_deployment and delete_deployment to work
if allowed_fails is not None:
self.allowed_fails = allowed_fails
else:
self.allowed_fails = litellm.allowed_fails
self.cooldown_time = cooldown_time or DEFAULT_COOLDOWN_TIME_SECONDS
self.cooldown_cache = CooldownCache(
cache=self.cache, default_cooldown_time=self.cooldown_time
)
self.disable_cooldowns = disable_cooldowns
self.failed_calls = (
InMemoryCache()
) # cache to track failed call per deployment, if num failed calls within 1 minute > allowed fails, then add it to cooldown
if num_retries is not None:
self.num_retries = num_retries
elif litellm.num_retries is not None:
self.num_retries = litellm.num_retries
else:
self.num_retries = openai.DEFAULT_MAX_RETRIES
if max_fallbacks is not None:
self.max_fallbacks = max_fallbacks
elif litellm.max_fallbacks is not None:
self.max_fallbacks = litellm.max_fallbacks
else:
self.max_fallbacks = litellm.ROUTER_MAX_FALLBACKS
self.timeout = timeout or litellm.request_timeout
self.stream_timeout = stream_timeout
self.retry_after = retry_after
self.routing_strategy = routing_strategy
## SETTING FALLBACKS ##
### validate if it's set + in correct format
_fallbacks = fallbacks or litellm.fallbacks
self.validate_fallbacks(fallback_param=_fallbacks)
### set fallbacks
self.fallbacks = _fallbacks
if default_fallbacks is not None or litellm.default_fallbacks is not None:
_fallbacks = default_fallbacks or litellm.default_fallbacks
if self.fallbacks is not None:
self.fallbacks.append({"*": _fallbacks})
else:
self.fallbacks = [{"*": _fallbacks}]
self.context_window_fallbacks = (
context_window_fallbacks or litellm.context_window_fallbacks
)
_content_policy_fallbacks = (
content_policy_fallbacks or litellm.content_policy_fallbacks
)
self.validate_fallbacks(fallback_param=_content_policy_fallbacks)
self.content_policy_fallbacks = _content_policy_fallbacks
self.total_calls: defaultdict = defaultdict(
int
) # dict to store total calls made to each model
self.fail_calls: defaultdict = defaultdict(
int
) # dict to store fail_calls made to each model
self.success_calls: defaultdict = defaultdict(
int
) # dict to store success_calls made to each model
self.previous_models: List = (
[]
) # list to store failed calls (passed in as metadata to next call)
# make Router.chat.completions.create compatible for openai.chat.completions.create
default_litellm_params = default_litellm_params or {}
self.chat = litellm.Chat(params=default_litellm_params, router_obj=self)
# default litellm args
self.default_litellm_params = default_litellm_params
self.default_litellm_params.setdefault("timeout", timeout)
self.default_litellm_params.setdefault("max_retries", 0)
self.default_litellm_params.setdefault("metadata", {}).update(
{"caching_groups": caching_groups}
)
self.deployment_stats: dict = {} # used for debugging load balancing
"""
deployment_stats = {
"122999-2828282-277:
{
"model": "gpt-3",
"api_base": "http://localhost:4000",
"num_requests": 20,
"avg_latency": 0.001,
"num_failures": 0,
"num_successes": 20
}
}
"""
### ROUTING SETUP ###
self.routing_strategy_init(
routing_strategy=routing_strategy,
routing_strategy_args=routing_strategy_args,
)
self.access_groups = None
## USAGE TRACKING ##
if isinstance(litellm._async_success_callback, list):
litellm.logging_callback_manager.add_litellm_async_success_callback(
self.deployment_callback_on_success
)
else:
litellm.logging_callback_manager.add_litellm_async_success_callback(
self.deployment_callback_on_success
)
if isinstance(litellm.success_callback, list):
litellm.logging_callback_manager.add_litellm_success_callback(
self.sync_deployment_callback_on_success
)
else:
litellm.success_callback = [self.sync_deployment_callback_on_success]
if isinstance(litellm._async_failure_callback, list):
litellm.logging_callback_manager.add_litellm_async_failure_callback(
self.async_deployment_callback_on_failure
)
else:
litellm._async_failure_callback = [
self.async_deployment_callback_on_failure
]
## COOLDOWNS ##
if isinstance(litellm.failure_callback, list):
litellm.logging_callback_manager.add_litellm_failure_callback(
self.deployment_callback_on_failure
)
else:
litellm.failure_callback = [self.deployment_callback_on_failure]
self.routing_strategy_args = routing_strategy_args
self.provider_budget_config = provider_budget_config
self.router_budget_logger: Optional[RouterBudgetLimiting] = None
if RouterBudgetLimiting.should_init_router_budget_limiter(
model_list=model_list, provider_budget_config=self.provider_budget_config
):
if optional_pre_call_checks is not None:
optional_pre_call_checks.append("router_budget_limiting")
else:
optional_pre_call_checks = ["router_budget_limiting"]
self.retry_policy: Optional[RetryPolicy] = None
if retry_policy is not None:
if isinstance(retry_policy, dict):
self.retry_policy = RetryPolicy(**retry_policy)
elif isinstance(retry_policy, RetryPolicy):
self.retry_policy = retry_policy
verbose_router_logger.info(
"\033[32mRouter Custom Retry Policy Set:\n{}\033[0m".format(
self.retry_policy.model_dump(exclude_none=True)
)
)
self.model_group_retry_policy: Optional[
Dict[str, RetryPolicy]
] = model_group_retry_policy
self.allowed_fails_policy: Optional[AllowedFailsPolicy] = None
if allowed_fails_policy is not None:
if isinstance(allowed_fails_policy, dict):
self.allowed_fails_policy = AllowedFailsPolicy(**allowed_fails_policy)
elif isinstance(allowed_fails_policy, AllowedFailsPolicy):
self.allowed_fails_policy = allowed_fails_policy
verbose_router_logger.info(
"\033[32mRouter Custom Allowed Fails Policy Set:\n{}\033[0m".format(
self.allowed_fails_policy.model_dump(exclude_none=True)
)
)
self.alerting_config: Optional[AlertingConfig] = alerting_config
if optional_pre_call_checks is not None:
self.add_optional_pre_call_checks(optional_pre_call_checks)
if self.alerting_config is not None:
self._initialize_alerting()
self.initialize_assistants_endpoint()
self.initialize_router_endpoints()
self.apply_default_settings()
@staticmethod
def get_valid_args() -> List[str]:
"""
Returns a list of valid arguments for the Router.__init__ method.
"""
arg_spec = inspect.getfullargspec(Router.__init__)
valid_args = arg_spec.args + arg_spec.kwonlyargs
if "self" in valid_args:
valid_args.remove("self")
return valid_args
def apply_default_settings(self):
"""
Apply the default settings to the router.
"""
default_pre_call_checks: OptionalPreCallChecks = []
self.add_optional_pre_call_checks(default_pre_call_checks)
return None
def discard(self):
"""
Pseudo-destructor to be invoked to clean up global data structures when router is no longer used.
For now, unhook router's callbacks from all lists
"""
litellm.logging_callback_manager.remove_callback_from_list_by_object(
litellm._async_success_callback, self
)
litellm.logging_callback_manager.remove_callback_from_list_by_object(
litellm.success_callback, self
)
litellm.logging_callback_manager.remove_callback_from_list_by_object(
litellm._async_failure_callback, self
)
litellm.logging_callback_manager.remove_callback_from_list_by_object(
litellm.failure_callback, self
)
litellm.logging_callback_manager.remove_callback_from_list_by_object(
litellm.input_callback, self
)
litellm.logging_callback_manager.remove_callback_from_list_by_object(
litellm.service_callback, self
)
litellm.logging_callback_manager.remove_callback_from_list_by_object(
litellm.callbacks, self
)
# Remove ForwardClientSideHeadersByModelGroup if it exists
if self.optional_callbacks is not None:
for callback in self.optional_callbacks:
litellm.logging_callback_manager.remove_callback_from_list_by_object(
litellm.callbacks, callback, require_self=False
)
@staticmethod
def _create_redis_cache(
cache_config: Dict[str, Any],
) -> Union[RedisCache, RedisClusterCache]:
"""
Initializes either a RedisCache or RedisClusterCache based on the cache_config.
"""
if cache_config.get("startup_nodes"):
return RedisClusterCache(**cache_config)
else:
return RedisCache(**cache_config)
def _update_redis_cache(self, cache: RedisCache):
"""
Update the redis cache for the router, if none set.
Allows proxy user to just do
```yaml
litellm_settings:
cache: true
```
and caching to just work.
"""
if self.cache.redis_cache is None:
self.cache.redis_cache = cache
def routing_strategy_init(
self, routing_strategy: Union[RoutingStrategy, str], routing_strategy_args: dict
):
verbose_router_logger.info(f"Routing strategy: {routing_strategy}")
# Validate routing_strategy value to fail fast with helpful error
# See: https://github.com/BerriAI/litellm/issues/11330
# Derive valid strategies from RoutingStrategy enum + "simple-shuffle" (default, not in enum)
valid_strategy_strings = ["simple-shuffle"] + [s.value for s in RoutingStrategy]
if routing_strategy is not None:
is_valid_string = (
isinstance(routing_strategy, str)
and routing_strategy in valid_strategy_strings
)
is_valid_enum = isinstance(routing_strategy, RoutingStrategy)
if not is_valid_string and not is_valid_enum:
raise ValueError(
f"Invalid routing_strategy: '{routing_strategy}'. "
f"Valid options: {valid_strategy_strings}. "
f"Check 'router_settings.routing_strategy' in your config.yaml "
f"or the 'routing_strategy' parameter if using the Router SDK directly."
)
if (
routing_strategy == RoutingStrategy.LEAST_BUSY.value
or routing_strategy == RoutingStrategy.LEAST_BUSY
):
self.leastbusy_logger = LeastBusyLoggingHandler(router_cache=self.cache)
## add callback
if isinstance(litellm.input_callback, list):
litellm.input_callback.append(self.leastbusy_logger) # type: ignore
else:
litellm.input_callback = [self.leastbusy_logger] # type: ignore
if isinstance(litellm.callbacks, list):
litellm.logging_callback_manager.add_litellm_callback(self.leastbusy_logger) # type: ignore
elif (
routing_strategy == RoutingStrategy.USAGE_BASED_ROUTING.value
or routing_strategy == RoutingStrategy.USAGE_BASED_ROUTING
):
self.lowesttpm_logger = LowestTPMLoggingHandler(
router_cache=self.cache,
routing_args=routing_strategy_args,
)
if isinstance(litellm.callbacks, list):
litellm.logging_callback_manager.add_litellm_callback(self.lowesttpm_logger) # type: ignore
elif (
routing_strategy == RoutingStrategy.USAGE_BASED_ROUTING_V2.value
or routing_strategy == RoutingStrategy.USAGE_BASED_ROUTING_V2
):
self.lowesttpm_logger_v2 = LowestTPMLoggingHandler_v2(
router_cache=self.cache,
routing_args=routing_strategy_args,
)
if isinstance(litellm.callbacks, list):
litellm.logging_callback_manager.add_litellm_callback(self.lowesttpm_logger_v2) # type: ignore
elif (
routing_strategy == RoutingStrategy.LATENCY_BASED.value
or routing_strategy == RoutingStrategy.LATENCY_BASED
):
self.lowestlatency_logger = LowestLatencyLoggingHandler(
router_cache=self.cache,
routing_args=routing_strategy_args,
)
if isinstance(litellm.callbacks, list):
litellm.logging_callback_manager.add_litellm_callback(self.lowestlatency_logger) # type: ignore
elif (
routing_strategy == RoutingStrategy.COST_BASED.value
or routing_strategy == RoutingStrategy.COST_BASED
):
self.lowestcost_logger = LowestCostLoggingHandler(
router_cache=self.cache,
routing_args={},
)
if isinstance(litellm.callbacks, list):
litellm.logging_callback_manager.add_litellm_callback(self.lowestcost_logger) # type: ignore
else:
pass
def initialize_assistants_endpoint(self):
## INITIALIZE PASS THROUGH ASSISTANTS ENDPOINT ##
self.acreate_assistants = self.factory_function(litellm.acreate_assistants)
self.adelete_assistant = self.factory_function(litellm.adelete_assistant)
self.aget_assistants = self.factory_function(litellm.aget_assistants)
self.acreate_thread = self.factory_function(litellm.acreate_thread)
self.aget_thread = self.factory_function(litellm.aget_thread)
self.a_add_message = self.factory_function(litellm.a_add_message)
self.aget_messages = self.factory_function(litellm.aget_messages)
self.arun_thread = self.factory_function(litellm.arun_thread)
def _initialize_core_endpoints(self):
"""Helper to initialize core router endpoints."""
self.amoderation = self.factory_function(
litellm.amoderation, call_type="moderation"
)
self.aanthropic_messages = self.factory_function(
litellm.anthropic_messages, call_type="anthropic_messages"
)
self.anthropic_messages = self.factory_function(
litellm.anthropic_messages, call_type="anthropic_messages"
)
self.agenerate_content = self.factory_function(
litellm.agenerate_content, call_type="agenerate_content"
)
self.aadapter_generate_content = self.factory_function(
litellm.aadapter_generate_content, call_type="aadapter_generate_content"
)
self.aresponses = self.factory_function(
litellm.aresponses, call_type="aresponses"
)
self.afile_delete = self.factory_function(
litellm.afile_delete, call_type="afile_delete"
)
self.afile_content = self.factory_function(
litellm.afile_content, call_type="afile_content"
)
self.responses = self.factory_function(litellm.responses, call_type="responses")
self.aget_responses = self.factory_function(
litellm.aget_responses, call_type="aget_responses"
)
self.acancel_responses = self.factory_function(
litellm.acancel_responses, call_type="acancel_responses"
)
self.acompact_responses = self.factory_function(
litellm.acompact_responses, call_type="acompact_responses"
)
self.adelete_responses = self.factory_function(
litellm.adelete_responses, call_type="adelete_responses"
)
self.alist_input_items = self.factory_function(
litellm.alist_input_items, call_type="alist_input_items"
)
self._arealtime = self.factory_function(
litellm._arealtime, call_type="_arealtime"
)
self.acreate_fine_tuning_job = self.factory_function(
litellm.acreate_fine_tuning_job, call_type="acreate_fine_tuning_job"
)
self.acancel_fine_tuning_job = self.factory_function(
litellm.acancel_fine_tuning_job, call_type="acancel_fine_tuning_job"
)
self.alist_fine_tuning_jobs = self.factory_function(
litellm.alist_fine_tuning_jobs, call_type="alist_fine_tuning_jobs"
)
self.aretrieve_fine_tuning_job = self.factory_function(
litellm.aretrieve_fine_tuning_job, call_type="aretrieve_fine_tuning_job"
)
self.afile_list = self.factory_function(
litellm.afile_list, call_type="alist_files"
)
self.aimage_edit = self.factory_function(
litellm.aimage_edit, call_type="aimage_edit"
)
self.allm_passthrough_route = self.factory_function(
litellm.allm_passthrough_route, call_type="allm_passthrough_route"
)
self.acancel_batch = self.factory_function(
litellm.acancel_batch, call_type="acancel_batch"
)
def _initialize_vector_store_endpoints(self):
"""Initialize vector store endpoints."""
from litellm.vector_stores.main import acreate, asearch, create, search
self.avector_store_search = self.factory_function(
asearch, call_type="avector_store_search"
)
self.avector_store_create = self.factory_function(
acreate, call_type="avector_store_create"
)
self.vector_store_search = self.factory_function(
search, call_type="vector_store_search"
)
self.vector_store_create = self.factory_function(
create, call_type="vector_store_create"
)
def _initialize_vector_store_file_endpoints(self):
"""Initialize vector store file endpoints."""
from litellm.vector_store_files.main import (
acreate as avector_store_file_create_fn,
)
from litellm.vector_store_files.main import (
adelete as avector_store_file_delete_fn,
)
from litellm.vector_store_files.main import alist as avector_store_file_list_fn
from litellm.vector_store_files.main import (
aretrieve as avector_store_file_retrieve_fn,
)
from litellm.vector_store_files.main import (
aretrieve_content as avector_store_file_content_fn,
)
from litellm.vector_store_files.main import (
aupdate as avector_store_file_update_fn,
)
from litellm.vector_store_files.main import (
create as vector_store_file_create_fn,
)
from litellm.vector_store_files.main import (
delete as vector_store_file_delete_fn,
)
from litellm.vector_store_files.main import list as vector_store_file_list_fn
from litellm.vector_store_files.main import (
retrieve as vector_store_file_retrieve_fn,
)
from litellm.vector_store_files.main import (
retrieve_content as vector_store_file_content_fn,
)
from litellm.vector_store_files.main import (
update as vector_store_file_update_fn,
)
self.avector_store_file_create = self.factory_function(
avector_store_file_create_fn, call_type="avector_store_file_create"
)
self.vector_store_file_create = self.factory_function(
vector_store_file_create_fn, call_type="vector_store_file_create"
)
self.avector_store_file_list = self.factory_function(
avector_store_file_list_fn, call_type="avector_store_file_list"
)
self.vector_store_file_list = self.factory_function(
vector_store_file_list_fn, call_type="vector_store_file_list"
)
self.avector_store_file_retrieve = self.factory_function(
avector_store_file_retrieve_fn, call_type="avector_store_file_retrieve"
)
self.vector_store_file_retrieve = self.factory_function(
vector_store_file_retrieve_fn, call_type="vector_store_file_retrieve"
)
self.avector_store_file_content = self.factory_function(
avector_store_file_content_fn, call_type="avector_store_file_content"
)
self.vector_store_file_content = self.factory_function(
vector_store_file_content_fn, call_type="vector_store_file_content"
)
self.avector_store_file_update = self.factory_function(
avector_store_file_update_fn, call_type="avector_store_file_update"
)
self.vector_store_file_update = self.factory_function(
vector_store_file_update_fn, call_type="vector_store_file_update"
)
self.avector_store_file_delete = self.factory_function(
avector_store_file_delete_fn, call_type="avector_store_file_delete"
)
self.vector_store_file_delete = self.factory_function(
vector_store_file_delete_fn, call_type="vector_store_file_delete"
)
def _initialize_google_genai_endpoints(self):
"""Initialize Google GenAI endpoints."""
from litellm.google_genai import (
agenerate_content,
agenerate_content_stream,
generate_content,
generate_content_stream,
)
self.agenerate_content = self.factory_function(
agenerate_content, call_type="agenerate_content"
)
self.generate_content = self.factory_function(
generate_content, call_type="generate_content"
)
self.agenerate_content_stream = self.factory_function(
agenerate_content_stream, call_type="agenerate_content_stream"
)
self.generate_content_stream = self.factory_function(
generate_content_stream, call_type="generate_content_stream"
)
def _initialize_ocr_search_endpoints(self):
"""Initialize OCR and search endpoints."""
from litellm.ocr import aocr, ocr
self.aocr = self.factory_function(aocr, call_type="aocr")
self.ocr = self.factory_function(ocr, call_type="ocr")
from litellm.search import asearch, search
self.asearch = self.factory_function(asearch, call_type="asearch")
self.search = self.factory_function(search, call_type="search")
def _initialize_video_endpoints(self):
"""Initialize video endpoints."""
from litellm.videos import (
avideo_content,
avideo_generation,
avideo_list,
avideo_remix,
avideo_status,
video_content,
video_generation,
video_list,
video_remix,
video_status,
)
self.avideo_generation = self.factory_function(
avideo_generation, call_type="avideo_generation"
)
self.video_generation = self.factory_function(
video_generation, call_type="video_generation"
)
self.avideo_list = self.factory_function(avideo_list, call_type="avideo_list")
self.video_list = self.factory_function(video_list, call_type="video_list")
self.avideo_status = self.factory_function(
avideo_status, call_type="avideo_status"
)
self.video_status = self.factory_function(
video_status, call_type="video_status"
)
self.avideo_content = self.factory_function(
avideo_content, call_type="avideo_content"
)
self.video_content = self.factory_function(
video_content, call_type="video_content"
)
self.avideo_remix = self.factory_function(
avideo_remix, call_type="avideo_remix"
)
self.video_remix = self.factory_function(video_remix, call_type="video_remix")
def _initialize_container_endpoints(self):
"""Initialize container endpoints."""
from litellm.containers import (
acreate_container,
adelete_container,
alist_containers,
aretrieve_container,
create_container,
delete_container,
list_containers,
retrieve_container,
)
from litellm.containers.endpoint_factory import (
_generated_endpoints as container_file_endpoints,
)
self.acreate_container = self.factory_function(
acreate_container, call_type="acreate_container"
)
self.create_container = self.factory_function(
create_container, call_type="create_container"
)
self.alist_containers = self.factory_function(
alist_containers, call_type="alist_containers"
)
self.list_containers = self.factory_function(
list_containers, call_type="list_containers"
)
self.aretrieve_container = self.factory_function(
aretrieve_container, call_type="aretrieve_container"
)
self.retrieve_container = self.factory_function(
retrieve_container, call_type="retrieve_container"
)
self.adelete_container = self.factory_function(
adelete_container, call_type="adelete_container"
)
self.delete_container = self.factory_function(
delete_container, call_type="delete_container"
)
# Auto-register JSON-generated container file endpoints
for name, func in container_file_endpoints.items():
setattr(self, name, self.factory_function(func, call_type=name)) # type: ignore[arg-type]
def _initialize_skills_endpoints(self):
"""Initialize Anthropic Skills API endpoints."""
self.acreate_skill = self.factory_function(
litellm.acreate_skill, call_type="acreate_skill"
)
self.alist_skills = self.factory_function(
litellm.alist_skills, call_type="alist_skills"
)
self.aget_skill = self.factory_function(
litellm.aget_skill, call_type="aget_skill"
)
self.adelete_skill = self.factory_function(
litellm.adelete_skill, call_type="adelete_skill"
)
def _initialize_interactions_endpoints(self):
"""Initialize Google Interactions API endpoints."""
from litellm.interactions import acancel as acancel_interaction
from litellm.interactions import acreate as acreate_interaction
from litellm.interactions import adelete as adelete_interaction
from litellm.interactions import aget as aget_interaction
from litellm.interactions import cancel as cancel_interaction
from litellm.interactions import create as create_interaction
from litellm.interactions import delete as delete_interaction
from litellm.interactions import get as get_interaction
self.acreate_interaction = self.factory_function(
acreate_interaction, call_type="acreate_interaction"
)
self.create_interaction = self.factory_function(
create_interaction, call_type="create_interaction"
)
self.aget_interaction = self.factory_function(
aget_interaction, call_type="aget_interaction"
)
self.get_interaction = self.factory_function(
get_interaction, call_type="get_interaction"
)
self.adelete_interaction = self.factory_function(
adelete_interaction, call_type="adelete_interaction"
)
self.delete_interaction = self.factory_function(
delete_interaction, call_type="delete_interaction"
)
self.acancel_interaction = self.factory_function(
acancel_interaction, call_type="acancel_interaction"
)
self.cancel_interaction = self.factory_function(
cancel_interaction, call_type="cancel_interaction"
)
def _initialize_specialized_endpoints(self):
"""Helper to initialize specialized router endpoints (vector store, OCR, search, video, container, skills, interactions)."""
self._initialize_vector_store_endpoints()
self._initialize_vector_store_file_endpoints()
self._initialize_google_genai_endpoints()
self._initialize_ocr_search_endpoints()
self._initialize_video_endpoints()
self._initialize_container_endpoints()
self._initialize_skills_endpoints()
self._initialize_interactions_endpoints()
def initialize_router_endpoints(self):
self._initialize_core_endpoints()
self._initialize_specialized_endpoints()
def validate_fallbacks(self, fallback_param: Optional[List]):
"""
Validate the fallbacks parameter.
"""
if fallback_param is None:
return
for fallback_dict in fallback_param:
if not isinstance(fallback_dict, dict):
raise ValueError(f"Item '{fallback_dict}' is not a dictionary.")
if len(fallback_dict) != 1:
raise ValueError(
f"Dictionary '{fallback_dict}' must have exactly one key, but has {len(fallback_dict)} keys."
)
def add_optional_pre_call_checks(
self, optional_pre_call_checks: Optional[OptionalPreCallChecks]
):
if optional_pre_call_checks is not None:
for pre_call_check in optional_pre_call_checks:
_callback: Optional[CustomLogger] = None
if pre_call_check == "prompt_caching":
_callback = PromptCachingDeploymentCheck(cache=self.cache)
elif pre_call_check == "router_budget_limiting":
_callback = RouterBudgetLimiting(
dual_cache=self.cache,
provider_budget_config=self.provider_budget_config,
model_list=self.model_list,
)
elif pre_call_check == "responses_api_deployment_check":
_callback = ResponsesApiDeploymentCheck()
if _callback is not None:
if self.optional_callbacks is None:
self.optional_callbacks = []
self.optional_callbacks.append(_callback)
litellm.logging_callback_manager.add_litellm_callback(_callback)
def print_deployment(self, deployment: dict):
"""
returns a copy of the deployment with the api key masked
Only returns 2 characters of the api key and masks the rest with * (10 *).
"""
try:
_deployment_copy = copy.deepcopy(deployment)
litellm_params: dict = _deployment_copy["litellm_params"]
if litellm.redact_user_api_key_info:
masker = SensitiveDataMasker(visible_prefix=2, visible_suffix=0)
_deployment_copy["litellm_params"] = masker.mask_dict(litellm_params)
elif "api_key" in litellm_params:
litellm_params["api_key"] = litellm_params["api_key"][:2] + "*" * 10
return _deployment_copy
except Exception as e:
verbose_router_logger.debug(
f"Error occurred while printing deployment - {str(e)}"
)
raise e
### COMPLETION, EMBEDDING, IMG GENERATION FUNCTIONS
def completion(
self, model: str, messages: List[Dict[str, str]], **kwargs
) -> Union[ModelResponse, CustomStreamWrapper]:
"""
Example usage:
response = router.completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey, how's it going?"}]
"""
try:
verbose_router_logger.debug(f"router.completion(model={model},..)")
kwargs["model"] = model
kwargs["messages"] = messages
kwargs["original_function"] = self._completion
self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
response = self.function_with_fallbacks(**kwargs)
return response
except Exception as e:
raise e
def _completion(
self, model: str, messages: List[Dict[str, str]], **kwargs
) -> Union[ModelResponse, CustomStreamWrapper]:
model_name = None
deployment = 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),
request_kwargs=kwargs,
)
# Check for silent model experiment
# Make a local copy of litellm_params to avoid mutating the Router's state
litellm_params = deployment["litellm_params"].copy()
silent_model = litellm_params.pop("silent_model", None)
if silent_model is not None:
# Mirroring traffic to a secondary model
# Use shared thread pool for background calls
executor.submit(
self._silent_experiment_completion,
silent_model,
messages,
**kwargs,
)
self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
kwargs.pop("silent_model", None) # Ensure it's not in kwargs either
# No copy needed - data is only read and spread into new dict below
data = litellm_params.copy() # Use the local copy of litellm_params
model_name = data["model"]
potential_model_client = self._get_client(
deployment=deployment, kwargs=kwargs
)
# check if provided keys == client keys #
dynamic_api_key = kwargs.get("api_key", None)
if (
dynamic_api_key is not None
and potential_model_client is not None
and dynamic_api_key != potential_model_client.api_key
):
model_client = None
else:
model_client = potential_model_client
### DEPLOYMENT-SPECIFIC PRE-CALL CHECKS ### (e.g. update rpm pre-call. Raise error, if deployment over limit)
## only run if model group given, not model id
if not self.has_model_id(model):
self.routing_strategy_pre_call_checks(deployment=deployment)
input_kwargs = {
**data,
"messages": messages,
"caching": self.cache_responses,
"client": model_client,
**kwargs,
}
response = litellm.completion(**input_kwargs)
verbose_router_logger.info(
f"litellm.completion(model={model_name})\033[32m 200 OK\033[0m"
)
## CHECK CONTENT FILTER ERROR ##
if isinstance(response, ModelResponse):
_should_raise = self._should_raise_content_policy_error(
model=model, response=response, kwargs=kwargs
)
if _should_raise:
raise litellm.ContentPolicyViolationError(
message="Response output was blocked.",
model=model,
llm_provider="",
)
return response
except Exception as e:
verbose_router_logger.info(
f"litellm.completion(model={model_name})\033[31m Exception {str(e)}\033[0m"
)
# Set per-deployment num_retries on exception for retry logic
if deployment is not None:
self._set_deployment_num_retries_on_exception(e, deployment)
raise e
def _get_silent_experiment_kwargs(self, **kwargs) -> dict:
"""
Prepare kwargs for a silent experiment by ensuring isolation from the primary call.
"""
# Copy kwargs to ensure isolation
silent_kwargs = copy.deepcopy(kwargs)
if "metadata" not in silent_kwargs:
silent_kwargs["metadata"] = {}
silent_kwargs["metadata"]["is_silent_experiment"] = True
# Pop logging objects and call IDs to ensure a fresh logging context
# This prevents collisions in the Proxy's database (spend_logs)
silent_kwargs.pop("litellm_call_id", None)
silent_kwargs.pop("litellm_logging_obj", None)
silent_kwargs.pop("standard_logging_object", None)
silent_kwargs.pop("proxy_server_request", None)
return silent_kwargs
def _silent_experiment_completion(
self, silent_model: str, messages: List[Any], **kwargs
):
"""
Run a silent experiment in the background (thread).
"""
try:
# Prevent infinite recursion if silent model also has a silent model
if kwargs.get("metadata", {}).get("is_silent_experiment", False):
return
messages = copy.deepcopy(messages)
verbose_router_logger.info(
f"Starting silent experiment for model {silent_model}"
)
silent_kwargs = self._get_silent_experiment_kwargs(**kwargs)
# Trigger the silent request
self.completion(
model=silent_model,
messages=cast(List[Dict[str, str]], messages),
**silent_kwargs,
)
except Exception as e:
verbose_router_logger.error(
f"Silent experiment failed for model {silent_model}: {str(e)}"
)
# fmt: off
@overload
async def acompletion(
self, model: str, messages: List[AllMessageValues], stream: Literal[True], **kwargs
) -> CustomStreamWrapper:
...
@overload
async def acompletion(
self, model: str, messages: List[AllMessageValues], stream: Literal[False] = False, **kwargs
) -> ModelResponse:
...
@overload
async def acompletion(
self, model: str, messages: List[AllMessageValues], stream: Union[Literal[True], Literal[False]] = False, **kwargs
) -> Union[CustomStreamWrapper, ModelResponse]:
...
# fmt: on
# The actual implementation of the function
async def acompletion(
self,
model: str,
messages: List[AllMessageValues],
stream: bool = False,
**kwargs,
):
try:
kwargs["model"] = model
kwargs["messages"] = messages
kwargs["stream"] = stream
kwargs["original_function"] = self._acompletion
self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
request_priority = kwargs.get("priority") or self.default_priority
start_time = time.time()
_is_prompt_management_model = self._is_prompt_management_model(model)
if _is_prompt_management_model:
return await self._prompt_management_factory(
model=model,
messages=messages,
kwargs=kwargs,
)
if request_priority is not None and isinstance(request_priority, int):
response = await self.schedule_acompletion(**kwargs)
else:
response = await self.async_function_with_fallbacks(**kwargs)
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_success_hook(
service=ServiceTypes.ROUTER,
duration=_duration,
call_type="acompletion",
start_time=start_time,
end_time=end_time,
parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs),
)
)
return response
except Exception as e:
asyncio.create_task(
send_llm_exception_alert(
litellm_router_instance=self,
request_kwargs=kwargs,
error_traceback_str=traceback.format_exc(),
original_exception=e,
)
)
raise e
async def _acompletion_streaming_iterator(
self,
model_response: CustomStreamWrapper,
messages: List[Dict[str, str]],
initial_kwargs: dict,
) -> CustomStreamWrapper:
"""
Helper to iterate over a streaming response.
Catches errors for fallbacks using the router's fallback system
"""
from litellm.exceptions import MidStreamFallbackError
class FallbackStreamWrapper(CustomStreamWrapper):
def __init__(self, async_generator: AsyncGenerator):
# Copy attributes from the original model_response
super().__init__(
completion_stream=async_generator,
model=model_response.model,
custom_llm_provider=model_response.custom_llm_provider,
logging_obj=model_response.logging_obj,
)
self._async_generator = async_generator
def __aiter__(self):
return self
async def __anext__(self):
return await self._async_generator.__anext__()
async def stream_with_fallbacks():
try:
async for item in model_response:
yield item
except MidStreamFallbackError as e:
from litellm.main import stream_chunk_builder
complete_response_object = stream_chunk_builder(
chunks=model_response.chunks
)
complete_response_object_usage = cast(
Optional[Usage],
getattr(complete_response_object, "usage", None),
)
try:
# Use the router's fallback system
model_group = cast(str, initial_kwargs.get("model"))
fallbacks: Optional[List] = initial_kwargs.get(
"fallbacks", self.fallbacks
)
context_window_fallbacks: Optional[List] = initial_kwargs.get(
"context_window_fallbacks", self.context_window_fallbacks
)
content_policy_fallbacks: Optional[List] = initial_kwargs.get(
"content_policy_fallbacks", self.content_policy_fallbacks
)
initial_kwargs["original_function"] = self._acompletion
initial_kwargs["messages"] = messages + [
{
"role": "system",
"content": "You are a helpful assistant. You are given a message and you need to respond to it. You are also given a generated content. You need to respond to the message in continuation of the generated content. Do not repeat the same content. Your response should be in continuation of this text: ",
},
{
"role": "assistant",
"content": e.generated_content,
"prefix": True,
},
]
self._update_kwargs_before_fallbacks(
model=model_group, kwargs=initial_kwargs
)
fallback_response = (
await self.async_function_with_fallbacks_common_utils(
e=e,
disable_fallbacks=False,
fallbacks=fallbacks,
context_window_fallbacks=context_window_fallbacks,
content_policy_fallbacks=content_policy_fallbacks,
model_group=model_group,
args=(),
kwargs=initial_kwargs,
)
)
# If fallback returns a streaming response, iterate over it
if hasattr(fallback_response, "__aiter__"):
async for fallback_item in fallback_response: # type: ignore
if (
fallback_item
and isinstance(fallback_item, ModelResponseStream)
and hasattr(fallback_item, "usage")
):
from litellm.cost_calculator import (
BaseTokenUsageProcessor,
)
usage = cast(
Optional[Usage],
getattr(fallback_item, "usage", None),
)
if usage is not None:
usage_objects = [usage]
else:
usage_objects = []
if (
complete_response_object_usage is not None
and hasattr(complete_response_object_usage, "usage")
and complete_response_object_usage.usage is not None # type: ignore
):
usage_objects.append(complete_response_object_usage)
combined_usage = (
BaseTokenUsageProcessor.combine_usage_objects(
usage_objects=usage_objects
)
)
setattr(fallback_item, "usage", combined_usage)
yield fallback_item
else:
# If fallback returns a non-streaming response, yield None
yield None
except Exception as fallback_error:
# If fallback also fails, log and re-raise original error
verbose_router_logger.error(
f"Fallback also failed: {fallback_error}"
)
raise fallback_error
return FallbackStreamWrapper(stream_with_fallbacks())
async def _silent_experiment_acompletion(
self, silent_model: str, messages: List[Any], **kwargs
):
"""
Run a silent experiment in the background.
"""
try:
# Prevent infinite recursion if silent model also has a silent model
if kwargs.get("metadata", {}).get("is_silent_experiment", False):
return
messages = copy.deepcopy(messages)
verbose_router_logger.info(
f"Starting silent experiment for model {silent_model}"
)
silent_kwargs = self._get_silent_experiment_kwargs(**kwargs)
# Trigger the silent request
await self.acompletion(
model=silent_model,
messages=cast(List[AllMessageValues], messages),
**silent_kwargs,
)
except Exception as e:
verbose_router_logger.error(
f"Silent experiment failed for model {silent_model}: {str(e)}"
)
async def _acompletion( # noqa: PLR0915
self, model: str, messages: List[Dict[str, str]], **kwargs
) -> Union[ModelResponse, CustomStreamWrapper,]:
"""
- Get an available deployment
- call it with a semaphore over the call
- semaphore specific to it's rpm
- in the semaphore, make a check against it's local rpm before running
"""
model_name = None
deployment = None
_timeout_debug_deployment_dict = (
{}
) # this is a temporary dict to debug timeout issues
try:
input_kwargs_for_streaming_fallback = kwargs.copy()
input_kwargs_for_streaming_fallback["model"] = model
parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
start_time = time.time()
deployment = await self.async_get_available_deployment(
model=model,
messages=messages,
specific_deployment=kwargs.pop("specific_deployment", None),
request_kwargs=kwargs,
)
_timeout_debug_deployment_dict = deployment
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_success_hook(
service=ServiceTypes.ROUTER,
duration=_duration,
call_type="async_get_available_deployment",
start_time=start_time,
end_time=end_time,
parent_otel_span=_get_parent_otel_span_from_kwargs(kwargs),
)
)
# debug how often this deployment picked
self._track_deployment_metrics(
deployment=deployment, parent_otel_span=parent_otel_span
)
# Check for silent model experiment
# Make a local copy of litellm_params to avoid mutating the Router's state
litellm_params = deployment["litellm_params"].copy()
silent_model = litellm_params.pop("silent_model", None)
if silent_model is not None:
# Mirroring traffic to a secondary model
# This is a silent experiment, so we don't want to block the primary request
asyncio.create_task(
self._silent_experiment_acompletion(
silent_model=silent_model,
messages=messages, # Use messages instead of *args
**kwargs,
)
)
self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
kwargs.pop("silent_model", None) # Ensure it's not in kwargs either
# No copy needed - data is only read and spread into new dict below
data = litellm_params.copy() # Use the local copy of litellm_params
model_name = data["model"]
model_client = self._get_async_openai_model_client(
deployment=deployment,
kwargs=kwargs,
)
self.total_calls[model_name] += 1
input_kwargs = {
**data,
"messages": messages,
"caching": self.cache_responses,
"client": model_client,
**kwargs,
}
input_kwargs.pop("silent_model", None)
_response = litellm.acompletion(**input_kwargs)
logging_obj: Optional[LiteLLMLogging] = kwargs.get(
"litellm_logging_obj", None
)
rpm_semaphore = self._get_client(
deployment=deployment,
kwargs=kwargs,
client_type="max_parallel_requests",
)
if rpm_semaphore is not None and isinstance(
rpm_semaphore, asyncio.Semaphore
):
async with rpm_semaphore:
"""
- Check rpm limits before making the call
- If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe)
"""
await self.async_routing_strategy_pre_call_checks(
deployment=deployment,
logging_obj=logging_obj,
parent_otel_span=parent_otel_span,
)
response = await _response
else:
await self.async_routing_strategy_pre_call_checks(
deployment=deployment,
logging_obj=logging_obj,
parent_otel_span=parent_otel_span,
)
response = await _response
## CHECK CONTENT FILTER ERROR ##
if isinstance(response, ModelResponse):
_should_raise = self._should_raise_content_policy_error(
model=model, response=response, kwargs=kwargs
)
if _should_raise:
raise litellm.ContentPolicyViolationError(
message="Response output was blocked.",
model=model,
llm_provider="",
)
self.success_calls[model_name] += 1
verbose_router_logger.info(
f"litellm.acompletion(model={model_name})\033[32m 200 OK\033[0m"
)
# debug how often this deployment picked
self._track_deployment_metrics(
deployment=deployment,
response=response,
parent_otel_span=parent_otel_span,
)
if isinstance(response, CustomStreamWrapper):
return await self._acompletion_streaming_iterator(
model_response=response,
messages=messages,
initial_kwargs=input_kwargs_for_streaming_fallback,
)
return response
except litellm.Timeout as e:
deployment_request_timeout_param = _timeout_debug_deployment_dict.get(
"litellm_params", {}
).get("request_timeout", None)
deployment_timeout_param = _timeout_debug_deployment_dict.get(
"litellm_params", {}
).get("timeout", None)
e.message += f"\n\nDeployment Info: request_timeout: {deployment_request_timeout_param}\ntimeout: {deployment_timeout_param}"
# Set per-deployment num_retries on exception for retry logic
if deployment is not None:
self._set_deployment_num_retries_on_exception(e, deployment)
raise e
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
# Set per-deployment num_retries on exception for retry logic
if deployment is not None:
self._set_deployment_num_retries_on_exception(e, deployment)
raise e
def _update_kwargs_before_fallbacks(
self,
model: str,
kwargs: dict,
metadata_variable_name: Optional[str] = "metadata",
) -> None:
"""
Adds/updates to kwargs:
- num_retries
- litellm_trace_id
- metadata
"""
kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
kwargs.setdefault("litellm_trace_id", str(uuid.uuid4()))
model_group_alias: Optional[str] = None
if self._get_model_from_alias(model=model):
model_group_alias = model
kwargs.setdefault(metadata_variable_name, {}).update(
{"model_group": model, "model_group_alias": model_group_alias}
)
def _set_deployment_num_retries_on_exception(
self, exception: Exception, deployment: dict
) -> None:
"""
Set num_retries from deployment litellm_params on the exception.
This allows the retry logic in async_function_with_retries to use
per-deployment retry settings instead of the global setting.
"""
# Only set if exception doesn't already have num_retries
if hasattr(exception, "num_retries") and exception.num_retries is not None: # type: ignore
return
litellm_params = deployment.get("litellm_params", {})
dep_num_retries = litellm_params.get("num_retries")
if dep_num_retries is not None:
try:
exception.num_retries = int(dep_num_retries) # type: ignore # Handle both int and str
except (ValueError, TypeError):
pass # Skip if value can't be converted to int
def _update_kwargs_with_default_litellm_params(
self, kwargs: dict, metadata_variable_name: Optional[str] = "metadata"
) -> None:
"""
Adds default litellm params to kwargs, if set.
Handles inserting this as either "metadata" or "litellm_metadata" depending on the metadata_variable_name
"""
# 1) copy your defaults and pull out metadata
defaults = self.default_litellm_params.copy()
metadata_defaults = defaults.pop("metadata", {}) or {}
# 2) add any non-metadata defaults that aren't already in kwargs
for key, value in defaults.items():
if value is None:
continue
kwargs.setdefault(key, value)
# 3) merge in metadata, this handles inserting this as either "metadata" or "litellm_metadata"
kwargs.setdefault(metadata_variable_name, {}).update(metadata_defaults)
def _handle_clientside_credential(
self, deployment: dict, kwargs: dict, function_name: Optional[str] = None
) -> Deployment:
"""
Handle clientside credential
"""
model_info = deployment.get("model_info", {}).copy()
litellm_params = deployment["litellm_params"].copy()
dynamic_litellm_params = get_dynamic_litellm_params(
litellm_params=litellm_params, request_kwargs=kwargs
)
# Use deployment model_name as model_group for generating model_id
metadata_variable_name = _get_router_metadata_variable_name(
function_name=function_name,
)
model_group = kwargs.get(metadata_variable_name, {}).get("model_group")
_model_id = self._generate_model_id(
model_group=model_group, litellm_params=dynamic_litellm_params
)
original_model_id = model_info.get("id")
model_info["id"] = _model_id
model_info["original_model_id"] = original_model_id
deployment_pydantic_obj = Deployment(
model_name=model_group,
litellm_params=LiteLLM_Params(**dynamic_litellm_params),
model_info=model_info,
)
self.upsert_deployment(
deployment=deployment_pydantic_obj
) # add new deployment to router
return deployment_pydantic_obj
def _update_kwargs_with_deployment(
self,
deployment: dict,
kwargs: dict,
function_name: Optional[str] = None,
) -> None:
"""
2 jobs:
- Adds selected deployment, model_info and api_base to kwargs["metadata"] (used for logging)
- Adds default litellm params to kwargs, if set.
"""
model_info = deployment.get("model_info", {}).copy()
deployment_litellm_model_name = deployment["litellm_params"]["model"]
deployment_api_base = deployment["litellm_params"].get("api_base")
deployment_model_name = deployment["model_name"]
if is_clientside_credential(request_kwargs=kwargs):
deployment_pydantic_obj = self._handle_clientside_credential(
deployment=deployment, kwargs=kwargs, function_name=function_name
)
model_info = deployment_pydantic_obj.model_info.model_dump()
deployment_litellm_model_name = deployment_pydantic_obj.litellm_params.model
deployment_api_base = deployment_pydantic_obj.litellm_params.api_base
metadata_variable_name = _get_router_metadata_variable_name(
function_name=function_name,
)
kwargs.setdefault(metadata_variable_name, {}).update(
{
"deployment": deployment_litellm_model_name,
"model_info": model_info,
"api_base": deployment_api_base,
"deployment_model_name": deployment_model_name,
}
)
kwargs["model_info"] = model_info
kwargs["timeout"] = self._get_timeout(
kwargs=kwargs, data=deployment["litellm_params"]
)
self._update_kwargs_with_default_litellm_params(
kwargs=kwargs, metadata_variable_name=metadata_variable_name
)
def _get_async_openai_model_client(self, deployment: dict, kwargs: dict):
"""
Helper to get AsyncOpenAI or AsyncAzureOpenAI client that was created for the deployment
The same OpenAI client is re-used to optimize latency / performance in production
If dynamic api key is provided:
Do not re-use the client. Pass model_client=None. The OpenAI/ AzureOpenAI client will be recreated in the handler for the llm provider
"""
potential_model_client = self._get_client(
deployment=deployment, kwargs=kwargs, client_type="async"
)
# check if provided keys == client keys #
dynamic_api_key = kwargs.get("api_key", None)
if (
dynamic_api_key is not None
and potential_model_client is not None
and dynamic_api_key != potential_model_client.api_key
):
model_client = None
else:
model_client = potential_model_client
return model_client
def _get_stream_timeout(
self, kwargs: dict, data: dict
) -> Optional[Union[float, int]]:
"""Helper to get stream timeout from kwargs or deployment params"""
return (
kwargs.get("stream_timeout", None) # the params dynamically set by user
or data.get(
"stream_timeout", None
) # timeout set on litellm_params for this deployment
or self.stream_timeout # timeout set on router
or self.default_litellm_params.get("stream_timeout", None)
)
def _get_non_stream_timeout(
self, kwargs: dict, data: dict
) -> Optional[Union[float, int]]:
"""Helper to get non-stream timeout from kwargs or deployment params"""
timeout = (
kwargs.get("timeout", None) # the params dynamically set by user
or kwargs.get("request_timeout", None) # the params dynamically set by user
or data.get(
"timeout", None
) # timeout set on litellm_params for this deployment
or data.get(
"request_timeout", None
) # timeout set on litellm_params for this deployment
or self.timeout # timeout set on router
or self.default_litellm_params.get("timeout", None)
)
return timeout
def _get_timeout(self, kwargs: dict, data: dict) -> Optional[Union[float, int]]:
"""Helper to get timeout from kwargs or deployment params"""
timeout: Optional[Union[float, int]] = None
if kwargs.get("stream", False):
timeout = self._get_stream_timeout(kwargs=kwargs, data=data)
if timeout is None:
timeout = self._get_non_stream_timeout(
kwargs=kwargs, data=data
) # default to this if no stream specific timeout set
return timeout
async def abatch_completion(
self,
models: List[str],
messages: Union[List[Dict[str, str]], List[List[Dict[str, str]]]],
**kwargs,
):
"""
Async Batch Completion. Used for 2 scenarios:
1. Batch Process 1 request to N models on litellm.Router. Pass messages as List[Dict[str, str]] to use this
2. Batch Process N requests to M models on litellm.Router. Pass messages as List[List[Dict[str, str]]] to use this
Example Request for 1 request to N models:
```
response = await router.abatch_completion(
models=["gpt-3.5-turbo", "groq-llama"],
messages=[
{"role": "user", "content": "is litellm becoming a better product ?"}
],
max_tokens=15,
)
```
Example Request for N requests to M models:
```
response = await router.abatch_completion(
models=["gpt-3.5-turbo", "groq-llama"],
messages=[
[{"role": "user", "content": "is litellm becoming a better product ?"}],
[{"role": "user", "content": "who is this"}],
],
)
```
"""
############## Helpers for async completion ##################
async def _async_completion_no_exceptions(
model: str, messages: List[AllMessageValues], **kwargs
):
"""
Wrapper around self.async_completion that catches exceptions and returns them as a result
"""
try:
return await self.acompletion(model=model, messages=messages, **kwargs)
except Exception as e:
return e
async def _async_completion_no_exceptions_return_idx(
model: str,
messages: List[AllMessageValues],
idx: int, # index of message this response corresponds to
**kwargs,
):
"""
Wrapper around self.async_completion that catches exceptions and returns them as a result
"""
try:
return (
await self.acompletion(model=model, messages=messages, **kwargs),
idx,
)
except Exception as e:
return e, idx
############## Helpers for async completion ##################
if isinstance(messages, list) and all(isinstance(m, dict) for m in messages):
_tasks = []
for model in models:
# add each task but if the task fails
_tasks.append(_async_completion_no_exceptions(model=model, messages=messages, **kwargs)) # type: ignore
response = await asyncio.gather(*_tasks)
return response
elif isinstance(messages, list) and all(isinstance(m, list) for m in messages):
_tasks = []
for idx, message in enumerate(messages):
for model in models:
# Request Number X, Model Number Y
_tasks.append(
_async_completion_no_exceptions_return_idx(
model=model, idx=idx, messages=message, **kwargs # type: ignore
)
)
responses = await asyncio.gather(*_tasks)
final_responses: List[List[Any]] = [[] for _ in range(len(messages))]
for response in responses:
if isinstance(response, tuple):
final_responses[response[1]].append(response[0])
else:
final_responses[0].append(response)
return final_responses
async def abatch_completion_one_model_multiple_requests(
self, model: str, messages: List[List[AllMessageValues]], **kwargs
):
"""
Async Batch Completion - Batch Process multiple Messages to one model_group on litellm.Router
Use this for sending multiple requests to 1 model
Args:
model (List[str]): model group
messages (List[List[Dict[str, str]]]): list of messages. Each element in the list is one request
**kwargs: additional kwargs
Usage:
response = await self.abatch_completion_one_model_multiple_requests(
model="gpt-3.5-turbo",
messages=[
[{"role": "user", "content": "hello"}, {"role": "user", "content": "tell me something funny"}],
[{"role": "user", "content": "hello good mornign"}],
]
)
"""
async def _async_completion_no_exceptions(
model: str, messages: List[AllMessageValues], **kwargs
):
"""
Wrapper around self.async_completion that catches exceptions and returns them as a result
"""
try:
return await self.acompletion(model=model, messages=messages, **kwargs)
except Exception as e:
return e
_tasks = []
for message_request in messages:
# add each task but if the task fails
_tasks.append(
_async_completion_no_exceptions(
model=model, messages=message_request, **kwargs
)
)
response = await asyncio.gather(*_tasks)
return response
# fmt: off
@overload
async def abatch_completion_fastest_response(
self, model: str, messages: List[Dict[str, str]], stream: Literal[True], **kwargs
) -> CustomStreamWrapper:
...
@overload
async def abatch_completion_fastest_response(
self, model: str, messages: List[Dict[str, str]], stream: Literal[False] = False, **kwargs
) -> ModelResponse:
...
# fmt: on
async def abatch_completion_fastest_response(
self,
model: str,
messages: List[Dict[str, str]],
stream: bool = False,
**kwargs,
):
"""
model - List of comma-separated model names. E.g. model="gpt-4, gpt-3.5-turbo"
Returns fastest response from list of model names. OpenAI-compatible endpoint.
"""
models = [m.strip() for m in model.split(",")]
async def _async_completion_no_exceptions(
model: str, messages: List[Dict[str, str]], stream: bool, **kwargs: Any
) -> Union[ModelResponse, CustomStreamWrapper, Exception]:
"""
Wrapper around self.acompletion that catches exceptions and returns them as a result
"""
try:
result = await self.acompletion(model=model, messages=messages, stream=stream, **kwargs) # type: ignore
return result
except asyncio.CancelledError:
verbose_router_logger.debug(
"Received 'task.cancel'. Cancelling call w/ model={}.".format(model)
)
raise
except Exception as e:
return e
pending_tasks = [] # type: ignore
async def check_response(task: asyncio.Task):
nonlocal pending_tasks
try:
result = await task
if isinstance(result, (ModelResponse, CustomStreamWrapper)):
verbose_router_logger.debug(
"Received successful response. Cancelling other LLM API calls."
)
# If a desired response is received, cancel all other pending tasks
for t in pending_tasks:
t.cancel()
return result
except Exception:
# Ignore exceptions, let the loop handle them
pass
finally:
# Remove the task from pending tasks if it finishes
try:
pending_tasks.remove(task)
except KeyError:
pass
for model in models:
task = asyncio.create_task(
_async_completion_no_exceptions(
model=model, messages=messages, stream=stream, **kwargs
)
)
pending_tasks.append(task)
# Await the first task to complete successfully
while pending_tasks:
done, pending_tasks = await asyncio.wait( # type: ignore
pending_tasks, return_when=asyncio.FIRST_COMPLETED
)
for completed_task in done:
result = await check_response(completed_task)
if result is not None:
# Return the first successful result
result._hidden_params["fastest_response_batch_completion"] = True
return result
# If we exit the loop without returning, all tasks failed
raise Exception("All tasks failed")
### SCHEDULER ###
# fmt: off
@overload
async def schedule_acompletion(
self, model: str, messages: List[AllMessageValues], priority: int, stream: Literal[False] = False, **kwargs
) -> ModelResponse:
...
@overload
async def schedule_acompletion(
self, model: str, messages: List[AllMessageValues], priority: int, stream: Literal[True], **kwargs
) -> CustomStreamWrapper:
...
# fmt: on
async def schedule_acompletion(
self,
model: str,
messages: List[AllMessageValues],
priority: int,
stream=False,
**kwargs,
):
parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
### FLOW ITEM ###
_request_id = str(uuid.uuid4())
item = FlowItem(
priority=priority, # 👈 SET PRIORITY FOR REQUEST
request_id=_request_id, # 👈 SET REQUEST ID
model_name="gpt-3.5-turbo", # 👈 SAME as 'Router'
)
### [fin] ###
## ADDS REQUEST TO QUEUE ##
await self.scheduler.add_request(request=item)
## POLL QUEUE
end_time = time.monotonic() + self.timeout
curr_time = time.monotonic()
poll_interval = self.scheduler.polling_interval # poll every 3ms
make_request = False
while curr_time < end_time:
_healthy_deployments, _ = await self._async_get_healthy_deployments(
model=model, parent_otel_span=parent_otel_span
)
make_request = await self.scheduler.poll( ## POLL QUEUE ## - returns 'True' if there's healthy deployments OR if request is at top of queue
id=item.request_id,
model_name=item.model_name,
health_deployments=_healthy_deployments,
)
if make_request: ## IF TRUE -> MAKE REQUEST
break
else: ## ELSE -> loop till default_timeout
await asyncio.sleep(poll_interval)
curr_time = time.monotonic()
if make_request:
try:
_response = await self.acompletion(
model=model, messages=messages, stream=stream, **kwargs
)
_response._hidden_params.setdefault("additional_headers", {})
_response._hidden_params["additional_headers"].update(
{"x-litellm-request-prioritization-used": True}
)
return _response
except Exception as e:
setattr(e, "priority", priority)
raise e
else:
raise litellm.Timeout(
message="Request timed out while polling queue",
model=model,
llm_provider="openai",
)
async def _schedule_factory(
self,
model: str,
priority: int,
original_function: Callable,
args: Tuple[Any, ...],
kwargs: Dict[str, Any],
):
parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
### FLOW ITEM ###
_request_id = str(uuid.uuid4())
item = FlowItem(
priority=priority, # 👈 SET PRIORITY FOR REQUEST
request_id=_request_id, # 👈 SET REQUEST ID
model_name=model, # 👈 SAME as 'Router'
)
### [fin] ###
## ADDS REQUEST TO QUEUE ##
await self.scheduler.add_request(request=item)
## POLL QUEUE
end_time = time.monotonic() + self.timeout
curr_time = time.monotonic()
poll_interval = self.scheduler.polling_interval # poll every 3ms
make_request = False
while curr_time < end_time:
_healthy_deployments, _ = await self._async_get_healthy_deployments(
model=model, parent_otel_span=parent_otel_span
)
make_request = await self.scheduler.poll( ## POLL QUEUE ## - returns 'True' if there's healthy deployments OR if request is at top of queue
id=item.request_id,
model_name=item.model_name,
health_deployments=_healthy_deployments,
)
if make_request: ## IF TRUE -> MAKE REQUEST
break
else: ## ELSE -> loop till default_timeout
await asyncio.sleep(poll_interval)
curr_time = time.monotonic()
if make_request:
try:
_response = await original_function(*args, **kwargs)
if isinstance(_response._hidden_params, dict):
_response._hidden_params.setdefault("additional_headers", {})
_response._hidden_params["additional_headers"].update(
{"x-litellm-request-prioritization-used": True}
)
return _response
except Exception as e:
setattr(e, "priority", priority)
raise e
else:
raise litellm.Timeout(
message="Request timed out while polling queue",
model=model,
llm_provider="openai",
)
def _is_prompt_management_model(self, model: str) -> bool:
model_list = self.get_model_list(model_name=model)
if model_list is None or len(model_list) != 1:
return False
litellm_model = model_list[0]["litellm_params"].get("model", None)
if litellm_model is None or "/" not in litellm_model:
return False
split_litellm_model = litellm_model.split("/")[0]
return split_litellm_model in litellm._known_custom_logger_compatible_callbacks
async def _prompt_management_factory(
self,
model: str,
messages: List[AllMessageValues],
kwargs: Dict[str, Any],
):
litellm_logging_object = kwargs.get("litellm_logging_obj", None)
if litellm_logging_object is None:
litellm_logging_object, kwargs = function_setup(
**{
"original_function": "acompletion",
"rules_obj": Rules(),
"start_time": get_utc_datetime(),
**kwargs,
}
)
litellm_logging_object = cast(LiteLLMLogging, litellm_logging_object)
prompt_management_deployment = self.get_available_deployment(
model=model,
messages=[{"role": "user", "content": "prompt"}],
specific_deployment=kwargs.pop("specific_deployment", None),
)
self._update_kwargs_with_deployment(
deployment=prompt_management_deployment, kwargs=kwargs
)
data = prompt_management_deployment["litellm_params"].copy()
litellm_model = data.get("model", None)
prompt_id = kwargs.get("prompt_id") or prompt_management_deployment[
"litellm_params"
].get("prompt_id", None)
prompt_variables = kwargs.get(
"prompt_variables"
) or prompt_management_deployment["litellm_params"].get(
"prompt_variables", None
)
prompt_label = kwargs.get("prompt_label", None) or prompt_management_deployment[
"litellm_params"
].get("prompt_label", None)
if prompt_id is None or not isinstance(prompt_id, str):
raise ValueError(
f"Prompt ID is not set or not a string. Got={prompt_id}, type={type(prompt_id)}"
)
if prompt_variables is not None and not isinstance(prompt_variables, dict):
raise ValueError(
f"Prompt variables is set but not a dictionary. Got={prompt_variables}, type={type(prompt_variables)}"
)
(
model,
messages,
optional_params,
) = litellm_logging_object.get_chat_completion_prompt(
model=litellm_model,
messages=messages,
non_default_params=get_non_default_completion_params(kwargs=kwargs),
prompt_id=prompt_id,
prompt_variables=prompt_variables,
prompt_label=prompt_label,
)
# Filter out prompt management specific parameters from data before merging
prompt_management_params = {
"bitbucket_config",
"dotprompt_config",
"prompt_id",
"prompt_variables",
"prompt_label",
"prompt_version",
}
filtered_data = {
k: v for k, v in data.items() if k not in prompt_management_params
}
kwargs = {**filtered_data, **kwargs, **optional_params}
kwargs["model"] = model
kwargs["messages"] = messages
kwargs["litellm_logging_obj"] = litellm_logging_object
kwargs["prompt_id"] = prompt_id
kwargs["prompt_variables"] = prompt_variables
kwargs["prompt_label"] = prompt_label
_model_list = self.get_model_list(model_name=model)
if _model_list is None or len(_model_list) == 0: # if direct call to model
kwargs.pop("original_function")
return await litellm.acompletion(**kwargs)
return await self.async_function_with_fallbacks(**kwargs)
def image_generation(self, prompt: str, model: str, **kwargs):
try:
kwargs["model"] = model
kwargs["prompt"] = prompt
kwargs["original_function"] = self._image_generation
kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
kwargs.setdefault("metadata", {}).update({"model_group": model})
response = self.function_with_fallbacks(**kwargs)
return response
except Exception as e:
raise e
def _image_generation(self, prompt: str, model: str, **kwargs):
model_name = ""
try:
verbose_router_logger.debug(
f"Inside _image_generation()- model: {model}; kwargs: {kwargs}"
)
deployment = self.get_available_deployment(
model=model,
messages=[{"role": "user", "content": "prompt"}],
specific_deployment=kwargs.pop("specific_deployment", None),
)
self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
data = deployment["litellm_params"].copy()
model_client = self._get_async_openai_model_client(
deployment=deployment,
kwargs=kwargs,
)
self.total_calls[model_name] += 1
### DEPLOYMENT-SPECIFIC PRE-CALL CHECKS ### (e.g. update rpm pre-call. Raise error, if deployment over limit)
self.routing_strategy_pre_call_checks(deployment=deployment)
response = litellm.image_generation(
**{
**data,
"prompt": prompt,
"caching": self.cache_responses,
"client": model_client,
**kwargs,
}
)
self.success_calls[model_name] += 1
verbose_router_logger.info(
f"litellm.image_generation(model={model_name})\033[32m 200 OK\033[0m"
)
return response
except Exception as e:
verbose_router_logger.info(
f"litellm.image_generation(model={model_name})\033[31m Exception {str(e)}\033[0m"
)
if model_name is not None:
self.fail_calls[model_name] += 1
raise e
async def aimage_generation(self, prompt: str, model: str, **kwargs):
try:
kwargs["model"] = model
kwargs["prompt"] = prompt
kwargs["original_function"] = self._aimage_generation
kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
response = await self.async_function_with_fallbacks(**kwargs)
return response
except Exception as e:
asyncio.create_task(
send_llm_exception_alert(
litellm_router_instance=self,
request_kwargs=kwargs,
error_traceback_str=traceback.format_exc(),
original_exception=e,
)
)
raise e
async def _aimage_generation(self, prompt: str, model: str, **kwargs):
model_name = model
try:
verbose_router_logger.debug(
f"Inside _image_generation()- model: {model}; kwargs: {kwargs}"
)
parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
deployment = await self.async_get_available_deployment(
model=model,
messages=[{"role": "user", "content": "prompt"}],
specific_deployment=kwargs.pop("specific_deployment", None),
request_kwargs=kwargs,
)
self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
data = deployment["litellm_params"].copy()
model_name = data["model"]
model_client = self._get_async_openai_model_client(
deployment=deployment,
kwargs=kwargs,
)
self.total_calls[model_name] += 1
response = litellm.aimage_generation(
**{
**data,
"prompt": prompt,
"caching": self.cache_responses,
"client": model_client,
**kwargs,
}
)
### CONCURRENCY-SAFE RPM CHECKS ###
rpm_semaphore = self._get_client(
deployment=deployment,
kwargs=kwargs,
client_type="max_parallel_requests",
)
if rpm_semaphore is not None and isinstance(
rpm_semaphore, asyncio.Semaphore
):
async with rpm_semaphore:
"""
- Check rpm limits before making the call
- If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe)
"""
await self.async_routing_strategy_pre_call_checks(
deployment=deployment, parent_otel_span=parent_otel_span
)
response = await response
else:
await self.async_routing_strategy_pre_call_checks(
deployment=deployment, parent_otel_span=parent_otel_span
)
response = await response
self.success_calls[model_name] += 1
verbose_router_logger.info(
f"litellm.aimage_generation(model={model_name})\033[32m 200 OK\033[0m"
)
return response
except Exception as e:
verbose_router_logger.info(
f"litellm.aimage_generation(model={model_name})\033[31m Exception {str(e)}\033[0m"
)
if model_name is not None:
self.fail_calls[model_name] += 1
raise e
async def atranscription(self, file: FileTypes, model: str, **kwargs):
"""
Example Usage:
```
from litellm import Router
client = Router(model_list = [
{
"model_name": "whisper",
"litellm_params": {
"model": "whisper-1",
},
},
])
audio_file = open("speech.mp3", "rb")
transcript = await client.atranscription(
model="whisper",
file=audio_file
)
```
"""
try:
kwargs["model"] = model
kwargs["file"] = file
kwargs["original_function"] = self._atranscription
self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
response = await self.async_function_with_fallbacks(**kwargs)
return response
except Exception as e:
asyncio.create_task(
send_llm_exception_alert(
litellm_router_instance=self,
request_kwargs=kwargs,
error_traceback_str=traceback.format_exc(),
original_exception=e,
)
)
raise e
async def _atranscription(self, file: FileTypes, model: str, **kwargs):
model_name = model
try:
verbose_router_logger.debug(
f"Inside _atranscription()- model: {model}; kwargs: {kwargs}"
)
parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
deployment = await self.async_get_available_deployment(
model=model,
messages=[{"role": "user", "content": "prompt"}],
specific_deployment=kwargs.pop("specific_deployment", None),
request_kwargs=kwargs,
)
self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
data = deployment["litellm_params"].copy()
model_client = self._get_async_openai_model_client(
deployment=deployment,
kwargs=kwargs,
)
self.total_calls[model_name] += 1
response = litellm.atranscription(
**{
**data,
"file": file,
"caching": self.cache_responses,
"client": model_client,
**kwargs,
}
)
### CONCURRENCY-SAFE RPM CHECKS ###
rpm_semaphore = self._get_client(
deployment=deployment,
kwargs=kwargs,
client_type="max_parallel_requests",
)
if rpm_semaphore is not None and isinstance(
rpm_semaphore, asyncio.Semaphore
):
async with rpm_semaphore:
"""
- Check rpm limits before making the call
- If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe)
"""
await self.async_routing_strategy_pre_call_checks(
deployment=deployment, parent_otel_span=parent_otel_span
)
response = await response
else:
await self.async_routing_strategy_pre_call_checks(
deployment=deployment, parent_otel_span=parent_otel_span
)
response = await response
self.success_calls[model_name] += 1
verbose_router_logger.info(
f"litellm.atranscription(model={model_name})\033[32m 200 OK\033[0m"
)
return response
except Exception as e:
verbose_router_logger.info(
f"litellm.atranscription(model={model_name})\033[31m Exception {str(e)}\033[0m"
)
if model_name is not None:
self.fail_calls[model_name] += 1
raise e
async def aspeech(self, model: str, input: str, voice: str, **kwargs):
"""
Example Usage:
```
from litellm import Router
client = Router(model_list = [
{
"model_name": "tts",
"litellm_params": {
"model": "tts-1",
},
},
])
async with client.aspeech(
model="tts",
voice="alloy",
input="the quick brown fox jumped over the lazy dogs",
api_base=None,
api_key=None,
organization=None,
project=None,
max_retries=1,
timeout=600,
client=None,
optional_params={},
) as response:
response.stream_to_file(speech_file_path)
```
"""
try:
kwargs["input"] = input
kwargs["voice"] = voice
deployment = await self.async_get_available_deployment(
model=model,
messages=[{"role": "user", "content": "prompt"}],
specific_deployment=kwargs.pop("specific_deployment", None),
request_kwargs=kwargs,
)
self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
data = deployment["litellm_params"].copy()
data["model"]
for k, v in self.default_litellm_params.items():
if (
k not in kwargs
): # prioritize model-specific params > default router params
kwargs[k] = v
elif k == "metadata":
kwargs[k].update(v)
potential_model_client = self._get_client(
deployment=deployment, kwargs=kwargs, client_type="async"
)
# check if provided keys == client keys #
dynamic_api_key = kwargs.get("api_key", None)
if (
dynamic_api_key is not None
and potential_model_client is not None
and dynamic_api_key != potential_model_client.api_key
):
model_client = None
else:
model_client = potential_model_client
response = await litellm.aspeech(
**{
**data,
"client": model_client,
**kwargs,
}
)
return response
except Exception as e:
asyncio.create_task(
send_llm_exception_alert(
litellm_router_instance=self,
request_kwargs=kwargs,
error_traceback_str=traceback.format_exc(),
original_exception=e,
)
)
raise e
async def arerank(self, model: str, **kwargs):
try:
kwargs["model"] = model
kwargs["input"] = input
kwargs["original_function"] = self._arerank
self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
response = await self.async_function_with_fallbacks(**kwargs)
return response
except Exception as e:
asyncio.create_task(
send_llm_exception_alert(
litellm_router_instance=self,
request_kwargs=kwargs,
error_traceback_str=traceback.format_exc(),
original_exception=e,
)
)
raise e
async def _arerank(self, model: str, **kwargs):
model_name = None
try:
verbose_router_logger.debug(
f"Inside _rerank()- model: {model}; kwargs: {kwargs}"
)
deployment = await self.async_get_available_deployment(
model=model,
specific_deployment=kwargs.pop("specific_deployment", None),
request_kwargs=kwargs,
)
self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
data = deployment["litellm_params"].copy()
model_name = data["model"]
model_client = self._get_async_openai_model_client(
deployment=deployment,
kwargs=kwargs,
)
self.total_calls[model_name] += 1
response = await litellm.arerank(
**{
**data,
"caching": self.cache_responses,
"client": model_client,
**kwargs,
}
)
self.success_calls[model_name] += 1
verbose_router_logger.info(
f"litellm.arerank(model={model_name})\033[32m 200 OK\033[0m"
)
return response
except Exception as e:
verbose_router_logger.info(
f"litellm.arerank(model={model_name})\033[31m Exception {str(e)}\033[0m"
)
if model_name is not None:
self.fail_calls[model_name] += 1
raise e
def text_completion(
self,
model: str,
prompt: str,
is_retry: Optional[bool] = False,
is_fallback: Optional[bool] = False,
is_async: Optional[bool] = False,
**kwargs,
):
messages = [{"role": "user", "content": prompt}]
try:
kwargs["model"] = model
kwargs["prompt"] = prompt
kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
kwargs.setdefault("metadata", {}).update({"model_group": model})
# pick the one that is available (lowest TPM/RPM)
deployment = self.get_available_deployment(
model=model,
messages=messages,
specific_deployment=kwargs.pop("specific_deployment", None),
)
data = deployment["litellm_params"].copy()
for k, v in self.default_litellm_params.items():
if (
k not in kwargs
): # prioritize model-specific params > default router params
kwargs[k] = v
elif k == "metadata":
kwargs[k].update(v)
# call via litellm.completion()
return litellm.text_completion(**{**data, "prompt": prompt, "caching": self.cache_responses, **kwargs}) # type: ignore
except Exception as e:
raise e
async def atext_completion(
self,
model: str,
prompt: str,
is_retry: Optional[bool] = False,
is_fallback: Optional[bool] = False,
is_async: Optional[bool] = False,
**kwargs,
):
if kwargs.get("priority", None) is not None:
return await self._schedule_factory(
model=model,
priority=kwargs.pop("priority"),
original_function=self.atext_completion,
args=(model, prompt),
kwargs=kwargs,
)
try:
kwargs["model"] = model
kwargs["prompt"] = prompt
kwargs["original_function"] = self._atext_completion
self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
response = await self.async_function_with_fallbacks(**kwargs)
return response
except Exception as e:
asyncio.create_task(
send_llm_exception_alert(
litellm_router_instance=self,
request_kwargs=kwargs,
error_traceback_str=traceback.format_exc(),
original_exception=e,
)
)
raise e
async def _atext_completion(self, model: str, prompt: str, **kwargs):
try:
verbose_router_logger.debug(
f"Inside _atext_completion()- model: {model}; kwargs: {kwargs}"
)
parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
deployment = await self.async_get_available_deployment(
model=model,
messages=[{"role": "user", "content": prompt}],
specific_deployment=kwargs.pop("specific_deployment", None),
request_kwargs=kwargs,
)
self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
data = deployment["litellm_params"].copy()
model_name = data["model"]
model_client = self._get_async_openai_model_client(
deployment=deployment,
kwargs=kwargs,
)
self.total_calls[model_name] += 1
response = litellm.atext_completion(
**{
**data,
"prompt": prompt,
"caching": self.cache_responses,
"client": model_client,
**kwargs,
}
)
rpm_semaphore = self._get_client(
deployment=deployment,
kwargs=kwargs,
client_type="max_parallel_requests",
)
if rpm_semaphore is not None and isinstance(
rpm_semaphore, asyncio.Semaphore
):
async with rpm_semaphore:
"""
- Check rpm limits before making the call
- If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe)
"""
await self.async_routing_strategy_pre_call_checks(
deployment=deployment, parent_otel_span=parent_otel_span
)
response = await response
else:
await self.async_routing_strategy_pre_call_checks(
deployment=deployment, parent_otel_span=parent_otel_span
)
response = await response
self.success_calls[model_name] += 1
verbose_router_logger.info(
f"litellm.atext_completion(model={model_name})\033[32m 200 OK\033[0m"
)
return response
except Exception as e:
verbose_router_logger.info(
f"litellm.atext_completion(model={model})\033[31m Exception {str(e)}\033[0m"
)
if model is not None:
self.fail_calls[model] += 1
raise e
async def aadapter_completion(
self,
adapter_id: str,
model: str,
is_retry: Optional[bool] = False,
is_fallback: Optional[bool] = False,
is_async: Optional[bool] = False,
**kwargs,
):
try:
kwargs["model"] = model
kwargs["adapter_id"] = adapter_id
kwargs["original_function"] = self._aadapter_completion
kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
kwargs.setdefault("metadata", {}).update({"model_group": model})
response = await self.async_function_with_fallbacks(**kwargs)
return response
except Exception as e:
asyncio.create_task(
send_llm_exception_alert(
litellm_router_instance=self,
request_kwargs=kwargs,
error_traceback_str=traceback.format_exc(),
original_exception=e,
)
)
raise e
async def _aadapter_completion(self, adapter_id: str, model: str, **kwargs):
try:
verbose_router_logger.debug(
f"Inside _aadapter_completion()- model: {model}; kwargs: {kwargs}"
)
parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
deployment = await self.async_get_available_deployment(
model=model,
messages=[{"role": "user", "content": "default text"}],
specific_deployment=kwargs.pop("specific_deployment", None),
request_kwargs=kwargs,
)
self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
data = deployment["litellm_params"].copy()
model_name = data["model"]
model_client = self._get_async_openai_model_client(
deployment=deployment,
kwargs=kwargs,
)
self.total_calls[model_name] += 1
response = litellm.aadapter_completion(
**{
**data,
"adapter_id": adapter_id,
"caching": self.cache_responses,
"client": model_client,
**kwargs,
}
)
rpm_semaphore = self._get_client(
deployment=deployment,
kwargs=kwargs,
client_type="max_parallel_requests",
)
if rpm_semaphore is not None and isinstance(
rpm_semaphore, asyncio.Semaphore
):
async with rpm_semaphore:
"""
- Check rpm limits before making the call
- If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe)
"""
await self.async_routing_strategy_pre_call_checks(
deployment=deployment, parent_otel_span=parent_otel_span
)
response = await response # type: ignore
else:
await self.async_routing_strategy_pre_call_checks(
deployment=deployment, parent_otel_span=parent_otel_span
)
response = await response # type: ignore
self.success_calls[model_name] += 1
verbose_router_logger.info(
f"litellm.aadapter_completion(model={model_name})\033[32m 200 OK\033[0m"
)
return response
except Exception as e:
verbose_router_logger.info(
f"litellm.aadapter_completion(model={model})\033[31m Exception {str(e)}\033[0m"
)
if model is not None:
self.fail_calls[model] += 1
raise e
async def _asearch_with_fallbacks(self, original_function: Callable, **kwargs):
"""
Helper function to make a search API call through the router with load balancing and fallbacks.
Reuses the router's retry/fallback infrastructure.
"""
from litellm.router_utils.search_api_router import SearchAPIRouter
return await SearchAPIRouter.async_search_with_fallbacks(
router_instance=self,
original_function=original_function,
**kwargs,
)
async def _asearch_with_fallbacks_helper(
self, model: str, original_generic_function: Callable, **kwargs
):
"""
Helper function for search API calls - selects a search tool and calls the original function.
Called by async_function_with_fallbacks for each retry attempt.
"""
from litellm.router_utils.search_api_router import SearchAPIRouter
return await SearchAPIRouter.async_search_with_fallbacks_helper(
router_instance=self,
model=model,
original_generic_function=original_generic_function,
**kwargs,
)
async def aguardrail(
self,
guardrail_name: str,
original_function: Callable,
**kwargs,
):
"""
Execute a guardrail with load balancing and fallbacks.
Args:
guardrail_name: Name of the guardrail to execute
original_function: The guardrail's execution function (e.g., async_pre_call_hook)
**kwargs: Additional arguments passed to the guardrail
Returns:
Result from the guardrail execution
"""
kwargs["model"] = guardrail_name # For fallback system compatibility
kwargs["original_generic_function"] = original_function
kwargs["original_function"] = self._aguardrail_helper
self._update_kwargs_before_fallbacks(
model=guardrail_name,
kwargs=kwargs,
metadata_variable_name="litellm_metadata",
)
verbose_router_logger.debug(
f"Inside aguardrail() - guardrail_name: {guardrail_name}; kwargs: {kwargs}"
)
response = await self.async_function_with_fallbacks(**kwargs)
return response
async def _aguardrail_helper(
self,
model: str,
original_generic_function: Callable,
**kwargs,
):
"""
Helper for aguardrail - selects a guardrail deployment and executes it.
Called by async_function_with_fallbacks for each retry attempt.
Args:
model: The guardrail_name (named 'model' for fallback system compatibility)
original_generic_function: The guardrail's execution function
**kwargs: Additional arguments
"""
guardrail_name = model
selected_guardrail = self.get_available_guardrail(
guardrail_name=guardrail_name,
)
verbose_router_logger.debug(
f"Selected guardrail deployment: {selected_guardrail.get('litellm_params', {}).get('guardrail')}"
)
# Pass the selected guardrail config to the original function
kwargs["selected_guardrail"] = selected_guardrail
response = await original_generic_function(**kwargs)
return response
def get_available_guardrail(
self,
guardrail_name: str,
) -> "GuardrailTypedDict":
"""
Select a guardrail deployment using the router's load balancing strategy.
Args:
guardrail_name: Name of the guardrail to select
Returns:
Selected guardrail configuration dict
"""
from litellm.router_strategy.simple_shuffle import simple_shuffle
healthy_deployments = [
g for g in self.guardrail_list if g.get("guardrail_name") == guardrail_name
]
if not healthy_deployments:
raise ValueError(f"No guardrail found with name: {guardrail_name}")
if len(healthy_deployments) == 1:
return healthy_deployments[0]
# Use simple_shuffle for weighted selection
return cast(
GuardrailTypedDict,
simple_shuffle(
llm_router_instance=self,
healthy_deployments=healthy_deployments,
model=guardrail_name,
),
)
async def _ageneric_api_call_with_fallbacks(
self, model: str, original_function: Callable, **kwargs
):
"""
Helper function to make a generic LLM API call through the router, this allows you to use retries/fallbacks with litellm router
"""
try:
kwargs["model"] = model
kwargs["original_generic_function"] = original_function
kwargs["original_function"] = self._ageneric_api_call_with_fallbacks_helper
self._update_kwargs_before_fallbacks(
model=model, kwargs=kwargs, metadata_variable_name="litellm_metadata"
)
verbose_router_logger.debug(
f"Inside ageneric_api_call_with_fallbacks() - model: {model}; kwargs: {kwargs}"
)
response = await self.async_function_with_fallbacks(**kwargs)
return response
return response
except Exception as e:
asyncio.create_task(
send_llm_exception_alert(
litellm_router_instance=self,
request_kwargs=kwargs,
error_traceback_str=traceback.format_exc(),
original_exception=e,
)
)
raise e
def _add_deployment_model_to_endpoint_for_llm_passthrough_route(
self, kwargs: Dict[str, Any], model: str, model_name: str
) -> Dict[str, Any]:
"""
Add the deployment model to the endpoint for LLM passthrough route.
e.g for bedrock invoke users can pass endpoint as /model/special-bedrock-model/invoke
it should be actually sent as /model/us.anthropic.claude-3-5-sonnet-20240620-v1:0/invoke
"""
if "endpoint" in kwargs and kwargs["endpoint"]:
# For provider-specific endpoints, strip the provider prefix from model_name
# e.g., "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0" -> "us.anthropic.claude-3-5-sonnet-20240620-v1:0"
from litellm import get_llm_provider
try:
# get_llm_provider returns (model_without_prefix, provider, api_key, api_base)
stripped_model_name, _, _, _ = get_llm_provider(
model=model_name,
custom_llm_provider=kwargs.get("custom_llm_provider"),
api_base=kwargs.get("api_base"),
)
replacement_model_name = stripped_model_name
except Exception:
# If get_llm_provider fails, fall back to using model_name as-is
replacement_model_name = model_name
kwargs["endpoint"] = kwargs["endpoint"].replace(
model, replacement_model_name
)
return kwargs
async def _ageneric_api_call_with_fallbacks_helper(
self, model: str, original_generic_function: Callable, **kwargs
):
"""
Helper function to make a generic LLM API call through the router, this allows you to use retries/fallbacks with litellm router
"""
passthrough_on_no_deployment = kwargs.pop("passthrough_on_no_deployment", False)
function_name = "_ageneric_api_call_with_fallbacks"
try:
parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
try:
deployment = await self.async_get_available_deployment(
model=model,
request_kwargs=kwargs,
messages=kwargs.get("messages", None),
specific_deployment=kwargs.pop("specific_deployment", None),
)
except Exception as e:
if passthrough_on_no_deployment:
return await original_generic_function(model=model, **kwargs)
raise e
self._update_kwargs_with_deployment(
deployment=deployment, kwargs=kwargs, function_name=function_name
)
data = deployment["litellm_params"].copy()
model_name = data["model"]
self.total_calls[model_name] += 1
self._add_deployment_model_to_endpoint_for_llm_passthrough_route(
kwargs=kwargs, model=model, model_name=model_name
)
### get custom
response = original_generic_function(
**{
**data,
"caching": self.cache_responses,
**kwargs,
}
)
rpm_semaphore = self._get_client(
deployment=deployment,
kwargs=kwargs,
client_type="max_parallel_requests",
)
if rpm_semaphore is not None and isinstance(
rpm_semaphore, asyncio.Semaphore
):
async with rpm_semaphore:
"""
- Check rpm limits before making the call
- If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe)
"""
await self.async_routing_strategy_pre_call_checks(
deployment=deployment, parent_otel_span=parent_otel_span
)
response = await response # type: ignore
else:
await self.async_routing_strategy_pre_call_checks(
deployment=deployment, parent_otel_span=parent_otel_span
)
response = await response # type: ignore
self.success_calls[model_name] += 1
verbose_router_logger.info(
f"ageneric_api_call_with_fallbacks(model={model_name})\033[32m 200 OK\033[0m"
)
return response
except Exception as e:
verbose_router_logger.info(
f"ageneric_api_call_with_fallbacks(model={model})\033[31m Exception {str(e)}\033[0m"
)
if model is not None:
self.fail_calls[model] += 1
raise e
def _generic_api_call_with_fallbacks(
self, model: str, original_function: Callable, **kwargs
):
"""
Make a generic LLM API call through the router, this allows you to use retries/fallbacks with litellm router
Args:
model: The model to use
original_function: The handler function to call (e.g., litellm.completion)
**kwargs: Additional arguments to pass to the handler function
Returns:
The response from the handler function
"""
handler_name = original_function.__name__
try:
verbose_router_logger.debug(
f"Inside _generic_api_call() - handler: {handler_name}, model: {model}; kwargs: {kwargs}"
)
deployment = self.get_available_deployment(
model=model,
messages=kwargs.get("messages", None),
specific_deployment=kwargs.pop("specific_deployment", None),
)
self._update_kwargs_with_deployment(
deployment=deployment, kwargs=kwargs, function_name="generic_api_call"
)
data = deployment["litellm_params"].copy()
model_name = data["model"]
self.total_calls[model_name] += 1
# For passthrough routes, use the actual model from deployment
# and swap model name in endpoint if present
if "endpoint" in kwargs and kwargs["endpoint"]:
kwargs["endpoint"] = kwargs["endpoint"].replace(model, model_name)
kwargs["model"] = model_name
# Perform pre-call checks for routing strategy
self.routing_strategy_pre_call_checks(deployment=deployment)
try:
_, custom_llm_provider, _, _ = get_llm_provider(model=data["model"])
except Exception:
custom_llm_provider = None
response = original_function(
**{
**data,
"custom_llm_provider": custom_llm_provider,
"caching": self.cache_responses,
**kwargs,
}
)
self.success_calls[model_name] += 1
verbose_router_logger.info(
f"{handler_name}(model={model_name})\033[32m 200 OK\033[0m"
)
return response
except Exception as e:
verbose_router_logger.info(
f"{handler_name}(model={model})\033[31m Exception {str(e)}\033[0m"
)
if model is not None:
self.fail_calls[model] += 1
raise e
def embedding(
self,
model: str,
input: Union[str, List],
is_async: Optional[bool] = False,
**kwargs,
) -> EmbeddingResponse:
try:
kwargs["model"] = model
kwargs["input"] = input
kwargs["original_function"] = self._embedding
self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
response = self.function_with_fallbacks(**kwargs)
return response
except Exception as e:
raise e
def _embedding(self, input: Union[str, List], model: str, **kwargs):
model_name = None
try:
verbose_router_logger.debug(
f"Inside embedding()- model: {model}; kwargs: {kwargs}"
)
deployment = self.get_available_deployment(
model=model,
input=input,
specific_deployment=kwargs.pop("specific_deployment", None),
)
self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
data = deployment["litellm_params"].copy()
model_name = data["model"]
potential_model_client = self._get_client(
deployment=deployment, kwargs=kwargs, client_type="sync"
)
# check if provided keys == client keys #
dynamic_api_key = kwargs.get("api_key", None)
if (
dynamic_api_key is not None
and potential_model_client is not None
and dynamic_api_key != potential_model_client.api_key
):
model_client = None
else:
model_client = potential_model_client
self.total_calls[model_name] += 1
### DEPLOYMENT-SPECIFIC PRE-CALL CHECKS ### (e.g. update rpm pre-call. Raise error, if deployment over limit)
self.routing_strategy_pre_call_checks(deployment=deployment)
response = litellm.embedding(
**{
**data,
"input": input,
"caching": self.cache_responses,
"client": model_client,
**kwargs,
}
)
self.success_calls[model_name] += 1
verbose_router_logger.info(
f"litellm.embedding(model={model_name})\033[32m 200 OK\033[0m"
)
return response
except Exception as e:
verbose_router_logger.info(
f"litellm.embedding(model={model_name})\033[31m Exception {str(e)}\033[0m"
)
if model_name is not None:
self.fail_calls[model_name] += 1
raise e
async def aembedding(
self,
model: str,
input: Union[str, List],
is_async: Optional[bool] = True,
**kwargs,
) -> EmbeddingResponse:
try:
kwargs["model"] = model
kwargs["input"] = input
kwargs["original_function"] = self._aembedding
self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
response = await self.async_function_with_fallbacks(**kwargs)
return response
except Exception as e:
asyncio.create_task(
send_llm_exception_alert(
litellm_router_instance=self,
request_kwargs=kwargs,
error_traceback_str=traceback.format_exc(),
original_exception=e,
)
)
raise e
async def _aembedding(self, input: Union[str, List], model: str, **kwargs):
model_name = None
try:
verbose_router_logger.debug(
f"Inside _aembedding()- model: {model}; kwargs: {kwargs}"
)
parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
deployment = await self.async_get_available_deployment(
model=model,
input=input,
specific_deployment=kwargs.pop("specific_deployment", None),
request_kwargs=kwargs,
)
self._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
data = deployment["litellm_params"].copy()
model_name = data["model"]
model_client = self._get_async_openai_model_client(
deployment=deployment,
kwargs=kwargs,
)
self.total_calls[model_name] += 1
response = litellm.aembedding(
**{
**data,
"input": input,
"caching": self.cache_responses,
"client": model_client,
**kwargs,
}
)
### CONCURRENCY-SAFE RPM CHECKS ###
rpm_semaphore = self._get_client(
deployment=deployment,
kwargs=kwargs,
client_type="max_parallel_requests",
)
if rpm_semaphore is not None and isinstance(
rpm_semaphore, asyncio.Semaphore
):
async with rpm_semaphore:
"""
- Check rpm limits before making the call
- If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe)
"""
await self.async_routing_strategy_pre_call_checks(
deployment=deployment, parent_otel_span=parent_otel_span
)
response = await response
else:
await self.async_routing_strategy_pre_call_checks(
deployment=deployment, parent_otel_span=parent_otel_span
)
response = await response
self.success_calls[model_name] += 1
verbose_router_logger.info(
f"litellm.aembedding(model={model_name})\033[32m 200 OK\033[0m"
)
return response
except Exception as e:
verbose_router_logger.info(
f"litellm.aembedding(model={model_name})\033[31m Exception {str(e)}\033[0m"
)
if model_name is not None:
self.fail_calls[model_name] += 1
raise e
#### FILES API ####
async def acreate_file(
self,
model: str,
**kwargs,
) -> OpenAIFileObject:
try:
kwargs["model"] = model
kwargs["original_function"] = self._acreate_file
kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs)
response = await self.async_function_with_fallbacks(**kwargs)
return response
except Exception as e:
asyncio.create_task(
send_llm_exception_alert(
litellm_router_instance=self,
request_kwargs=kwargs,
error_traceback_str=traceback.format_exc(),
original_exception=e,
)
)
raise e
async def _acreate_file(
self,
model: str,
**kwargs,
) -> OpenAIFileObject:
try:
from litellm.router_utils.common_utils import add_model_file_id_mappings
verbose_router_logger.debug(
f"Inside _atext_completion()- model: {model}; kwargs: {kwargs}"
)
parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
healthy_deployments = await self.async_get_healthy_deployments(
model=model,
messages=[{"role": "user", "content": "files-api-fake-text"}],
specific_deployment=kwargs.pop("specific_deployment", None),
request_kwargs=kwargs,
parent_otel_span=parent_otel_span,
)
async def create_file_for_deployment(deployment: dict) -> OpenAIFileObject:
from litellm.litellm_core_utils.core_helpers import safe_deep_copy
kwargs_copy = safe_deep_copy(kwargs)
self._update_kwargs_with_deployment(
deployment=deployment,
kwargs=kwargs_copy,
function_name="acreate_file",
)
data = deployment["litellm_params"].copy()
model_name = data["model"]
model_client = self._get_async_openai_model_client(
deployment=deployment,
kwargs=kwargs_copy,
)
self.total_calls[model_name] += 1
## REPLACE MODEL IN FILE WITH SELECTED DEPLOYMENT ##
stripped_model, custom_llm_provider, _, _ = get_llm_provider(
model=data["model"]
)
## REPLACE MODEL IN FILE WITH SELECTED DEPLOYMENT ##
purpose = cast(Optional[OpenAIFilesPurpose], kwargs.get("purpose"))
file = cast(Optional[FileTypes], kwargs.get("file"))
if not file or not purpose:
raise Exception(
"file and file_purpose are required for create_file"
)
replace_model_in_jsonl_bool = should_replace_model_in_jsonl(
purpose=purpose,
)
if replace_model_in_jsonl_bool:
file = replace_model_in_jsonl(
file_content=file,
new_model_name=stripped_model,
)
kwargs_copy["file"] = file
response = litellm.acreate_file(
**{
**data,
"custom_llm_provider": custom_llm_provider,
"caching": self.cache_responses,
"client": model_client,
**kwargs_copy,
}
)
rpm_semaphore = self._get_client(
deployment=deployment,
kwargs=kwargs_copy,
client_type="max_parallel_requests",
)
if rpm_semaphore is not None and isinstance(
rpm_semaphore, asyncio.Semaphore
):
async with rpm_semaphore:
"""
- Check rpm limits before making the call
- If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe)
"""
await self.async_routing_strategy_pre_call_checks(
deployment=deployment, parent_otel_span=parent_otel_span
)
response = await response # type: ignore
else:
await self.async_routing_strategy_pre_call_checks(
deployment=deployment, parent_otel_span=parent_otel_span
)
response = await response # type: ignore
self.success_calls[model_name] += 1
verbose_router_logger.info(
f"litellm.acreate_file(model={model_name})\033[32m 200 OK\033[0m"
)
return response
tasks = []
if isinstance(healthy_deployments, dict):
tasks.append(create_file_for_deployment(healthy_deployments))
else:
for deployment in healthy_deployments:
tasks.append(create_file_for_deployment(deployment))
responses = await asyncio.gather(*tasks)
if len(responses) == 0:
raise Exception("No healthy deployments found.")
model_file_id_mapping = add_model_file_id_mappings(
healthy_deployments=healthy_deployments, responses=responses
)
returned_response = cast(OpenAIFileObject, responses[0])
returned_response._hidden_params[
"model_file_id_mapping"
] = model_file_id_mapping
return returned_response
except Exception as e:
verbose_router_logger.exception(
f"litellm.acreate_file(model={model}, {kwargs})\033[31m Exception {str(e)}\033[0m"
)
if model is not None:
self.fail_calls[model] += 1
raise e
async def acreate_batch(
self,
model: str,
**kwargs,
) -> LiteLLMBatch:
try:
kwargs["model"] = model
kwargs["original_function"] = self._acreate_batch
kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
metadata_variable_name = _get_router_metadata_variable_name(
function_name="_acreate_batch"
)
self._update_kwargs_before_fallbacks(
model=model,
kwargs=kwargs,
metadata_variable_name=metadata_variable_name,
)
response = await self.async_function_with_fallbacks(**kwargs)
return response
except Exception as e:
asyncio.create_task(
send_llm_exception_alert(
litellm_router_instance=self,
request_kwargs=kwargs,
error_traceback_str=traceback.format_exc(),
original_exception=e,
)
)
raise e
async def _acreate_batch(
self,
model: str,
**kwargs,
) -> LiteLLMBatch:
try:
verbose_router_logger.debug(
f"Inside _acreate_batch()- model: {model}; kwargs: {kwargs}"
)
parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
deployment = await self.async_get_available_deployment(
model=model,
messages=[{"role": "user", "content": "files-api-fake-text"}],
specific_deployment=kwargs.pop("specific_deployment", None),
request_kwargs=kwargs,
)
data = deployment["litellm_params"].copy()
model_name = data["model"]
self._update_kwargs_with_deployment(
deployment=deployment, kwargs=kwargs, function_name="_acreate_batch"
)
model_client = self._get_async_openai_model_client(
deployment=deployment,
kwargs=kwargs,
)
self.total_calls[model_name] += 1
## SET CUSTOM PROVIDER TO SELECTED DEPLOYMENT ##
_, custom_llm_provider, _, _ = get_llm_provider(model=data["model"])
response = litellm.acreate_batch(
**{
**data,
"custom_llm_provider": custom_llm_provider,
"caching": self.cache_responses,
"client": model_client,
**kwargs,
}
)
rpm_semaphore = self._get_client(
deployment=deployment,
kwargs=kwargs,
client_type="max_parallel_requests",
)
if rpm_semaphore is not None and isinstance(
rpm_semaphore, asyncio.Semaphore
):
async with rpm_semaphore:
"""
- Check rpm limits before making the call
- If allowed, increment the rpm limit (allows global value to be updated, concurrency-safe)
"""
await self.async_routing_strategy_pre_call_checks(
deployment=deployment, parent_otel_span=parent_otel_span
)
response = await response # type: ignore
else:
await self.async_routing_strategy_pre_call_checks(
deployment=deployment, parent_otel_span=parent_otel_span
)
response = await response # type: ignore
self.success_calls[model_name] += 1
verbose_router_logger.info(
f"litellm.acreate_batch(model={model_name})\033[32m 200 OK\033[0m"
)
return response # type: ignore
except Exception as e:
verbose_router_logger.exception(
f"litellm._acreate_batch(model={model}, {kwargs})\033[31m Exception {str(e)}\033[0m"
)
if model is not None:
self.fail_calls[model] += 1
raise e
async def aretrieve_batch(
self,
model: Optional[str] = None,
**kwargs,
) -> LiteLLMBatch:
"""
Iterate through all models in a model group to check for batch
Future Improvement - cache the result.
"""
try:
parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
if model is not None:
filtered_model_list: Optional[
Union[List[DeploymentTypedDict], List[Dict], Dict]
] = await self.async_get_healthy_deployments(
model=model,
messages=[{"role": "user", "content": "retrieve-api-fake-text"}],
specific_deployment=kwargs.pop("specific_deployment", None),
request_kwargs=kwargs,
parent_otel_span=parent_otel_span,
)
else:
filtered_model_list = self.get_model_list()
if filtered_model_list is None:
raise Exception("Router not yet initialized.")
receieved_exceptions = []
async def try_retrieve_batch(model_name: DeploymentTypedDict):
try:
from litellm.litellm_core_utils.core_helpers import safe_deep_copy
model = model_name["litellm_params"].get("model")
data = model_name["litellm_params"].copy()
custom_llm_provider = data.get("custom_llm_provider")
if model is None:
raise Exception(
f"Model not found in litellm_params for deployment: {model_name}"
)
# Update kwargs with the current model name or any other model-specific adjustments
## SET CUSTOM PROVIDER TO SELECTED DEPLOYMENT ##
if not custom_llm_provider:
_, custom_llm_provider, _, _ = get_llm_provider( # type: ignore
model=model
)
new_kwargs = safe_deep_copy(kwargs)
self._update_kwargs_with_deployment(
deployment=cast(dict, model_name),
kwargs=new_kwargs,
function_name="aretrieve_batch",
)
new_kwargs.pop("custom_llm_provider", None)
data.pop("custom_llm_provider", None)
return await litellm.aretrieve_batch(
**{
**data,
"custom_llm_provider": custom_llm_provider,
**new_kwargs, # type: ignore
},
)
except Exception as e:
import traceback
traceback.print_exc()
receieved_exceptions.append(e)
return None
# Check all models in parallel
if (
filtered_model_list is not None
and isinstance(filtered_model_list, list)
and len(filtered_model_list) > 0
):
results = await asyncio.gather(
*[
try_retrieve_batch(cast(DeploymentTypedDict, model))
for model in filtered_model_list
],
return_exceptions=True,
)
elif filtered_model_list is not None and isinstance(
filtered_model_list, dict
):
results = await try_retrieve_batch(
cast(DeploymentTypedDict, filtered_model_list)
)
else:
raise Exception("No healthy deployments found.")
# Check for successful responses and handle exceptions
if results is not None:
if isinstance(results, LiteLLMBatch):
return results
elif isinstance(results, list):
for result in results:
if isinstance(result, LiteLLMBatch):
return result
# If no valid Batch response was found, raise the first encountered exception
if receieved_exceptions:
raise receieved_exceptions[0] # Raising the first exception encountered
# If no exceptions were encountered, raise a generic exception
raise Exception(
"Unable to find batch in any model. Received errors - {}".format(
receieved_exceptions
)
)
except Exception as e:
asyncio.create_task(
send_llm_exception_alert(
litellm_router_instance=self,
request_kwargs=kwargs,
error_traceback_str=traceback.format_exc(),
original_exception=e,
)
)
raise e
async def alist_batches(
self,
model: str,
**kwargs,
):
"""
Return all the batches across all deployments of a model group.
"""
filtered_model_list = self.get_model_list(model_name=model)
if filtered_model_list is None:
raise Exception("Router not yet initialized.")
async def try_retrieve_batch(model: DeploymentTypedDict):
try:
# Update kwargs with the current model name or any other model-specific adjustments
return await litellm.alist_batches(
**{**model["litellm_params"], **kwargs}
)
except Exception:
return None
# Check all models in parallel
results = await asyncio.gather(
*[try_retrieve_batch(model) for model in filtered_model_list]
)
final_results: Dict = {
"object": "list",
"data": [],
"first_id": None,
"last_id": None,
"has_more": False,
}
for result in results:
if result is not None:
## check batch id
if final_results["first_id"] is None and hasattr(result, "first_id"):
final_results["first_id"] = getattr(result, "first_id")
final_results["last_id"] = getattr(result, "last_id")
final_results["data"].extend(result.data) # type: ignore
## check 'has_more'
if getattr(result, "has_more", False) is True:
final_results["has_more"] = True
return final_results
#### PASSTHROUGH API ####
async def _pass_through_moderation_endpoint_factory(
self,
original_function: Callable,
custom_llm_provider: Optional[str] = None,
**kwargs,
):
# update kwargs with model_group
self._update_kwargs_before_fallbacks(
model=kwargs.get("model", ""),
kwargs=kwargs,
)
if kwargs.get("model") and self.get_model_list(model_name=kwargs["model"]):
deployment = await self.async_get_available_deployment(
model=kwargs["model"],
request_kwargs=kwargs,
)
kwargs["model"] = deployment["litellm_params"]["model"]
data = deployment["litellm_params"].copy()
self._update_kwargs_with_deployment(
deployment=deployment,
kwargs=kwargs,
)
kwargs.update(data)
return await original_function(**kwargs)
def factory_function(
self,
original_function: Callable,
call_type: Literal[
"assistants",
"moderation",
"anthropic_messages",
"aresponses",
"acancel_responses",
"acompact_responses",
"responses",
"aget_responses",
"adelete_responses",
"afile_delete",
"afile_content",
"_arealtime",
"acancel_batch",
"acreate_fine_tuning_job",
"acancel_fine_tuning_job",
"alist_fine_tuning_jobs",
"aretrieve_fine_tuning_job",
"alist_files",
"aimage_edit",
"allm_passthrough_route",
"alist_input_items",
"agenerate_content",
"generate_content",
"agenerate_content_stream",
"generate_content_stream",
"avector_store_search",
"avector_store_create",
"avector_store_file_create",
"avector_store_file_list",
"avector_store_file_retrieve",
"avector_store_file_content",
"avector_store_file_update",
"avector_store_file_delete",
"vector_store_search",
"vector_store_create",
"vector_store_file_create",
"vector_store_file_list",
"vector_store_file_retrieve",
"vector_store_file_content",
"vector_store_file_update",
"vector_store_file_delete",
"aocr",
"ocr",
"asearch",
"search",
"aadapter_generate_content",
"avideo_generation",
"video_generation",
"avideo_list",
"video_list",
"avideo_status",
"video_status",
"avideo_content",
"video_content",
"avideo_remix",
"video_remix",
"acreate_container",
"create_container",
"alist_containers",
"list_containers",
"aretrieve_container",
"retrieve_container",
"adelete_container",
"delete_container",
"aupload_container_file",
"upload_container_file",
"alist_container_files",
"list_container_files",
"aretrieve_container_file",
"retrieve_container_file",
"adelete_container_file",
"delete_container_file",
"acreate_skill",
"alist_skills",
"aget_skill",
"adelete_skill",
"acreate_interaction",
"create_interaction",
"aget_interaction",
"get_interaction",
"adelete_interaction",
"delete_interaction",
"acancel_interaction",
"cancel_interaction",
] = "assistants",
):
"""
Creates appropriate wrapper functions for different API call types.
Returns:
- A synchronous function for synchronous call types
- An asynchronous function for asynchronous call types
"""
# Handle synchronous call types
if call_type in (
"responses",
"generate_content",
"generate_content_stream",
"vector_store_search",
"vector_store_create",
"ocr",
"search",
"video_generation",
"video_list",
"video_status",
"video_content",
"video_remix",
"create_container",
"list_containers",
"retrieve_container",
"delete_container",
):
def sync_wrapper(
custom_llm_provider: Optional[str] = None,
client: Optional[Any] = None,
**kwargs,
):
return self._generic_api_call_with_fallbacks(
original_function=original_function, **kwargs
)
return sync_wrapper
if call_type in (
"vector_store_file_create",
"vector_store_file_list",
"vector_store_file_retrieve",
"vector_store_file_content",
"vector_store_file_update",
"vector_store_file_delete",
):
def vector_store_file_sync_wrapper(
custom_llm_provider: Optional[str] = None,
client: Optional[Any] = None,
**kwargs,
):
return original_function(
custom_llm_provider=custom_llm_provider,
client=client,
**kwargs,
)
return vector_store_file_sync_wrapper
# Handle asynchronous call types
async def async_wrapper(
custom_llm_provider: Optional[str] = None,
client: Optional[Any] = None,
**kwargs,
):
if call_type == "assistants":
return await self._pass_through_assistants_endpoint_factory(
original_function=original_function,
custom_llm_provider=custom_llm_provider,
client=client,
**kwargs,
)
elif call_type == "moderation":
return await self._pass_through_moderation_endpoint_factory(
original_function=original_function, **kwargs
)
elif call_type in ("asearch", "search"):
return await self._asearch_with_fallbacks(
original_function=original_function,
**kwargs,
)
elif call_type in (
"avector_store_file_create",
"avector_store_file_list",
"avector_store_file_retrieve",
"avector_store_file_content",
"avector_store_file_update",
"avector_store_file_delete",
):
return await self._init_vector_store_api_endpoints(
original_function=original_function,
custom_llm_provider=custom_llm_provider,
**kwargs,
)
elif call_type in (
"anthropic_messages",
"aresponses",
"_arealtime",
"acreate_fine_tuning_job",
"acancel_fine_tuning_job",
"alist_fine_tuning_jobs",
"aretrieve_fine_tuning_job",
"alist_files",
"aimage_edit",
"agenerate_content",
"agenerate_content_stream",
"aocr",
"ocr",
"avideo_generation",
"avideo_list",
"avideo_status",
"avideo_content",
"avideo_remix",
"acancel_batch",
"acreate_skill",
"alist_skills",
"aget_skill",
"adelete_skill",
"acreate_interaction",
"create_interaction",
):
return await self._ageneric_api_call_with_fallbacks(
original_function=original_function,
**kwargs,
)
elif call_type in (
"acreate_container",
"alist_containers",
"aretrieve_container",
"adelete_container",
"aupload_container_file",
"alist_container_files",
"aretrieve_container_file",
"adelete_container_file",
"aretrieve_container_file_content",
):
return await self._init_containers_api_endpoints(
original_function=original_function,
custom_llm_provider=custom_llm_provider,
**kwargs,
)
elif call_type == "allm_passthrough_route":
return await self._ageneric_api_call_with_fallbacks(
original_function=original_function,
passthrough_on_no_deployment=True,
**kwargs,
)
elif call_type in (
"aget_responses",
"acancel_responses",
"acompact_responses",
"adelete_responses",
"alist_input_items",
):
return await self._init_responses_api_endpoints(
original_function=original_function,
**kwargs,
)
elif call_type in (
"avector_store_search",
"avector_store_create",
):
return await self._init_vector_store_api_endpoints(
original_function=original_function,
custom_llm_provider=custom_llm_provider,
**kwargs,
)
elif call_type in ("afile_delete", "afile_content"):
return await self._ageneric_api_call_with_fallbacks(
original_function=original_function,
custom_llm_provider=custom_llm_provider,
client=client,
**kwargs,
)
elif call_type in (
"aget_interaction",
"adelete_interaction",
"acancel_interaction",
):
return await self._init_interactions_api_endpoints(
original_function=original_function,
custom_llm_provider=custom_llm_provider,
**kwargs,
)
return async_wrapper
async def _init_vector_store_api_endpoints(
self,
original_function: Callable,
custom_llm_provider: Optional[str] = None,
**kwargs,
):
"""
Initialize the Vector Store API endpoints on the router.
"""
if custom_llm_provider and "custom_llm_provider" not in kwargs:
kwargs["custom_llm_provider"] = custom_llm_provider
return await original_function(**kwargs)
async def _init_containers_api_endpoints(
self,
original_function: Callable,
custom_llm_provider: Optional[str] = None,
**kwargs,
):
"""
Initialize the Containers API endpoints on the router.
Container operations don't need model-based routing, so we call the
original function directly with the custom_llm_provider.
"""
if custom_llm_provider and "custom_llm_provider" not in kwargs:
kwargs["custom_llm_provider"] = custom_llm_provider
return await original_function(**kwargs)
async def _init_responses_api_endpoints(
self,
original_function: Callable,
**kwargs,
):
"""
Initialize the Responses API endpoints on the router.
GET, DELETE, CANCEL Responses API Requests encode the model_id in the response_id, this function decodes the response_id and sets the model to the model_id.
"""
from litellm.responses.utils import ResponsesAPIRequestUtils
model_id = ResponsesAPIRequestUtils.get_model_id_from_response_id(
kwargs.get("response_id")
)
if model_id is not None:
kwargs["model"] = model_id
return await self._ageneric_api_call_with_fallbacks(
original_function=original_function,
**kwargs,
)
async def _init_interactions_api_endpoints(
self,
original_function: Callable,
custom_llm_provider: Optional[str] = None,
**kwargs,
):
"""
Initialize the Interactions API endpoints on the router.
GET, DELETE, CANCEL Interactions API Requests don't need model-based routing,
so we call the original function directly with the custom_llm_provider.
"""
if custom_llm_provider and "custom_llm_provider" not in kwargs:
kwargs["custom_llm_provider"] = custom_llm_provider
# Default to gemini for interactions API
if "custom_llm_provider" not in kwargs:
kwargs["custom_llm_provider"] = "gemini"
return await original_function(**kwargs)
async def _pass_through_assistants_endpoint_factory(
self,
original_function: Callable,
custom_llm_provider: Optional[str] = None,
client: Optional[AsyncOpenAI] = None,
**kwargs,
):
"""Internal helper function to pass through the assistants endpoint"""
if custom_llm_provider is None:
if self.assistants_config is not None:
custom_llm_provider = self.assistants_config["custom_llm_provider"]
kwargs.update(self.assistants_config["litellm_params"])
else:
raise Exception(
"'custom_llm_provider' must be set. Either via:\n `Router(assistants_config={'custom_llm_provider': ..})` \nor\n `router.arun_thread(custom_llm_provider=..)`"
)
return await original_function( # type: ignore
custom_llm_provider=custom_llm_provider, client=client, **kwargs
)
#### [END] ASSISTANTS API ####
async def async_function_with_fallbacks_common_utils( # noqa: PLR0915
self,
e: Exception,
disable_fallbacks: Optional[bool],
fallbacks: Optional[List],
context_window_fallbacks: Optional[List],
content_policy_fallbacks: Optional[List],
model_group: Optional[str],
args: tuple,
kwargs: dict,
):
"""
Common utilities for async_function_with_fallbacks
"""
verbose_router_logger.debug(f"Traceback{traceback.format_exc()}")
original_exception = e
fallback_model_group = None
original_model_group: Optional[str] = kwargs.get("model") # type: ignore
fallback_failure_exception_str = ""
if disable_fallbacks is True or original_model_group is None:
raise e
input_kwargs = {
"litellm_router": self,
"original_exception": original_exception,
**kwargs,
}
if "max_fallbacks" not in input_kwargs:
input_kwargs["max_fallbacks"] = self.max_fallbacks
if "fallback_depth" not in input_kwargs:
input_kwargs["fallback_depth"] = 0
try:
verbose_router_logger.info("Trying to fallback b/w models")
# check if client-side fallbacks are used (e.g. fallbacks = ["gpt-3.5-turbo", "claude-3-haiku"] or fallbacks=[{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "Hey, how's it going?"}]}]
is_non_standard_fallback_format = _check_non_standard_fallback_format(
fallbacks=fallbacks
)
if is_non_standard_fallback_format:
input_kwargs.update(
{
"fallback_model_group": fallbacks,
"original_model_group": original_model_group,
}
)
response = await run_async_fallback(
*args,
**input_kwargs,
)
return response
if isinstance(e, litellm.ContextWindowExceededError):
if context_window_fallbacks is not None:
context_window_fallback_model_group: Optional[
List[str]
] = self._get_fallback_model_group_from_fallbacks(
fallbacks=context_window_fallbacks,
model_group=model_group,
)
if context_window_fallback_model_group is None:
raise original_exception
input_kwargs.update(
{
"fallback_model_group": context_window_fallback_model_group,
"original_model_group": original_model_group,
}
)
response = await run_async_fallback(
*args,
**input_kwargs,
)
return response
else:
error_message = "model={}. context_window_fallbacks={}. fallbacks={}.\n\nSet 'context_window_fallback' - https://docs.litellm.ai/docs/routing#fallbacks".format(
model_group, context_window_fallbacks, fallbacks
)
verbose_router_logger.info(
msg="Got 'ContextWindowExceededError'. No context_window_fallback set. Defaulting \
to fallbacks, if available.{}".format(
error_message
)
)
e.message += "\n{}".format(error_message)
elif isinstance(e, litellm.ContentPolicyViolationError):
if content_policy_fallbacks is not None:
content_policy_fallback_model_group: Optional[
List[str]
] = self._get_fallback_model_group_from_fallbacks(
fallbacks=content_policy_fallbacks,
model_group=model_group,
)
if content_policy_fallback_model_group is None:
raise original_exception
input_kwargs.update(
{
"fallback_model_group": content_policy_fallback_model_group,
"original_model_group": original_model_group,
}
)
response = await run_async_fallback(
*args,
**input_kwargs,
)
return response
else:
error_message = "model={}. content_policy_fallback={}. fallbacks={}.\n\nSet 'content_policy_fallback' - https://docs.litellm.ai/docs/routing#fallbacks".format(
model_group, content_policy_fallbacks, fallbacks
)
verbose_router_logger.info(
msg="Got 'ContentPolicyViolationError'. No content_policy_fallback set. Defaulting \
to fallbacks, if available.{}".format(
error_message
)
)
e.message += "\n{}".format(error_message)
if fallbacks is not None and model_group is not None:
verbose_router_logger.debug(f"inside model fallbacks: {fallbacks}")
(
fallback_model_group,
generic_fallback_idx,
) = get_fallback_model_group(
fallbacks=fallbacks, # if fallbacks = [{"gpt-3.5-turbo": ["claude-3-haiku"]}]
model_group=cast(str, model_group),
)
## if none, check for generic fallback
if fallback_model_group is None and generic_fallback_idx is not None:
fallback_model_group = fallbacks[generic_fallback_idx]["*"]
if fallback_model_group is None:
verbose_router_logger.info(
f"No fallback model group found for original model_group={model_group}. Fallbacks={fallbacks}"
)
if hasattr(original_exception, "message"):
original_exception.message += f"No fallback model group found for original model_group={model_group}. Fallbacks={fallbacks}" # type: ignore
raise original_exception
input_kwargs.update(
{
"fallback_model_group": fallback_model_group,
"original_model_group": original_model_group,
}
)
response = await run_async_fallback(
*args,
**input_kwargs,
)
return response
except Exception as new_exception:
parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
verbose_router_logger.error(
"litellm.router.py::async_function_with_fallbacks() - Error occurred while trying to do fallbacks - {}\n{}\n\nDebug Information:\nCooldown Deployments={}".format(
str(new_exception),
traceback.format_exc(),
await _async_get_cooldown_deployments_with_debug_info(
litellm_router_instance=self,
parent_otel_span=parent_otel_span,
),
)
)
fallback_failure_exception_str = str(new_exception)
if hasattr(original_exception, "message"):
# add the available fallbacks to the exception
original_exception.message += ". Received Model Group={}\nAvailable Model Group Fallbacks={}".format( # type: ignore
model_group,
fallback_model_group,
)
if len(fallback_failure_exception_str) > 0:
original_exception.message += ( # type: ignore
"\nError doing the fallback: {}".format(
fallback_failure_exception_str
)
)
raise original_exception
@tracer.wrap()
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: Optional[str] = kwargs.get("model")
disable_fallbacks: Optional[bool] = kwargs.pop("disable_fallbacks", False)
fallbacks: Optional[List] = kwargs.get("fallbacks", self.fallbacks)
context_window_fallbacks: Optional[List] = kwargs.get(
"context_window_fallbacks", self.context_window_fallbacks
)
content_policy_fallbacks: Optional[List] = kwargs.get(
"content_policy_fallbacks", self.content_policy_fallbacks
)
mock_timeout = kwargs.pop("mock_timeout", None)
try:
self._handle_mock_testing_fallbacks(
kwargs=kwargs,
model_group=model_group,
fallbacks=fallbacks,
context_window_fallbacks=context_window_fallbacks,
content_policy_fallbacks=content_policy_fallbacks,
)
if mock_timeout is not None:
response = await self.async_function_with_retries(
*args, **kwargs, mock_timeout=mock_timeout
)
else:
response = await self.async_function_with_retries(*args, **kwargs)
verbose_router_logger.debug(f"Async Response: {response}")
response = add_fallback_headers_to_response(
response=response,
attempted_fallbacks=0,
)
return response
except Exception as e:
return await self.async_function_with_fallbacks_common_utils(
e,
disable_fallbacks,
fallbacks,
context_window_fallbacks,
content_policy_fallbacks,
model_group,
args,
kwargs,
)
def _handle_mock_testing_fallbacks(
self,
kwargs: dict,
model_group: Optional[str] = None,
fallbacks: Optional[List] = None,
context_window_fallbacks: Optional[List] = None,
content_policy_fallbacks: Optional[List] = None,
):
"""
Helper function to raise a litellm Error for mock testing purposes.
Raises:
litellm.InternalServerError: when `mock_testing_fallbacks=True` passed in request params
litellm.ContextWindowExceededError: when `mock_testing_context_fallbacks=True` passed in request params
litellm.ContentPolicyViolationError: when `mock_testing_content_policy_fallbacks=True` passed in request params
"""
mock_testing_params = MockRouterTestingParams.from_kwargs(kwargs)
if (
mock_testing_params.mock_testing_fallbacks is not None
and mock_testing_params.mock_testing_fallbacks is True
):
raise litellm.InternalServerError(
model=model_group,
llm_provider="",
message=f"This is a mock exception for model={model_group}, to trigger a fallback. Fallbacks={fallbacks}",
)
elif (
mock_testing_params.mock_testing_context_fallbacks is not None
and mock_testing_params.mock_testing_context_fallbacks is True
):
raise litellm.ContextWindowExceededError(
model=model_group,
llm_provider="",
message=f"This is a mock exception for model={model_group}, to trigger a fallback. \
Context_Window_Fallbacks={context_window_fallbacks}",
)
elif (
mock_testing_params.mock_testing_content_policy_fallbacks is not None
and mock_testing_params.mock_testing_content_policy_fallbacks is True
):
raise litellm.ContentPolicyViolationError(
model=model_group,
llm_provider="",
message=f"This is a mock exception for model={model_group}, to trigger a fallback. \
Context_Policy_Fallbacks={content_policy_fallbacks}",
)
@tracer.wrap()
async def async_function_with_retries(self, *args, **kwargs): # noqa: PLR0915
verbose_router_logger.debug("Inside async function with retries.")
original_function = kwargs.pop("original_function")
fallbacks = kwargs.pop("fallbacks", self.fallbacks)
parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
context_window_fallbacks = kwargs.pop(
"context_window_fallbacks", self.context_window_fallbacks
)
content_policy_fallbacks = kwargs.pop(
"content_policy_fallbacks", self.content_policy_fallbacks
)
model_group: Optional[str] = kwargs.get("model")
num_retries = kwargs.pop("num_retries")
## ADD MODEL GROUP SIZE TO METADATA - used for model_group_rate_limit_error tracking
_metadata: dict = kwargs.get("litellm_metadata", kwargs.get("metadata")) or {}
if "model_group" in _metadata and isinstance(_metadata["model_group"], str):
model_list = self.get_model_list(model_name=_metadata["model_group"])
if model_list is not None:
_metadata.update({"model_group_size": len(model_list)})
verbose_router_logger.debug(
f"async function w/ retries: original_function - {original_function}, num_retries - {num_retries}"
)
try:
self._handle_mock_testing_rate_limit_error(
model_group=model_group, kwargs=kwargs
)
# if the function call is successful, no exception will be raised and we'll break out of the loop
response = await self.make_call(original_function, *args, **kwargs)
response = add_retry_headers_to_response(
response=response, attempted_retries=0, max_retries=None
)
return response
except Exception as e:
current_attempt = None
original_exception = e
deployment_num_retries = getattr(e, "num_retries", None)
if deployment_num_retries is not None and isinstance(
deployment_num_retries, int
):
num_retries = deployment_num_retries
"""
Retry Logic
"""
(
_healthy_deployments,
_all_deployments,
) = await self._async_get_healthy_deployments(
model=kwargs.get("model") or "",
parent_otel_span=parent_otel_span,
)
# Check retry policy FIRST, before should_retry_this_error
# This allows retry policies to override the healthy deployments check
_retry_policy_applies = False
if (
self.retry_policy is not None
or self.model_group_retry_policy is not None
):
# get num_retries from retry policy
# Use the model_group captured at the start of the function, or get it from metadata
# kwargs.get("model") at this point is the deployment model, not the model_group
_model_group_for_retry_policy = (
model_group or _metadata.get("model_group") or kwargs.get("model")
)
_retry_policy_retries = self.get_num_retries_from_retry_policy(
exception=original_exception,
model_group=_model_group_for_retry_policy,
)
if _retry_policy_retries is not None:
num_retries = _retry_policy_retries
_retry_policy_applies = True
# raises an exception if this error should not be retries
# Skip this check if retry policy applies (retry policy takes precedence)
if not _retry_policy_applies:
self.should_retry_this_error(
error=e,
healthy_deployments=_healthy_deployments,
all_deployments=_all_deployments,
context_window_fallbacks=context_window_fallbacks,
regular_fallbacks=fallbacks,
content_policy_fallbacks=content_policy_fallbacks,
)
## LOGGING
if num_retries > 0:
kwargs = self.log_retry(kwargs=kwargs, e=original_exception)
else:
raise
verbose_router_logger.debug(
f"Retrying request with num_retries: {num_retries}"
)
# decides how long to sleep before retry
retry_after = self._time_to_sleep_before_retry(
e=original_exception,
remaining_retries=num_retries,
num_retries=num_retries,
healthy_deployments=_healthy_deployments,
all_deployments=_all_deployments,
)
await asyncio.sleep(retry_after)
for current_attempt in range(num_retries):
try:
# if the function call is successful, no exception will be raised and we'll break out of the loop
response = await self.make_call(original_function, *args, **kwargs)
if coroutine_checker.is_async_callable(
response
): # async errors are often returned as coroutines
response = await response
response = add_retry_headers_to_response(
response=response,
attempted_retries=current_attempt + 1,
max_retries=num_retries,
)
return response
except Exception as e:
## LOGGING
kwargs = self.log_retry(kwargs=kwargs, e=e)
remaining_retries = num_retries - current_attempt - 1
_model: Optional[str] = kwargs.get("model") # type: ignore
if _model is not None:
(
_healthy_deployments,
_,
) = await self._async_get_healthy_deployments(
model=_model,
parent_otel_span=parent_otel_span,
)
else:
_healthy_deployments = []
_timeout = self._time_to_sleep_before_retry(
e=original_exception,
remaining_retries=remaining_retries,
num_retries=num_retries,
healthy_deployments=_healthy_deployments,
all_deployments=_all_deployments,
)
await asyncio.sleep(_timeout)
if type(original_exception) in litellm.LITELLM_EXCEPTION_TYPES:
setattr(original_exception, "max_retries", num_retries)
# current_attempt is 0-indexed (0 to num_retries-1), so after loop completion
# it represents the last attempt index. The actual number of retries attempted
# is current_attempt + 1, which equals num_retries when all retries are exhausted.
# We've already verified num_retries > 0 before entering the loop, so current_attempt
# will always be set (never None) when we reach this point.
actual_retries_attempted = (
current_attempt + 1 if current_attempt is not None else num_retries
)
setattr(original_exception, "num_retries", actual_retries_attempted)
raise original_exception
async def make_call(self, original_function: Any, *args, **kwargs):
"""
Handler for making a call to the .completion()/.embeddings()/etc. functions.
"""
model_group = kwargs.get("model")
response = original_function(*args, **kwargs)
if coroutine_checker.is_async_callable(response) or inspect.isawaitable(
response
):
response = await response
## PROCESS RESPONSE HEADERS
response = await self.set_response_headers(
response=response, model_group=model_group
)
return response
def _handle_mock_testing_rate_limit_error(
self, kwargs: dict, model_group: Optional[str] = None
):
"""
Helper function to raise a mock litellm.RateLimitError error for testing purposes.
Raises:
litellm.RateLimitError error when `mock_testing_rate_limit_error=True` passed in request params
"""
mock_testing_rate_limit_error: Optional[bool] = kwargs.pop(
"mock_testing_rate_limit_error", None
)
available_models = self.get_model_list(model_name=model_group)
num_retries: Optional[int] = None
if available_models is not None and len(available_models) == 1:
num_retries = cast(
Optional[int], available_models[0]["litellm_params"].get("num_retries")
)
if (
mock_testing_rate_limit_error is not None
and mock_testing_rate_limit_error is True
):
verbose_router_logger.info(
f"litellm.router.py::_mock_rate_limit_error() - Raising mock RateLimitError for model={model_group}"
)
raise litellm.RateLimitError(
model=model_group,
llm_provider="",
message=f"This is a mock exception for model={model_group}, to trigger a rate limit error.",
num_retries=num_retries,
)
def should_retry_this_error(
self,
error: Exception,
healthy_deployments: Optional[List] = None,
all_deployments: Optional[List] = None,
context_window_fallbacks: Optional[List] = None,
content_policy_fallbacks: Optional[List] = None,
regular_fallbacks: Optional[List] = None,
):
"""
1. raise an exception for ContextWindowExceededError if context_window_fallbacks is not None
2. raise an exception for ContentPolicyViolationError if content_policy_fallbacks is not None
2. raise an exception for RateLimitError if
- there are no fallbacks
- there are no healthy deployments in the same model group
"""
_num_healthy_deployments = 0
if healthy_deployments is not None and isinstance(healthy_deployments, list):
_num_healthy_deployments = len(healthy_deployments)
_num_all_deployments = 0
if all_deployments is not None and isinstance(all_deployments, list):
_num_all_deployments = len(all_deployments)
### CHECK IF RATE LIMIT / CONTEXT WINDOW ERROR / CONTENT POLICY VIOLATION ERROR w/ fallbacks available / Bad Request Error
if (
isinstance(error, litellm.ContextWindowExceededError)
and context_window_fallbacks is not None
):
raise error
if (
isinstance(error, litellm.ContentPolicyViolationError)
and content_policy_fallbacks is not None
):
raise error
status_code = getattr(error, "status_code", None)
if status_code is not None and not litellm._should_retry(status_code):
# 401/403 are special cases - allow retry if multiple deployments exist (handled below)
if status_code not in (401, 403):
raise error
if isinstance(error, litellm.NotFoundError):
raise error
# Error we should only retry if there are other deployments
if isinstance(error, openai.RateLimitError):
if (
_num_healthy_deployments <= 0 # if no healthy deployments
and regular_fallbacks is not None # and fallbacks available
and len(regular_fallbacks) > 0
):
raise error # then raise the error
if isinstance(error, openai.AuthenticationError):
"""
- if other deployments available -> retry
- else -> raise error
"""
if (
_num_all_deployments <= 1
): # if there is only 1 deployment for this model group then don't retry
raise error # then raise error
# Do not retry if there are no healthy deployments
# just raise the error
if _num_healthy_deployments <= 0: # if no healthy deployments
raise error
return True
def function_with_fallbacks(self, *args, **kwargs):
"""
Sync wrapper for async_function_with_fallbacks
Wrapped to reduce code duplication and prevent bugs.
"""
return run_async_function(self.async_function_with_fallbacks, *args, **kwargs)
def _get_fallback_model_group_from_fallbacks(
self,
fallbacks: List[Dict[str, List[str]]],
model_group: Optional[str] = None,
) -> Optional[List[str]]:
"""
Returns the list of fallback models to use for a given model group
If no fallback model group is found, returns None
Example:
fallbacks = [{"gpt-3.5-turbo": ["gpt-4"]}, {"gpt-4o": ["gpt-3.5-turbo"]}]
model_group = "gpt-3.5-turbo"
returns: ["gpt-4"]
"""
if model_group is None:
return None
fallback_model_group: Optional[List[str]] = None
for item in fallbacks: # [{"gpt-3.5-turbo": ["gpt-4"]}]
if list(item.keys())[0] == model_group:
fallback_model_group = item[model_group]
break
return fallback_model_group
def _get_first_default_fallback(self) -> Optional[str]:
"""
Returns the first model from the default_fallbacks list, if it exists.
"""
if self.fallbacks is None:
return None
for fallback in self.fallbacks:
if isinstance(fallback, dict) and "*" in fallback:
default_list = fallback["*"]
if isinstance(default_list, list) and len(default_list) > 0:
return default_list[0]
return None
def _time_to_sleep_before_retry(
self,
e: Exception,
remaining_retries: int,
num_retries: int,
healthy_deployments: Optional[List] = None,
all_deployments: Optional[List] = None,
) -> Union[int, float]:
"""
Calculate back-off, then retry
It should instantly retry only when:
1. there are healthy deployments in the same model group
2. there are fallbacks for the completion call
"""
## base case - single deployment
if all_deployments is not None and len(all_deployments) == 1:
pass
elif (
healthy_deployments is not None
and isinstance(healthy_deployments, list)
and len(healthy_deployments) > 0
):
return 0
response_headers: Optional[httpx.Headers] = None
if hasattr(e, "response") and hasattr(e.response, "headers"): # type: ignore
response_headers = e.response.headers # type: ignore
if hasattr(e, "litellm_response_headers"):
response_headers = e.litellm_response_headers # type: ignore
if response_headers is not None:
timeout = litellm._calculate_retry_after(
remaining_retries=remaining_retries,
max_retries=num_retries,
response_headers=response_headers,
min_timeout=self.retry_after,
)
else:
timeout = litellm._calculate_retry_after(
remaining_retries=remaining_retries,
max_retries=num_retries,
min_timeout=self.retry_after,
)
return timeout
### HELPER FUNCTIONS
async def deployment_callback_on_success(
self,
kwargs, # kwargs to completion
completion_response, # response from completion
start_time,
end_time, # start/end time
):
"""
Track remaining tpm/rpm quota for model in model_list
"""
from litellm.types.caching import RedisPipelineIncrementOperation
try:
standard_logging_object: Optional[StandardLoggingPayload] = kwargs.get(
"standard_logging_object", None
)
if standard_logging_object is None:
raise ValueError("standard_logging_object is None")
if kwargs["litellm_params"].get("metadata") is None:
pass
else:
deployment_name = kwargs["litellm_params"]["metadata"].get(
"deployment", None
) # stable name - works for wildcard routes as well
# Get model_group and id from kwargs like the sync version does
model_group = kwargs["litellm_params"]["metadata"].get(
"model_group", None
)
model_info = kwargs["litellm_params"].get("model_info", {}) or {}
id = model_info.get("id", None)
if model_group is None or id is None:
return
elif isinstance(id, int):
id = str(id)
## get deployment info
deployment_info = self.get_deployment(model_id=id)
if deployment_info is None:
return
else:
deployment_model_info = self.get_router_model_info(
deployment=deployment_info.model_dump(),
received_model_name=model_group,
)
# get tpm/rpm from deployment info
tpm = deployment_info.get("tpm", None)
rpm = deployment_info.get("rpm", None)
## check tpm/rpm in litellm_params
tpm_litellm_params = deployment_info.litellm_params.tpm
rpm_litellm_params = deployment_info.litellm_params.rpm
## check tpm/rpm in model_info
tpm_model_info = deployment_model_info.get("tpm", None)
rpm_model_info = deployment_model_info.get("rpm", None)
# Always track deployment successes for cooldown logic, regardless of TPM/RPM limits
increment_deployment_successes_for_current_minute(
litellm_router_instance=self,
deployment_id=id,
)
## if all are none, return - no need to track current tpm/rpm usage for models with no tpm/rpm set
if (
tpm is None
and rpm is None
and tpm_litellm_params is None
and rpm_litellm_params is None
and tpm_model_info is None
and rpm_model_info is None
):
return
parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
total_tokens: float = standard_logging_object.get("total_tokens", 0)
# ------------
# Setup values
# ------------
dt = get_utc_datetime()
current_minute = dt.strftime(
"%H-%M"
) # use the same timezone regardless of system clock
tpm_key = RouterCacheEnum.TPM.value.format(
id=id, current_minute=current_minute, model=deployment_name
)
# ------------
# Update usage
# ------------
# update cache
pipeline_operations: List[RedisPipelineIncrementOperation] = []
## TPM
pipeline_operations.append(
RedisPipelineIncrementOperation(
key=tpm_key,
increment_value=total_tokens,
ttl=RoutingArgs.ttl.value,
)
)
## RPM
rpm_key = RouterCacheEnum.RPM.value.format(
id=id, current_minute=current_minute, model=deployment_name
)
pipeline_operations.append(
RedisPipelineIncrementOperation(
key=rpm_key,
increment_value=1,
ttl=RoutingArgs.ttl.value,
)
)
await self.cache.async_increment_cache_pipeline(
increment_list=pipeline_operations,
parent_otel_span=parent_otel_span,
)
return tpm_key
except Exception as e:
verbose_router_logger.debug(
"litellm.router.Router::deployment_callback_on_success(): Exception occured - {}".format(
str(e)
)
)
pass
def sync_deployment_callback_on_success(
self,
kwargs, # kwargs to completion
completion_response, # response from completion
start_time,
end_time, # start/end time
) -> Optional[str]:
"""
Tracks the number of successes for a deployment in the current minute (using in-memory cache)
Returns:
- key: str - The key used to increment the cache
- None: if no key is found
"""
id = None
if kwargs["litellm_params"].get("metadata") is None:
pass
else:
model_group = kwargs["litellm_params"]["metadata"].get("model_group", None)
model_info = kwargs["litellm_params"].get("model_info", {}) or {}
id = model_info.get("id", None)
if model_group is None or id is None:
return None
elif isinstance(id, int):
id = str(id)
if id is not None:
key = increment_deployment_successes_for_current_minute(
litellm_router_instance=self,
deployment_id=id,
)
return key
return None
def deployment_callback_on_failure(
self,
kwargs, # kwargs to completion
completion_response, # response from completion
start_time,
end_time, # start/end time
) -> bool:
"""
2 jobs:
- Tracks the number of failures for a deployment in the current minute (using in-memory cache)
- Puts the deployment in cooldown if it exceeds the allowed fails / minute
Returns:
- True if the deployment should be put in cooldown
- False if the deployment should not be put in cooldown
"""
verbose_router_logger.debug("Router: Entering 'deployment_callback_on_failure'")
try:
exception = kwargs.get("exception", None)
exception_status = getattr(exception, "status_code", "")
# Cache litellm_params to avoid repeated dict lookups
litellm_params = kwargs.get("litellm_params", {})
_model_info = litellm_params.get("model_info", {})
exception_headers = litellm.litellm_core_utils.exception_mapping_utils._get_response_headers(
original_exception=exception
)
# Determine cooldown time with priority: deployment config > response header > router default
deployment_cooldown = litellm_params.get("cooldown_time", None)
header_cooldown = None
if exception_headers is not None:
header_cooldown = litellm.utils._get_retry_after_from_exception_header(
response_headers=exception_headers
)
##############################################
# Logic to determine cooldown time
# 1. Check if a cooldown time is set in the deployment config
# 2. Check if a cooldown time is set in the response header
# 3. If no cooldown time is set, use the router default cooldown time
##############################################
if deployment_cooldown is not None and deployment_cooldown >= 0:
_time_to_cooldown = deployment_cooldown
elif header_cooldown is not None and header_cooldown >= 0:
_time_to_cooldown = header_cooldown
else:
_time_to_cooldown = self.cooldown_time
if isinstance(_model_info, dict):
deployment_id: Optional[str] = _model_info.get("id")
if deployment_id is None:
return False
increment_deployment_failures_for_current_minute(
litellm_router_instance=self,
deployment_id=deployment_id,
)
result = _set_cooldown_deployments(
litellm_router_instance=self,
exception_status=exception_status,
original_exception=exception,
deployment=deployment_id,
time_to_cooldown=_time_to_cooldown,
) # setting deployment_id in cooldown deployments
return result
else:
verbose_router_logger.debug(
"Router: Exiting 'deployment_callback_on_failure' without cooldown. No model_info found."
)
return False
except Exception as e:
raise e
async def async_deployment_callback_on_failure(
self, kwargs, completion_response: Optional[Any], start_time, end_time
):
"""
Update RPM usage for a deployment
"""
deployment_name = kwargs["litellm_params"]["metadata"].get(
"deployment", None
) # handles wildcard routes - by giving the original name sent to `litellm.completion`
model_group = kwargs["litellm_params"]["metadata"].get("model_group", None)
model_info = kwargs["litellm_params"].get("model_info", {}) or {}
id = model_info.get("id", None)
if model_group is None or id is None:
return
elif isinstance(id, int):
id = str(id)
parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs)
dt = get_utc_datetime()
current_minute = dt.strftime(
"%H-%M"
) # use the same timezone regardless of system clock
## RPM
rpm_key = RouterCacheEnum.RPM.value.format(
id=id, current_minute=current_minute, model=deployment_name
)
await self.cache.async_increment_cache(
key=rpm_key,
value=1,
parent_otel_span=parent_otel_span,
ttl=RoutingArgs.ttl.value,
)
def _get_metadata_variable_name_from_kwargs(
self, kwargs: dict
) -> Literal["metadata", "litellm_metadata"]:
"""
Helper to return what the "metadata" field should be called in the request data
- New endpoints return `litellm_metadata`
- Old endpoints return `metadata`
Context:
- LiteLLM used `metadata` as an internal field for storing metadata
- OpenAI then started using this field for their metadata
- LiteLLM is now moving to using `litellm_metadata` for our metadata
"""
return get_metadata_variable_name_from_kwargs(kwargs)
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:
_metadata_var = (
"litellm_metadata" if "litellm_metadata" in kwargs else "metadata"
)
# 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_var, "messages", "original_function"]:
previous_model[k] = v
elif k == _metadata_var and isinstance(v, dict):
previous_model[_metadata_var] = {} # type: ignore
for metadata_k, metadata_v in kwargs[_metadata_var].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_var]["previous_models"] = self.previous_models
return kwargs
except Exception as e:
raise e
def _update_usage(
self, deployment_id: str, parent_otel_span: Optional[Span]
) -> int:
"""
Update deployment rpm for that minute
Returns:
- int: request count
"""
rpm_key = deployment_id
request_count = self.cache.get_cache(
key=rpm_key, parent_otel_span=parent_otel_span, local_only=True
)
if request_count is None:
request_count = 1
self.cache.set_cache(
key=rpm_key, value=request_count, local_only=True, ttl=60
) # only store for 60s
else:
request_count += 1
self.cache.set_cache(
key=rpm_key, value=request_count, local_only=True
) # don't change existing ttl
return request_count
def _has_default_fallbacks(self) -> bool:
if self.fallbacks is None:
return False
for fallback in self.fallbacks:
if isinstance(fallback, dict):
if "*" in fallback:
return True
return False
def _should_raise_content_policy_error(
self, model: str, response: ModelResponse, kwargs: dict
) -> bool:
"""
Determines if a content policy error should be raised.
Only raised if a fallback is available.
Else, original response is returned.
"""
if response.choices and len(response.choices) > 0:
if response.choices[0].finish_reason != "content_filter":
return False
content_policy_fallbacks = kwargs.get(
"content_policy_fallbacks", self.content_policy_fallbacks
)
### ONLY RAISE ERROR IF CP FALLBACK AVAILABLE ###
if content_policy_fallbacks is not None:
fallback_model_group = None
for item in content_policy_fallbacks: # [{"gpt-3.5-turbo": ["gpt-4"]}]
if list(item.keys())[0] == model:
fallback_model_group = item[model]
break
if fallback_model_group is not None:
return True
elif self._has_default_fallbacks(): # default fallbacks set
return True
verbose_router_logger.debug(
"Content Policy Error occurred. No available fallbacks. Returning original response. model={}, content_policy_fallbacks={}".format(
model, content_policy_fallbacks
)
)
return False
def _get_healthy_deployments(self, model: str, parent_otel_span: Optional[Span]):
_all_deployments: list = []
try:
_, _all_deployments = self._common_checks_available_deployment( # type: ignore
model=model,
)
if isinstance(_all_deployments, dict):
return []
except Exception:
pass
unhealthy_deployments = _get_cooldown_deployments(
litellm_router_instance=self, parent_otel_span=parent_otel_span
)
healthy_deployments: list = []
for deployment in _all_deployments:
if deployment["model_info"]["id"] in unhealthy_deployments:
continue
else:
healthy_deployments.append(deployment)
return healthy_deployments, _all_deployments
async def _async_get_healthy_deployments(
self, model: str, parent_otel_span: Optional[Span]
) -> Tuple[List[Dict], List[Dict]]:
"""
Returns Tuple of:
- Tuple[List[Dict], List[Dict]]:
1. healthy_deployments: list of healthy deployments
2. all_deployments: list of all deployments
"""
_all_deployments: list = []
try:
_, _all_deployments = self._common_checks_available_deployment( # type: ignore
model=model,
)
if isinstance(_all_deployments, dict):
return [], _all_deployments
except Exception:
pass
unhealthy_deployments = await _async_get_cooldown_deployments(
litellm_router_instance=self, parent_otel_span=parent_otel_span
)
# Convert to set for O(1) lookup instead of O(n)
unhealthy_deployments_set = set(unhealthy_deployments)
healthy_deployments: list = []
for deployment in _all_deployments:
if deployment["model_info"]["id"] not in unhealthy_deployments_set:
healthy_deployments.append(deployment)
return healthy_deployments, _all_deployments
def routing_strategy_pre_call_checks(self, deployment: dict):
"""
Mimics 'async_routing_strategy_pre_call_checks'
Ensures consistent update rpm implementation for 'usage-based-routing-v2'
Returns:
- None
Raises:
- Rate Limit Exception - If the deployment is over it's tpm/rpm limits
"""
for _callback in litellm.callbacks:
if isinstance(_callback, CustomLogger):
_callback.pre_call_check(deployment)
async def async_routing_strategy_pre_call_checks(
self,
deployment: dict,
parent_otel_span: Optional[Span],
logging_obj: Optional[LiteLLMLogging] = None,
):
"""
For usage-based-routing-v2, enables running rpm checks before the call is made, inside the semaphore.
-> makes the calls concurrency-safe, when rpm limits are set for a deployment
Returns:
- None
Raises:
- Rate Limit Exception - If the deployment is over it's tpm/rpm limits
"""
for _callback in litellm.callbacks:
if isinstance(_callback, CustomLogger):
try:
await _callback.async_pre_call_check(deployment, parent_otel_span)
except litellm.RateLimitError as e:
## LOG FAILURE EVENT
if logging_obj is not None:
asyncio.create_task(
logging_obj.async_failure_handler(
exception=e,
traceback_exception=traceback.format_exc(),
end_time=time.time(),
)
)
## LOGGING
threading.Thread(
target=logging_obj.failure_handler,
args=(e, traceback.format_exc()),
).start() # log response
_set_cooldown_deployments(
litellm_router_instance=self,
exception_status=e.status_code,
original_exception=e,
deployment=deployment["model_info"]["id"],
time_to_cooldown=self.cooldown_time,
)
raise e
except Exception as e:
## LOG FAILURE EVENT
if logging_obj is not None:
asyncio.create_task(
logging_obj.async_failure_handler(
exception=e,
traceback_exception=traceback.format_exc(),
end_time=time.time(),
)
)
## LOGGING
threading.Thread(
target=logging_obj.failure_handler,
args=(e, traceback.format_exc()),
).start() # log response
raise e
async def async_callback_filter_deployments(
self,
model: str,
healthy_deployments: List[dict],
messages: Optional[List[AllMessageValues]],
parent_otel_span: Optional[Span],
request_kwargs: Optional[dict] = None,
logging_obj: Optional[LiteLLMLogging] = None,
):
"""
For usage-based-routing-v2, enables running rpm checks before the call is made, inside the semaphore.
-> makes the calls concurrency-safe, when rpm limits are set for a deployment
Returns:
- None
Raises:
- Rate Limit Exception - If the deployment is over it's tpm/rpm limits
"""
returned_healthy_deployments = healthy_deployments
for _callback in litellm.callbacks:
if isinstance(_callback, CustomLogger):
try:
returned_healthy_deployments = (
await _callback.async_filter_deployments(
model=model,
healthy_deployments=returned_healthy_deployments,
messages=messages,
request_kwargs=request_kwargs,
parent_otel_span=parent_otel_span,
)
)
except Exception as e:
## LOG FAILURE EVENT
if logging_obj is not None:
asyncio.create_task(
logging_obj.async_failure_handler(
exception=e,
traceback_exception=traceback.format_exc(),
end_time=time.time(),
)
)
## LOGGING
threading.Thread(
target=logging_obj.failure_handler,
args=(e, traceback.format_exc()),
).start() # log response
raise e
return returned_healthy_deployments
def _generate_model_id(self, model_group: str, litellm_params: dict):
"""
Helper function to consistently generate the same id for a deployment
- create a string from all the litellm params
- hash
- use hash as id
"""
# Optimized: Use list and join instead of string concatenation in loop
# This avoids creating many temporary string objects (O(n) vs O(n²) complexity)
parts = [model_group]
for k, v in litellm_params.items():
if isinstance(k, str):
parts.append(k)
elif isinstance(k, dict):
parts.append(json.dumps(k))
else:
parts.append(str(k))
if isinstance(v, str):
parts.append(v)
elif isinstance(v, dict):
parts.append(json.dumps(v))
else:
parts.append(str(v))
concat_str = "".join(parts)
hash_object = hashlib.sha256(concat_str.encode())
return hash_object.hexdigest()
def _create_deployment(
self,
deployment_info: dict,
_model_name: str,
_litellm_params: dict,
_model_info: dict,
) -> Optional[Deployment]:
"""
Create a deployment object and add it to the model list
If the deployment is not active for the current environment, it is ignored
Returns:
- Deployment: The deployment object
- None: If the deployment is not active for the current environment (if 'supported_environments' is set in litellm_params)
"""
try:
litellm_params: LiteLLM_Params = LiteLLM_Params(**_litellm_params)
deployment = Deployment(
**deployment_info,
model_name=_model_name,
litellm_params=litellm_params,
model_info=_model_info,
)
for field in CustomPricingLiteLLMParams.model_fields.keys():
if deployment.litellm_params.get(field) is not None:
_model_info[field] = deployment.litellm_params[field]
## REGISTER MODEL INFO IN LITELLM MODEL COST MAP
model_id = deployment.model_info.id
if model_id is not None:
litellm.register_model(
model_cost={
model_id: _model_info,
}
)
## OLD MODEL REGISTRATION ## Kept to prevent breaking changes
_model_name = deployment.litellm_params.model
if deployment.litellm_params.custom_llm_provider is not None:
_model_name = (
deployment.litellm_params.custom_llm_provider + "/" + _model_name
)
litellm.register_model(
model_cost={
_model_name: _model_info,
}
)
## Check if LLM Deployment is allowed for this deployment
if (
self.deployment_is_active_for_environment(deployment=deployment)
is not True
):
verbose_router_logger.warning(
f"Ignoring deployment {deployment.model_name} as it is not active for environment {deployment.model_info['supported_environments']}"
)
return None
deployment = self._add_deployment(deployment=deployment)
model = deployment.to_json(exclude_none=True)
self._add_model_to_list_and_index_map(
model=model, model_id=deployment.model_info.id
)
return deployment
except Exception as e:
if self.ignore_invalid_deployments:
verbose_router_logger.exception(
f"Error creating deployment: {e}, ignoring and continuing with other deployments."
)
return None
else:
raise e
def _is_auto_router_deployment(self, litellm_params: LiteLLM_Params) -> bool:
"""
Check if the deployment is an auto-router deployment.
Returns True if the litellm_params model starts with "auto_router/"
"""
if litellm_params.model.startswith("auto_router/"):
return True
return False
def init_auto_router_deployment(self, deployment: Deployment):
"""
Initialize the auto-router deployment.
This will initialize the auto-router and add it to the auto-routers dictionary.
"""
from litellm.router_strategy.auto_router.auto_router import AutoRouter
auto_router_config_path: Optional[
str
] = deployment.litellm_params.auto_router_config_path
auto_router_config: Optional[str] = deployment.litellm_params.auto_router_config
if auto_router_config_path is None and auto_router_config is None:
raise ValueError(
"auto_router_config_path or auto_router_config is required for auto-router deployments. Please set it in the litellm_params"
)
default_model: Optional[
str
] = deployment.litellm_params.auto_router_default_model
if default_model is None:
raise ValueError(
"auto_router_default_model is required for auto-router deployments. Please set it in the litellm_params"
)
embedding_model: Optional[
str
] = deployment.litellm_params.auto_router_embedding_model
if embedding_model is None:
raise ValueError(
"auto_router_embedding_model is required for auto-router deployments. Please set it in the litellm_params"
)
autor_router: AutoRouter = AutoRouter(
model_name=deployment.model_name,
auto_router_config_path=auto_router_config_path,
auto_router_config=auto_router_config,
default_model=default_model,
embedding_model=embedding_model,
litellm_router_instance=self,
)
if deployment.model_name in self.auto_routers:
raise ValueError(
f"Auto-router deployment {deployment.model_name} already exists. Please use a different model name."
)
self.auto_routers[deployment.model_name] = autor_router
def deployment_is_active_for_environment(self, deployment: Deployment) -> bool:
"""
Function to check if a llm deployment is active for a given environment. Allows using the same config.yaml across multople environments
Requires `LITELLM_ENVIRONMENT` to be set in .env. Valid values for environment:
- development
- staging
- production
Raises:
- ValueError: If LITELLM_ENVIRONMENT is not set in .env or not one of the valid values
- ValueError: If supported_environments is not set in model_info or not one of the valid values
"""
if (
deployment.model_info is None
or "supported_environments" not in deployment.model_info
or deployment.model_info["supported_environments"] is None
):
return True
litellm_environment = get_secret_str(secret_name="LITELLM_ENVIRONMENT")
if litellm_environment is None:
raise ValueError(
"Set 'supported_environments' for model but not 'LITELLM_ENVIRONMENT' set in .env"
)
if litellm_environment not in VALID_LITELLM_ENVIRONMENTS:
raise ValueError(
f"LITELLM_ENVIRONMENT must be one of {VALID_LITELLM_ENVIRONMENTS}. but set as: {litellm_environment}"
)
for _env in deployment.model_info["supported_environments"]:
if _env not in VALID_LITELLM_ENVIRONMENTS:
raise ValueError(
f"supported_environments must be one of {VALID_LITELLM_ENVIRONMENTS}. but set as: {_env} for deployment: {deployment}"
)
if litellm_environment in deployment.model_info["supported_environments"]:
return True
return False
def set_model_list(self, model_list: list):
original_model_list = copy.deepcopy(model_list)
self.model_list = []
self.model_id_to_deployment_index_map = {} # Reset the index
self.model_name_to_deployment_indices = {} # Reset the model_name index
# we add api_base/api_key each model so load balancing between azure/gpt on api_base1 and api_base2 works
for model in original_model_list:
_model_name = model.pop("model_name")
_litellm_params = model.pop("litellm_params")
## check if litellm params in os.environ
if isinstance(_litellm_params, dict):
for k, v in _litellm_params.items():
if isinstance(v, str) and v.startswith("os.environ/"):
_litellm_params[k] = get_secret(v)
_model_info: dict = model.pop("model_info", {})
# check if model info has id
if "id" not in _model_info:
_id = self._generate_model_id(_model_name, _litellm_params)
_model_info["id"] = _id
if _litellm_params.get("organization", None) is not None and isinstance(
_litellm_params["organization"], list
): # Addresses https://github.com/BerriAI/litellm/issues/3949
for org in _litellm_params["organization"]:
_litellm_params["organization"] = org
self._create_deployment(
deployment_info=model,
_model_name=_model_name,
_litellm_params=_litellm_params,
_model_info=_model_info,
)
else:
self._create_deployment(
deployment_info=model,
_model_name=_model_name,
_litellm_params=_litellm_params,
_model_info=_model_info,
)
verbose_router_logger.debug(
f"\nInitialized Model List {self.get_model_names()}"
)
self.model_names = {m["model_name"] for m in model_list}
# Note: model_name_to_deployment_indices is already built incrementally
# by _create_deployment -> _add_model_to_list_and_index_map
def _add_deployment(self, deployment: Deployment) -> Deployment:
import os
#### VALIDATE MODEL ########
# Check if this is a prompt management model before validating as LLM provider
litellm_model = deployment.litellm_params.model
is_prompt_management_model = False
if "/" in litellm_model:
split_litellm_model = litellm_model.split("/")[0]
if split_litellm_model in litellm._known_custom_logger_compatible_callbacks:
is_prompt_management_model = True
if is_prompt_management_model:
# For prompt management models, skip LLM provider validation
# The actual model will be resolved at runtime from the prompt file
_model = litellm_model
custom_llm_provider = None
dynamic_api_key = None
api_base = None
else:
# 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
),
)
# done reading model["litellm_params"]
# Check if provider is supported: either in enum or JSON-configured
if (
custom_llm_provider not in litellm.provider_list
and not JSONProviderRegistry.exists(custom_llm_provider)
):
raise Exception(f"Unsupported provider - {custom_llm_provider}")
#### 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")
# Check if user is trying to use model_name == "*"
# this is a catch all model for their specific api key
# if deployment.model_name == "*":
# if deployment.litellm_params.model == "*":
# # user wants to pass through all requests to litellm.acompletion for unknown deployments
# self.router_general_settings.pass_through_all_models = True
# else:
# self.default_deployment = deployment.to_json(exclude_none=True)
# Check if user is using provider specific wildcard routing
# example model_name = "databricks/*" or model_name = "anthropic/*"
if "*" in deployment.model_name:
# store this as a regex pattern - all deployments matching this pattern will be sent to this deployment
# Store deployment.model_name as a regex pattern
self.pattern_router.add_pattern(
deployment.model_name, deployment.to_json(exclude_none=True)
)
if deployment.model_info.id:
self.provider_default_deployment_ids.append(deployment.model_info.id)
_team_id = deployment.model_info.get("team_id")
_team_public_model_name = deployment.model_info.get("team_public_model_name")
if (
_team_id is not None
and _team_public_model_name is not None
and "*" in _team_public_model_name
):
if _team_id not in self.team_pattern_routers:
self.team_pattern_routers[_team_id] = PatternMatchRouter()
self.team_pattern_routers[_team_id].add_pattern(
_team_public_model_name, deployment.to_json(exclude_none=True)
)
# Azure GPT-Vision Enhancements, users can pass os.environ/
data_sources = deployment.litellm_params.get("dataSources", []) or []
for data_source in data_sources:
params = data_source.get("parameters", {})
for param_key in ["endpoint", "key"]:
# if endpoint or key set for Azure GPT Vision Enhancements, check if it's an env var
if param_key in params and params[param_key].startswith("os.environ/"):
env_name = params[param_key].replace("os.environ/", "")
params[param_key] = os.environ.get(env_name, "")
# # init OpenAI, Azure clients
# InitalizeOpenAISDKClient.set_client(
# litellm_router_instance=self, model=deployment.to_json(exclude_none=True)
# )
if custom_llm_provider is not None:
self._initialize_deployment_for_pass_through(
deployment=deployment,
custom_llm_provider=custom_llm_provider,
model=deployment.litellm_params.model,
)
#########################################################
# Check if this is an auto-router deployment
#########################################################
if self._is_auto_router_deployment(litellm_params=deployment.litellm_params):
self.init_auto_router_deployment(deployment=deployment)
return deployment
def _initialize_deployment_for_pass_through(
self, deployment: Deployment, custom_llm_provider: str, model: str
):
"""
Optional: Initialize deployment for pass-through endpoints if `deployment.litellm_params.use_in_pass_through` is True
Each provider uses diff .env vars for pass-through endpoints, this helper uses the deployment credentials to set the .env vars for pass-through endpoints
"""
if deployment.litellm_params.use_in_pass_through is True:
from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import (
passthrough_endpoint_router,
)
if deployment.litellm_params.litellm_credential_name is not None:
credential_values = CredentialAccessor.get_credential_values(
deployment.litellm_params.litellm_credential_name
)
else:
credential_values = {}
if custom_llm_provider == "vertex_ai":
vertex_project = (
credential_values.get("vertex_project")
or deployment.litellm_params.vertex_project
)
vertex_location = (
credential_values.get("vertex_location")
or deployment.litellm_params.vertex_location
)
vertex_credentials = (
credential_values.get("vertex_credentials")
or deployment.litellm_params.vertex_credentials
)
if vertex_project is None or vertex_location is None:
raise ValueError(
"vertex_project, and vertex_location must be set in litellm_params for pass-through endpoints."
)
passthrough_endpoint_router.add_vertex_credentials(
project_id=vertex_project,
location=vertex_location,
vertex_credentials=vertex_credentials,
)
else:
api_base = (
credential_values.get("api_base")
or deployment.litellm_params.api_base
)
api_key = (
credential_values.get("api_key")
or deployment.litellm_params.api_key
)
passthrough_endpoint_router.set_pass_through_credentials(
custom_llm_provider=custom_llm_provider,
api_base=api_base,
api_key=api_key,
)
pass
pass
def add_deployment(self, deployment: Deployment) -> Optional[Deployment]:
"""
Parameters:
- deployment: Deployment - the deployment to be added to the Router
Returns:
- The added deployment
- OR None (if deployment already exists)
"""
# check if deployment already exists
_deployment_model_id = deployment.model_info.id
if _deployment_model_id and self.has_model_id(_deployment_model_id):
return None
# add to model list
_deployment = deployment.to_json(exclude_none=True)
# initialize client
self._add_deployment(deployment=deployment)
# add to model names
self._add_model_to_list_and_index_map(
model=_deployment, model_id=deployment.model_info.id
)
self.model_names.add(deployment.model_name)
return deployment
def _update_deployment_indices_after_removal(
self, model_id: str, removal_idx: int
) -> None:
"""
Helper method to update deployment indices after a deployment has been removed from model_list.
Parameters:
- model_id: str - the id of the deployment that was removed
- removal_idx: int - the index where the deployment was removed from model_list
"""
# Update indices for all models after the removed one
for deployment_id, idx in self.model_id_to_deployment_index_map.items():
if idx > removal_idx:
self.model_id_to_deployment_index_map[deployment_id] = idx - 1
# Remove the deleted model from index
if model_id in self.model_id_to_deployment_index_map:
del self.model_id_to_deployment_index_map[model_id]
# Update model_name_to_deployment_indices
for model_name, indices in list(self.model_name_to_deployment_indices.items()):
# Remove the deleted index
if removal_idx in indices:
indices.remove(removal_idx)
# Decrement all indices greater than removal_idx
updated_indices = []
for idx in indices:
if idx > removal_idx:
updated_indices.append(idx - 1)
else:
updated_indices.append(idx)
# Update or remove the entry
if len(updated_indices) > 0:
self.model_name_to_deployment_indices[model_name] = updated_indices
else:
del self.model_name_to_deployment_indices[model_name]
def _add_model_to_list_and_index_map(
self, model: dict, model_id: Optional[str] = None
) -> None:
"""
Helper method to add a model to the model_list and update both indices.
Parameters:
- model: dict - the model to add to the list
- model_id: Optional[str] - the model ID to use for indexing. If None, will try to get from model["model_info"]["id"]
"""
idx = len(self.model_list)
self.model_list.append(model)
# Update model_id index for O(1) lookup
if model_id is not None:
self.model_id_to_deployment_index_map[model_id] = idx
elif model.get("model_info", {}).get("id") is not None:
self.model_id_to_deployment_index_map[model["model_info"]["id"]] = idx
# Update model_name index for O(1) lookup
model_name = model.get("model_name")
if model_name:
if model_name not in self.model_name_to_deployment_indices:
self.model_name_to_deployment_indices[model_name] = []
self.model_name_to_deployment_indices[model_name].append(idx)
def upsert_deployment(self, deployment: Deployment) -> Optional[Deployment]:
"""
Add or update deployment
Parameters:
- deployment: Deployment - the deployment to be added to the Router
Returns:
- The added/updated deployment
"""
try:
# check if deployment already exists
_deployment_model_id = deployment.model_info.id or ""
_deployment_on_router: Optional[Deployment] = self.get_deployment(
model_id=_deployment_model_id
)
if _deployment_on_router is not None:
# deployment with this model_id exists on the router
if deployment.litellm_params == _deployment_on_router.litellm_params:
# No need to update
return None
# if there is a new litellm param -> then update the deployment
# remove the previous deployment
removal_idx: Optional[int] = None
deployment_id = deployment.model_info.id
deployment_fast_mapping = self.model_id_to_deployment_index_map
if deployment_id in deployment_fast_mapping:
removal_idx = deployment_fast_mapping[deployment_id]
if removal_idx is not None:
self.model_list.pop(removal_idx)
self._update_deployment_indices_after_removal(
model_id=deployment_id, removal_idx=removal_idx
)
# if the model_id is not in router
self.add_deployment(deployment=deployment)
return deployment
except Exception as e:
if self.ignore_invalid_deployments:
verbose_router_logger.debug(
f"Error upserting deployment: {e}, ignoring and continuing with other deployments."
)
return None
else:
raise e
def delete_deployment(self, id: str) -> Optional[Deployment]:
"""
Parameters:
- id: str - the id of the deployment to be deleted
Returns:
- The deleted deployment
- OR None (if deleted deployment not found)
"""
deployment_idx = None
if id in self.model_id_to_deployment_index_map:
deployment_idx = self.model_id_to_deployment_index_map[id]
try:
if deployment_idx is not None:
# Pop the item from the list first
item = self.model_list.pop(deployment_idx)
self._update_deployment_indices_after_removal(
model_id=id, removal_idx=deployment_idx
)
return item
else:
return None
except Exception:
return None
def get_deployment(self, model_id: str) -> Optional[Deployment]:
"""
Returns -> Deployment or None
Raise Exception -> if model found in invalid format
"""
# Use O(1) lookup via model_id_to_deployment_index_map only
if model_id in self.model_id_to_deployment_index_map:
idx = self.model_id_to_deployment_index_map[model_id]
model = self.model_list[idx]
if isinstance(model, dict):
return Deployment(**model)
elif isinstance(model, Deployment):
return model
else:
raise Exception("Model invalid format - {}".format(type(model)))
return None
def get_deployment_credentials(self, model_id: str) -> Optional[dict]:
"""
Returns -> dict of credentials for a given model id
"""
deployment = self.get_deployment(model_id=model_id)
if deployment is None:
return None
return CredentialLiteLLMParams(
**deployment.litellm_params.model_dump(exclude_none=True)
).model_dump(exclude_none=True)
def get_deployment_by_model_group_name(
self, model_group_name: str
) -> Optional[Deployment]:
"""
Returns -> Deployment or None
Raise Exception -> if model found in invalid format
Optimized with O(1) index lookup instead of O(n) linear scan.
"""
# O(1) lookup in model_name index
if model_group_name in self.model_name_to_deployment_indices:
indices = self.model_name_to_deployment_indices[model_group_name]
if indices:
# Return first deployment for this model_name
model = self.model_list[indices[0]]
if isinstance(model, dict):
return Deployment(**model)
elif isinstance(model, Deployment):
return model
else:
raise Exception("Model Name invalid - {}".format(type(model)))
return None
def get_deployment_credentials_with_provider(
self, model_id: str
) -> Optional[Dict[str, Any]]:
"""
Get API credentials and provider info from a model name in model_list.
Useful for passthrough endpoints (files, batches, etc.) that need credentials.
This method tries to find a deployment by model_id first, and if not found,
it tries to find by model_group_name (model_name).
Args:
model_id: Model ID or model name from model_list (e.g., "gpt-4o-litellm")
Returns:
Dictionary containing api_key, api_base, custom_llm_provider, etc.
Returns None if model not found.
Example:
credentials = router.get_deployment_credentials_with_provider("gpt-4o-litellm")
# Returns: {"api_key": "sk-...", "custom_llm_provider": "openai", ...}
"""
# Try to get deployment by model_id first
deployment = self.get_deployment(model_id=model_id)
# If not found, try by model_group_name
if deployment is None:
deployment = self.get_deployment_by_model_group_name(
model_group_name=model_id
)
if deployment is None:
return None
# Get basic credentials
credentials = CredentialLiteLLMParams(
**deployment.litellm_params.model_dump(exclude_none=True)
).model_dump(exclude_none=True)
# Add custom_llm_provider
if deployment.litellm_params.custom_llm_provider:
credentials[
"custom_llm_provider"
] = deployment.litellm_params.custom_llm_provider
elif "/" in deployment.litellm_params.model:
# Extract provider from "provider/model" format
credentials["custom_llm_provider"] = deployment.litellm_params.model.split(
"/"
)[0]
else:
credentials["custom_llm_provider"] = "openai" # default
return credentials
@overload
def get_router_model_info(
self, deployment: dict, received_model_name: str, id: None = None
) -> ModelMapInfo:
pass
@overload
def get_router_model_info(
self, deployment: None, received_model_name: str, id: str
) -> ModelMapInfo:
pass
def get_router_model_info(
self,
deployment: Optional[dict],
received_model_name: str,
id: Optional[str] = None,
) -> ModelMapInfo:
"""
For a given model id, return the model info (max tokens, input cost, output cost, etc.).
Augment litellm info with additional params set in `model_info`.
For azure models, ignore the `model:`. Only set max tokens, cost values if base_model is set.
Returns
- ModelInfo - If found -> typed dict with max tokens, input cost, etc.
Raises:
- ValueError -> If model is not mapped yet
"""
if id is not None:
_deployment = self.get_deployment(model_id=id)
if _deployment is not None:
deployment = _deployment.model_dump(exclude_none=True)
if deployment is None:
raise ValueError("Deployment not found")
## GET BASE MODEL
base_model = deployment.get("model_info", {}).get("base_model", None)
if base_model is None:
base_model = deployment.get("litellm_params", {}).get("base_model", None)
model = base_model
## GET PROVIDER
_model, custom_llm_provider, _, _ = litellm.get_llm_provider(
model=deployment.get("litellm_params", {}).get("model", ""),
litellm_params=LiteLLM_Params(**deployment.get("litellm_params", {})),
)
## SET MODEL TO 'model=' - if base_model is None + not azure
if custom_llm_provider == "azure" and base_model is None:
verbose_router_logger.error(
f"Could not identify azure model '{_model}'. Set azure 'base_model' for accurate max tokens, cost tracking, etc.- https://docs.litellm.ai/docs/proxy/cost_tracking#spend-tracking-for-azure-openai-models"
)
elif custom_llm_provider != "azure":
model = _model
if "*" in model: # only call pattern_router for wildcard models
potential_models = self.pattern_router.route(received_model_name)
if potential_models is not None:
for potential_model in potential_models:
try:
if potential_model.get("model_info", {}).get(
"id"
) == deployment.get("model_info", {}).get("id"):
model = potential_model.get("litellm_params", {}).get(
"model"
)
break
except Exception:
pass
## GET LITELLM MODEL INFO - raises exception, if model is not mapped
if model is None:
# Handle case where base_model is None (e.g., Azure models without base_model set)
# Use the original model from litellm_params
model = _model
if not model.startswith("{}/".format(custom_llm_provider)):
model_info_name = "{}/{}".format(custom_llm_provider, model)
else:
model_info_name = model
model_info = litellm.get_model_info(model=model_info_name)
## CHECK USER SET MODEL INFO
user_model_info = deployment.get("model_info", {})
model_info.update(user_model_info)
return model_info
def get_model_info(self, id: str) -> Optional[dict]:
"""
For a given model id, return the model info
Returns
- dict: the model in list with 'model_name', 'litellm_params', Optional['model_info']
- None: could not find deployment in list
Optimized with O(1) index lookup instead of O(n) linear scan.
"""
# O(1) lookup via model_id_to_deployment_index_map
if id in self.model_id_to_deployment_index_map:
idx = self.model_id_to_deployment_index_map[id]
return self.model_list[idx]
return None
def get_model_group(self, id: str) -> Optional[List]:
"""
Return list of all models in the same model group as that model id
"""
model_info = self.get_model_info(id=id)
if model_info is None:
return None
model_name = model_info["model_name"]
return self.get_model_list(model_name=model_name)
def get_deployment_model_info(
self, model_id: str, model_name: str
) -> Optional[ModelInfo]:
"""
For a given model id, return the model info
1. Check if model_id is in model info
2. If not, check if litellm model name is in model info
3. If not, return None
"""
from litellm.utils import _update_dictionary
model_info: Optional[ModelInfo] = None
custom_model_info: Optional[dict] = None
litellm_model_name_model_info: Optional[ModelInfo] = None
try:
custom_model_info = litellm.model_cost.get(model_id)
except Exception:
pass
try:
litellm_model_name_model_info = litellm.get_model_info(model=model_name)
except Exception:
pass
## check for base model
try:
if custom_model_info is not None:
base_model = custom_model_info.get("base_model", None)
if base_model is not None:
## update litellm model info with base model info
base_model_info = litellm.get_model_info(model=base_model)
if base_model_info is not None:
custom_model_info = custom_model_info or {}
# Base model provides defaults, custom model info overrides
custom_model_info = _update_dictionary(
cast(dict, base_model_info),
custom_model_info,
)
except Exception:
pass
if custom_model_info is not None and litellm_model_name_model_info is not None:
model_info = cast(
ModelInfo,
_update_dictionary(
cast(dict, litellm_model_name_model_info).copy(),
custom_model_info,
),
)
elif litellm_model_name_model_info is not None:
model_info = litellm_model_name_model_info
return model_info
def _set_model_group_info( # noqa: PLR0915
self, model_group: str, user_facing_model_group_name: str
) -> Optional[ModelGroupInfo]:
"""
For a given model group name, return the combined model info
Returns:
- ModelGroupInfo if able to construct a model group
- None if error constructing model group info
"""
model_group_info: Optional[ModelGroupInfo] = None
total_tpm: Optional[int] = None
total_rpm: Optional[int] = None
configurable_clientside_auth_params: CONFIGURABLE_CLIENTSIDE_AUTH_PARAMS = None
model_list = self.get_model_list(model_name=model_group)
if model_list is None:
return None
for model in model_list:
is_match = False
if (
"model_name" in model and model["model_name"] == model_group
): # exact match
is_match = True
elif (
"model_name" in model
and self.pattern_router.route(model_group) is not None
): # wildcard model
is_match = True
if not is_match:
continue
# model in model group found #
litellm_params = LiteLLM_Params(**model["litellm_params"]) # type: ignore
# get configurable clientside auth params
configurable_clientside_auth_params = (
litellm_params.configurable_clientside_auth_params
)
# Cache nested dict access to avoid repeated temporary dict allocations
model_litellm_params = model.get("litellm_params", {})
model_info_dict = model.get("model_info", {})
# get model tpm
_deployment_tpm: Optional[int] = None
if _deployment_tpm is None:
_deployment_tpm = model.get("tpm", None) # type: ignore
if _deployment_tpm is None:
_deployment_tpm = model_litellm_params.get("tpm", None) # type: ignore
if _deployment_tpm is None:
_deployment_tpm = model_info_dict.get("tpm", None) # type: ignore
# get model rpm
_deployment_rpm: Optional[int] = None
if _deployment_rpm is None:
_deployment_rpm = model.get("rpm", None) # type: ignore
if _deployment_rpm is None:
_deployment_rpm = model_litellm_params.get("rpm", None) # type: ignore
if _deployment_rpm is None:
_deployment_rpm = model_info_dict.get("rpm", None) # type: ignore
# get model info
try:
model_id = model_info_dict.get("id", None)
if model_id is not None:
model_info = self.get_deployment_model_info(
model_id=model_id, model_name=litellm_params.model
)
else:
model_info = None
except Exception:
model_info = None
# get llm provider
litellm_model, llm_provider = "", ""
try:
litellm_model, llm_provider, _, _ = litellm.get_llm_provider(
model=litellm_params.model,
custom_llm_provider=litellm_params.custom_llm_provider,
)
except litellm.exceptions.BadRequestError as e:
verbose_router_logger.error(
"litellm.router.py::get_model_group_info() - {}".format(str(e))
)
if model_info is None:
supported_openai_params = litellm.get_supported_openai_params(
model=litellm_model, custom_llm_provider=llm_provider
)
if supported_openai_params is None:
supported_openai_params = []
# Get mode from database model_info if available, otherwise default to "chat"
db_model_info = model.get("model_info", {})
mode = db_model_info.get("mode", "chat")
model_info = ModelMapInfo(
key=model_group,
max_tokens=None,
max_input_tokens=None,
max_output_tokens=None,
input_cost_per_token=0,
output_cost_per_token=0,
litellm_provider=llm_provider,
mode=mode,
supported_openai_params=supported_openai_params,
supports_system_messages=None,
)
if model_group_info is None:
model_group_info = ModelGroupInfo( # type: ignore
**{
"model_group": user_facing_model_group_name,
"providers": [llm_provider],
**model_info,
}
)
else:
# if max_input_tokens > curr
# if max_output_tokens > curr
# if input_cost_per_token > curr
# if output_cost_per_token > curr
# supports_parallel_function_calling == True
# supports_vision == True
# supports_function_calling == True
if llm_provider not in model_group_info.providers:
model_group_info.providers.append(llm_provider)
if (
model_info.get("max_input_tokens", None) is not None
and model_info["max_input_tokens"] is not None
and (
model_group_info.max_input_tokens is None
or model_info["max_input_tokens"]
> model_group_info.max_input_tokens
)
):
model_group_info.max_input_tokens = model_info["max_input_tokens"]
if (
model_info.get("max_output_tokens", None) is not None
and model_info["max_output_tokens"] is not None
and (
model_group_info.max_output_tokens is None
or model_info["max_output_tokens"]
> model_group_info.max_output_tokens
)
):
model_group_info.max_output_tokens = model_info["max_output_tokens"]
if model_info.get("input_cost_per_token", None) is not None and (
model_group_info.input_cost_per_token is None
or model_info["input_cost_per_token"]
> model_group_info.input_cost_per_token
):
model_group_info.input_cost_per_token = model_info[
"input_cost_per_token"
]
if model_info.get("output_cost_per_token", None) is not None and (
model_group_info.output_cost_per_token is None
or model_info["output_cost_per_token"]
> model_group_info.output_cost_per_token
):
model_group_info.output_cost_per_token = model_info[
"output_cost_per_token"
]
if (
model_info.get("supports_parallel_function_calling", None)
is not None
and model_info["supports_parallel_function_calling"] is True # type: ignore
):
model_group_info.supports_parallel_function_calling = True
if (
model_info.get("supports_vision", None) is not None
and model_info["supports_vision"] is True # type: ignore
):
model_group_info.supports_vision = True
if (
model_info.get("supports_function_calling", None) is not None
and model_info["supports_function_calling"] is True # type: ignore
):
model_group_info.supports_function_calling = True
if (
model_info.get("supports_web_search", None) is not None
and model_info["supports_web_search"] is True # type: ignore
):
model_group_info.supports_web_search = True
if (
model_info.get("supports_url_context", None) is not None
and model_info["supports_url_context"] is True # type: ignore
):
model_group_info.supports_url_context = True
if (
model_info.get("supports_reasoning", None) is not None
and model_info["supports_reasoning"] is True # type: ignore
):
model_group_info.supports_reasoning = True
if (
model_info.get("supported_openai_params", None) is not None
and model_info["supported_openai_params"] is not None
):
model_group_info.supported_openai_params = model_info[
"supported_openai_params"
]
if model_info.get("tpm", None) is not None and _deployment_tpm is None:
_deployment_tpm = model_info.get("tpm")
if model_info.get("rpm", None) is not None and _deployment_rpm is None:
_deployment_rpm = model_info.get("rpm")
if _deployment_tpm is not None:
if total_tpm is None:
total_tpm = 0
total_tpm += _deployment_tpm # type: ignore
if _deployment_rpm is not None:
if total_rpm is None:
total_rpm = 0
total_rpm += _deployment_rpm # type: ignore
if model_group_info is not None:
## UPDATE WITH TOTAL TPM/RPM FOR MODEL GROUP
if total_tpm is not None:
model_group_info.tpm = total_tpm
if total_rpm is not None:
model_group_info.rpm = total_rpm
## UPDATE WITH CONFIGURABLE CLIENTSIDE AUTH PARAMS FOR MODEL GROUP
if configurable_clientside_auth_params is not None:
model_group_info.configurable_clientside_auth_params = (
configurable_clientside_auth_params
)
return model_group_info
def get_model_group_info(self, model_group: str) -> Optional[ModelGroupInfo]:
"""
For a given model group name, return the combined model info
Returns:
- ModelGroupInfo if able to construct a model group
- None if error constructing model group info or hidden model group
"""
## Check if model group alias
if model_group in self.model_group_alias:
item = self.model_group_alias[model_group]
if isinstance(item, str):
_router_model_group = item
elif isinstance(item, dict):
if item["hidden"] is True:
return None
else:
_router_model_group = item["model"]
else:
return None
return self._set_model_group_info(
model_group=_router_model_group,
user_facing_model_group_name=model_group,
)
## Check if actual model
return self._set_model_group_info(
model_group=model_group, user_facing_model_group_name=model_group
)
async def get_model_group_usage(
self, model_group: str
) -> Tuple[Optional[int], Optional[int]]:
"""
Returns current tpm/rpm usage for model group
Parameters:
- model_group: str - the received model name from the user (can be a wildcard route).
Returns:
- usage: Tuple[tpm, rpm]
"""
dt = get_utc_datetime()
current_minute = dt.strftime(
"%H-%M"
) # use the same timezone regardless of system clock
tpm_keys: List[str] = []
rpm_keys: List[str] = []
model_list = self.get_model_list(model_name=model_group)
if model_list is None: # no matching deployments
return None, None
for model in model_list:
id: Optional[str] = model.get("model_info", {}).get("id") # type: ignore
litellm_model: Optional[str] = model["litellm_params"].get(
"model"
) # USE THE MODEL SENT TO litellm.completion() - consistent with how global_router cache is written.
if id is None or litellm_model is None:
continue
tpm_keys.append(
RouterCacheEnum.TPM.value.format(
id=id,
model=litellm_model,
current_minute=current_minute,
)
)
rpm_keys.append(
RouterCacheEnum.RPM.value.format(
id=id,
model=litellm_model,
current_minute=current_minute,
)
)
combined_tpm_rpm_keys = tpm_keys + rpm_keys
combined_tpm_rpm_values = await self.cache.async_batch_get_cache(
keys=combined_tpm_rpm_keys
)
if combined_tpm_rpm_values is None:
return None, None
tpm_usage_list: Optional[List] = combined_tpm_rpm_values[: len(tpm_keys)]
rpm_usage_list: Optional[List] = combined_tpm_rpm_values[len(tpm_keys) :]
## TPM
tpm_usage: Optional[int] = None
if tpm_usage_list is not None:
for t in tpm_usage_list:
if isinstance(t, int):
if tpm_usage is None:
tpm_usage = 0
tpm_usage += t
## RPM
rpm_usage: Optional[int] = None
if rpm_usage_list is not None:
for t in rpm_usage_list:
if isinstance(t, int):
if rpm_usage is None:
rpm_usage = 0
rpm_usage += t
return tpm_usage, rpm_usage
@lru_cache(maxsize=DEFAULT_MAX_LRU_CACHE_SIZE)
def _cached_get_model_group_info(
self, model_group: str
) -> Optional[ModelGroupInfo]:
"""
Cached version of get_model_group_info, uses @lru_cache wrapper
This is a speed optimization, since set_response_headers makes a call to get_model_group_info on every request
"""
return self.get_model_group_info(model_group)
async def get_remaining_model_group_usage(self, model_group: str) -> Dict[str, int]:
model_group_info = self._cached_get_model_group_info(model_group)
if model_group_info is not None and model_group_info.tpm is not None:
tpm_limit = model_group_info.tpm
else:
tpm_limit = None
if model_group_info is not None and model_group_info.rpm is not None:
rpm_limit = model_group_info.rpm
else:
rpm_limit = None
if tpm_limit is None and rpm_limit is None:
return {}
current_tpm, current_rpm = await self.get_model_group_usage(model_group)
returned_dict = {}
if tpm_limit is not None:
returned_dict["x-ratelimit-remaining-tokens"] = tpm_limit - (
current_tpm or 0
)
returned_dict["x-ratelimit-limit-tokens"] = tpm_limit
if rpm_limit is not None:
returned_dict["x-ratelimit-remaining-requests"] = rpm_limit - (
current_rpm or 0
)
returned_dict["x-ratelimit-limit-requests"] = rpm_limit
return returned_dict
async def set_response_headers(
self, response: Any, model_group: Optional[str] = None
) -> Any:
"""
Add the most accurate rate limit headers for a given model response.
## TODO: add model group rate limit headers
# - if healthy_deployments > 1, return model group rate limit headers
# - else return the model's rate limit headers
"""
if (
isinstance(response, BaseModel)
and hasattr(response, "_hidden_params")
and isinstance(response._hidden_params, dict) # type: ignore
):
response._hidden_params.setdefault("additional_headers", {}) # type: ignore
response._hidden_params["additional_headers"][ # type: ignore
"x-litellm-model-group"
] = model_group
additional_headers = response._hidden_params["additional_headers"] # type: ignore
if (
"x-ratelimit-remaining-tokens" not in additional_headers
and "x-ratelimit-remaining-requests" not in additional_headers
and model_group is not None
):
remaining_usage = await self.get_remaining_model_group_usage(
model_group
)
for header, value in remaining_usage.items():
if value is not None:
additional_headers[header] = value
return response
def _build_model_name_index(self, model_list: list) -> None:
"""
Build model_name -> deployment indices mapping for O(1) lookups.
This index allows us to find all deployments for a given model_name in O(1) time
instead of O(n) linear scan through the entire model_list.
"""
self.model_name_to_deployment_indices.clear()
for idx, model in enumerate(model_list):
model_name = model.get("model_name")
if model_name:
if model_name not in self.model_name_to_deployment_indices:
self.model_name_to_deployment_indices[model_name] = []
self.model_name_to_deployment_indices[model_name].append(idx)
def _build_model_id_to_deployment_index_map(self, model_list: list):
"""
Build model index from model list to enable O(1) lookups immediately.
This is called during initialization to avoid the race condition where
requests arrive before model_id_to_deployment_index_map is populated.
"""
# First populate the model_list
self.model_list = []
for _, model in enumerate(model_list):
# Extract model_info from the model dict
model_info = model.get("model_info", {})
model_id = model_info.get("id")
# If no ID exists, generate one using the same logic as set_model_list
if model_id is None:
model_name = model.get("model_name", "")
litellm_params = model.get("litellm_params", {})
model_id = self._generate_model_id(model_name, litellm_params)
# Update the model_info in the original list
if "model_info" not in model:
model["model_info"] = {}
model["model_info"]["id"] = model_id
self._add_model_to_list_and_index_map(model=model, model_id=model_id)
def get_model_ids(
self, model_name: Optional[str] = None, exclude_team_models: bool = False
) -> List[str]:
"""
if 'model_name' is none, returns all.
Returns list of model id's.
Optimized with O(1) or O(k) index lookup when model_name provided,
instead of O(n) linear scan.
"""
ids = []
if model_name is not None:
# O(1) lookup in model_name index, then O(k) iteration where k = deployments for this model_name
if model_name in self.model_name_to_deployment_indices:
indices = self.model_name_to_deployment_indices[model_name]
for idx in indices:
model = self.model_list[idx]
if "model_info" in model and "id" in model["model_info"]:
if exclude_team_models and model["model_info"].get("team_id"):
continue
ids.append(model["model_info"]["id"])
else:
# When model_name is None, return all model IDs
# Use the index map keys for O(n) where n = total deployments
for model_id in self.model_id_to_deployment_index_map.keys():
idx = self.model_id_to_deployment_index_map[model_id]
model = self.model_list[idx]
if "model_info" in model and "id" in model["model_info"]:
if exclude_team_models and model["model_info"].get("team_id"):
continue
ids.append(model_id)
return ids
def has_model_id(self, candidate_id: str) -> bool:
"""
O(1) membership check for a deployment ID without allocating large lists.
Note: Call sites may pass a variable named `model` when it actually
contains a deployment ID. This helper expects the deployment ID string.
Uses the existing `model_id_to_deployment_index_map` which is kept
in sync by `_build_model_id_to_deployment_index_map` and model-list
mutation helpers.
"""
return candidate_id in self.model_id_to_deployment_index_map
def resolve_model_name_from_model_id(
self, model_id: Optional[str]
) -> Optional[str]:
"""
Resolve model_name from model_id.
This method attempts to find the correct model_name to use with the router
so that litellm_params can be automatically injected from the model config.
Strategy:
1. First, check if model_id directly matches a model_name or deployment ID
2. If not, search through router's model_list to find a match by litellm_params.model
3. Return the model_name if found, None otherwise
Args:
model_id: The model_id extracted from decoded video_id
(could be model_name or litellm_params.model value)
Returns:
model_name if found, None otherwise. If None, the request will fall through
to normal flow using environment variables.
"""
if not model_id:
return None
# Strategy 1: Check if model_id directly matches a model_name or deployment ID
if model_id in self.model_names or self.has_model_id(model_id):
return model_id
# Strategy 2: Search through router's model_list to find by litellm_params.model
all_models = self.get_model_list(model_name=None)
if not all_models:
return None
for deployment in all_models:
litellm_params = deployment.get("litellm_params", {})
actual_model = litellm_params.get("model")
# Match by exact match or by checking if actual_model ends with /model_id or :model_id
# e.g., model_id="veo-2.0-generate-001" matches actual_model="vertex_ai/veo-2.0-generate-001"
matches = (
actual_model == model_id
or (actual_model and actual_model.endswith(f"/{model_id}"))
or (actual_model and actual_model.endswith(f":{model_id}"))
)
if matches:
model_name = deployment.get("model_name")
if model_name:
return model_name
# No match found
return None
def map_team_model(self, team_model_name: str, team_id: str) -> Optional[str]:
"""
Map a team model name to a team-specific model name.
Returns:
- deployment id: str - the deployment id of the team-specific model
- None: if no team-specific model name is found
"""
models = self.get_model_list(model_name=team_model_name, team_id=team_id)
if not models:
return None
for model in models:
if model.get("model_info", {}).get("team_id") == team_id:
return model.get("model_name")
## wildcard models
return None
def should_include_deployment(
self, model_name: str, model: dict, team_id: Optional[str] = None
) -> bool:
"""
Get the team-specific model name if team_id matches the deployment.
"""
if (
team_id is not None
and model["model_info"].get("team_id") == team_id
and model_name == model["model_info"].get("team_public_model_name")
):
return True
elif model_name is not None and model["model_name"] == model_name:
return True
return False
def _get_all_deployments(
self,
model_name: str,
model_alias: Optional[str] = None,
team_id: Optional[str] = None,
) -> List[DeploymentTypedDict]:
"""
Return all deployments of a model name
Used for accurate 'get_model_list'.
if team_id specified, only return team-specific models
Optimized with O(1) index lookup instead of O(n) linear scan.
"""
returned_models: List[DeploymentTypedDict] = []
# O(1) lookup in model_name index
if model_name in self.model_name_to_deployment_indices:
indices = self.model_name_to_deployment_indices[model_name]
# O(k) where k = deployments for this model_name (typically 1-10)
for idx in indices:
model = self.model_list[idx]
if self.should_include_deployment(
model_name=model_name, model=model, team_id=team_id
):
if model_alias is not None:
# Optimized: Use shallow copy since we only modify top-level model_name
# This is much faster than deepcopy for nested dict structures
alias_model = model.copy()
alias_model["model_name"] = model_alias
returned_models.append(alias_model)
else:
returned_models.append(model)
elif team_id is not None:
# Fallback: if team_id is provided and model_name not in index,
# check if model_name matches any team_public_model_name
# O(n) scan but only when team_id lookup fails
for idx, model in enumerate(self.model_list):
if self.should_include_deployment(
model_name=model_name, model=model, team_id=team_id
):
if model_alias is not None:
# Optimized: Use shallow copy since we only modify top-level model_name
alias_model = model.copy()
alias_model["model_name"] = model_alias
returned_models.append(alias_model)
else:
returned_models.append(model)
return returned_models
def get_model_names(self, team_id: Optional[str] = None) -> List[str]:
"""
Returns all possible model names for the router, including models defined via model_group_alias.
If a team_id is provided, only deployments configured with that team_id (i.e. team‐specific models)
will yield their team public name.
"""
deployments = self.get_model_list() or []
model_names = []
for deployment in deployments:
model_info = deployment.get("model_info")
if self._is_team_specific_model(model_info):
team_model_name = self._get_team_specific_model(
deployment=deployment, team_id=team_id
)
if team_model_name:
model_names.append(team_model_name)
else:
model_names.append(deployment.get("model_name", ""))
return model_names
def _get_team_specific_model(
self, deployment: DeploymentTypedDict, team_id: Optional[str] = None
) -> Optional[str]:
"""
Get the team-specific model name if team_id matches the deployment.
Args:
deployment: DeploymentTypedDict - The model deployment
team_id: Optional[str] - If passed, will return router models set with a `team_id` matching the passed `team_id`.
Returns:
str: The `team_public_model_name` if team_id matches
None: If team_id doesn't match or no team info exists
"""
model_info: Optional[Dict] = deployment.get("model_info") or {}
if model_info is None:
return None
if team_id == model_info.get("team_id"):
return model_info.get("team_public_model_name")
return None
def _is_team_specific_model(self, model_info: Optional[Dict]) -> bool:
"""
Check if model info contains team-specific configuration.
Args:
model_info: Model information dictionary
Returns:
bool: True if model has team-specific configuration
"""
return bool(model_info and model_info.get("team_id"))
def get_model_list_from_model_alias(
self, model_name: Optional[str] = None
) -> List[DeploymentTypedDict]:
"""
Helper function to get model list from model alias.
Used by `.get_model_list` to get model list from model alias.
"""
returned_models: List[DeploymentTypedDict] = []
for model_alias, model_value in self.model_group_alias.items():
if model_name is not None and model_alias != model_name:
continue
if isinstance(model_value, str):
_router_model_name: str = model_value
elif isinstance(model_value, dict):
_model_value = RouterModelGroupAliasItem(**model_value) # type: ignore
if _model_value["hidden"] is True:
continue
else:
_router_model_name = _model_value["model"]
else:
continue
returned_models.extend(
self._get_all_deployments(
model_name=_router_model_name, model_alias=model_alias
)
)
return returned_models
def get_model_list(
self, model_name: Optional[str] = None, team_id: Optional[str] = None
) -> Optional[List[DeploymentTypedDict]]:
"""
Includes router model_group_alias'es as well
if team_id specified, returns matching team-specific models
"""
# Note: model_list and model_group_alias are always initialized in __init__
# so hasattr checks are unnecessary
returned_models: List[DeploymentTypedDict] = []
if model_name is not None:
returned_models.extend(
self._get_all_deployments(model_name=model_name, team_id=team_id)
)
returned_models.extend(
self.get_model_list_from_model_alias(model_name=model_name)
)
if len(returned_models) == 0: # check if wildcard route
potential_wildcard_models = self.pattern_router.route(model_name) or []
## check for team-specific wildcard models
if team_id is not None and team_id in self.team_pattern_routers:
potential_team_only_wildcard_models = (
self.team_pattern_routers[team_id].route(model_name) or []
)
potential_wildcard_models.extend(potential_team_only_wildcard_models)
if model_name is not None and potential_wildcard_models is not None:
for m in potential_wildcard_models:
deployment_typed_dict = DeploymentTypedDict(**m) # type: ignore
deployment_typed_dict["model_name"] = model_name
returned_models.append(deployment_typed_dict)
if model_name is None:
returned_models += self.model_list
return returned_models
def get_model_access_groups(
self,
model_name: Optional[str] = None,
model_access_group: Optional[str] = None,
team_id: Optional[str] = None,
) -> Dict[str, List[str]]:
"""
If model_name is provided, only return access groups for that model.
Parameters:
- model_name: Optional[str] - the received model name from the user (can be a wildcard route). If set, will only return access groups for that model.
- model_access_group: Optional[str] - the received model access group from the user. If set, will only return models for that access group.
- team_id: Optional[str] - the team id, to resolve team-specific models
"""
from collections import defaultdict
access_groups = defaultdict(list)
model_list = self.get_model_list(model_name=model_name, team_id=team_id)
if model_list:
for m in model_list:
_model_info = m.get("model_info")
if _model_info:
for group in _model_info.get("access_groups", []) or []:
if model_access_group is not None:
if group == model_access_group:
model_name = m["model_name"]
access_groups[group].append(model_name)
else:
model_name = m["model_name"]
access_groups[group].append(model_name)
return access_groups
def _is_model_access_group_for_wildcard_route(
self, model_access_group: str
) -> bool:
"""
Return True if model access group is a wildcard route
"""
# GET ACCESS GROUPS
access_groups = self.get_model_access_groups(
model_access_group=model_access_group
)
if len(access_groups) == 0:
return False
models = access_groups.get(model_access_group, [])
for model in models:
# CHECK IF MODEL ACCESS GROUP IS A WILDCARD ROUTE
if self.pattern_router.route(request=model) is not None:
return True
return False
def get_settings(self):
"""
Get router settings method, returns a dictionary of the settings and their values.
For example get the set values for routing_strategy_args, routing_strategy, allowed_fails, cooldown_time, num_retries, timeout, max_retries, retry_after
"""
_all_vars = vars(self)
_settings_to_return = {}
vars_to_include = [
"routing_strategy_args",
"routing_strategy",
"allowed_fails",
"cooldown_time",
"num_retries",
"timeout",
"max_retries",
"retry_after",
"fallbacks",
"context_window_fallbacks",
"model_group_retry_policy",
"retry_policy",
"model_group_alias",
]
for var in vars_to_include:
if var in _all_vars:
_settings_to_return[var] = _all_vars[var]
if (
var == "routing_strategy_args"
and self.routing_strategy == "latency-based-routing"
):
_settings_to_return[var] = self.lowestlatency_logger.routing_args.json()
return _settings_to_return
def update_settings(self, **kwargs):
"""
Update the router settings.
"""
# only the following settings are allowed to be configured
_allowed_settings = [
"routing_strategy_args",
"routing_strategy",
"allowed_fails",
"cooldown_time",
"num_retries",
"timeout",
"max_retries",
"retry_after",
"fallbacks",
"context_window_fallbacks",
"model_group_retry_policy",
"model_group_alias",
]
_int_settings = [
"timeout",
"num_retries",
"retry_after",
"allowed_fails",
"cooldown_time",
]
_existing_router_settings = self.get_settings()
for var in kwargs:
if var in _allowed_settings:
if var in _int_settings:
_casted_value = int(kwargs[var])
setattr(self, var, _casted_value)
else:
# only run routing strategy init if it has changed
if (
var == "routing_strategy"
and _existing_router_settings["routing_strategy"] != kwargs[var]
):
self.routing_strategy_init(
routing_strategy=kwargs[var],
routing_strategy_args=kwargs.get(
"routing_strategy_args", {}
),
)
setattr(self, var, kwargs[var])
else:
verbose_router_logger.debug("Setting {} is not allowed".format(var))
verbose_router_logger.debug(f"Updated Router settings: {self.get_settings()}")
def _get_client(self, deployment, kwargs, client_type=None):
"""
Returns the appropriate client based on the given deployment, kwargs, and client_type.
Parameters:
deployment (dict): The deployment dictionary containing the clients.
kwargs (dict): The keyword arguments passed to the function.
client_type (str): The type of client to return.
Returns:
The appropriate client based on the given client_type and kwargs.
"""
model_id = deployment["model_info"]["id"]
parent_otel_span: Optional[Span] = _get_parent_otel_span_from_kwargs(kwargs)
if client_type == "max_parallel_requests":
cache_key = "{}_max_parallel_requests_client".format(model_id)
client = self.cache.get_cache(
key=cache_key, local_only=True, parent_otel_span=parent_otel_span
)
if client is None:
InitalizeCachedClient.set_max_parallel_requests_client(
litellm_router_instance=self, model=deployment
)
client = self.cache.get_cache(
key=cache_key, local_only=True, parent_otel_span=parent_otel_span
)
return client
elif client_type == "async":
if kwargs.get("stream") is True:
cache_key = f"{model_id}_stream_async_client"
client = self.cache.get_cache(
key=cache_key, local_only=True, parent_otel_span=parent_otel_span
)
return client
else:
cache_key = f"{model_id}_async_client"
client = self.cache.get_cache(
key=cache_key, local_only=True, parent_otel_span=parent_otel_span
)
return client
else:
if kwargs.get("stream") is True:
cache_key = f"{model_id}_stream_client"
client = self.cache.get_cache(
key=cache_key, parent_otel_span=parent_otel_span
)
return client
else:
cache_key = f"{model_id}_client"
client = self.cache.get_cache(
key=cache_key, parent_otel_span=parent_otel_span
)
return client
def _pre_call_checks( # noqa: PLR0915
self,
model: str,
healthy_deployments: List,
messages: List[Dict[str, str]],
request_kwargs: Optional[dict] = None,
):
"""
Filter out model in model group, if:
- model context window < message length. For azure openai models, requires 'base_model' is set. - https://docs.litellm.ai/docs/proxy/cost_tracking#spend-tracking-for-azure-openai-models
- filter models above rpm limits
- if region given, filter out models not in that region / unknown region
- [TODO] function call and model doesn't support function calling
"""
verbose_router_logger.debug(
f"Starting Pre-call checks for deployments in model={model}"
)
# Optimized: Use list() shallow copy instead of deepcopy
# We only pop from the list, not modify deployment dicts - 100x+ faster on hot path (every request)
_returned_deployments = list(healthy_deployments)
invalid_model_indices = set() # Use set for O(1) membership checks
try:
input_tokens = litellm.token_counter(messages=messages)
except Exception as e:
verbose_router_logger.error(
"litellm.router.py::_pre_call_checks: failed to count tokens. Returning initial list of deployments. Got - {}".format(
str(e)
)
)
return _returned_deployments
_context_window_error = False
_potential_error_str = ""
_rate_limit_error = False
parent_otel_span = _get_parent_otel_span_from_kwargs(request_kwargs)
## get model group RPM ##
dt = get_utc_datetime()
current_minute = dt.strftime("%H-%M")
rpm_key = f"{model}:rpm:{current_minute}"
model_group_cache = (
self.cache.get_cache(
key=rpm_key, local_only=True, parent_otel_span=parent_otel_span
)
or {}
) # check the in-memory cache used by lowest_latency and usage-based routing. Only check the local cache.
for idx, deployment in enumerate(_returned_deployments):
# Cache nested dict access to avoid repeated temporary dict allocations
_litellm_params = deployment.get("litellm_params", {})
_model_info = deployment.get("model_info", {})
# see if we have the info for this model
try:
base_model = _model_info.get("base_model", None)
if base_model is None:
base_model = _litellm_params.get("base_model", None)
model_info = self.get_router_model_info(
deployment=deployment, received_model_name=model
)
model = base_model or _litellm_params.get("model", None)
if (
isinstance(model_info, dict)
and model_info.get("max_input_tokens", None) is not None
):
if (
isinstance(model_info["max_input_tokens"], int)
and input_tokens > model_info["max_input_tokens"]
):
invalid_model_indices.add(idx)
_context_window_error = True
_potential_error_str += (
"Model={}, Max Input Tokens={}, Got={}".format(
model, model_info["max_input_tokens"], input_tokens
)
)
continue
except Exception as e:
verbose_router_logger.exception("An error occurs - {}".format(str(e)))
model_id = _model_info.get("id", "")
## RPM CHECK ##
### get local router cache ###
current_request_cache_local = (
self.cache.get_cache(
key=model_id, local_only=True, parent_otel_span=parent_otel_span
)
or 0
)
### get usage based cache ###
if (
isinstance(model_group_cache, dict)
and self.routing_strategy != "usage-based-routing-v2"
):
model_group_cache[model_id] = model_group_cache.get(model_id, 0)
current_request = max(
current_request_cache_local, model_group_cache[model_id]
)
if (
isinstance(_litellm_params, dict)
and _litellm_params.get("rpm", None) is not None
):
if (
isinstance(_litellm_params["rpm"], int)
and _litellm_params["rpm"] <= current_request
):
invalid_model_indices.add(idx)
_rate_limit_error = True
continue
## REGION CHECK ##
if (
request_kwargs is not None
and request_kwargs.get("allowed_model_region") is not None
):
allowed_model_region = request_kwargs.get("allowed_model_region")
if allowed_model_region is not None:
if not is_region_allowed(
litellm_params=LiteLLM_Params(**_litellm_params),
allowed_model_region=allowed_model_region,
):
invalid_model_indices.add(idx)
continue
## INVALID PARAMS ## -> catch 'gpt-3.5-turbo-16k' not supporting 'response_format' param
if request_kwargs is not None and litellm.drop_params is False:
# get supported params
model, custom_llm_provider, _, _ = litellm.get_llm_provider(
model=model, litellm_params=LiteLLM_Params(**_litellm_params)
)
supported_openai_params = litellm.get_supported_openai_params(
model=model, custom_llm_provider=custom_llm_provider
)
if supported_openai_params is None:
continue
else:
# check the non-default openai params in request kwargs
non_default_params = litellm.utils.get_non_default_params(
passed_params=request_kwargs
)
special_params = ["response_format"]
# check if all params are supported
for k, v in non_default_params.items():
if k not in supported_openai_params and k in special_params:
# if not -> invalid model
verbose_router_logger.debug(
f"INVALID MODEL INDEX @ REQUEST KWARG FILTERING, k={k}"
)
invalid_model_indices.add(idx)
if len(invalid_model_indices) == len(_returned_deployments):
"""
- no healthy deployments available b/c context window checks or rate limit error
- First check for rate limit errors (if this is true, it means the model passed the context window check but failed the rate limit check)
"""
if _rate_limit_error is True: # allow generic fallback logic to take place
raise RouterRateLimitErrorBasic(
model=model,
)
elif _context_window_error is True:
raise litellm.ContextWindowExceededError(
message="litellm._pre_call_checks: Context Window exceeded for given call. No models have context window large enough for this call.\n{}".format(
_potential_error_str
),
model=model,
llm_provider="",
)
if len(invalid_model_indices) > 0:
# Single-pass filter using set for O(1) lookups (avoids O(n^2) from repeated pops)
_returned_deployments = [
d
for i, d in enumerate(_returned_deployments)
if i not in invalid_model_indices
]
## ORDER FILTERING ## -> if user set 'order' in deployments, return deployments with lowest order (e.g. order=1 > order=2)
if len(_returned_deployments) > 0:
_returned_deployments = litellm.utils._get_order_filtered_deployments(
_returned_deployments
)
return _returned_deployments
def _get_model_from_alias(self, model: str) -> Optional[str]:
"""
Get the model from the alias.
Returns:
- str, the litellm model name
- None, if model is not in model group alias
"""
if model not in self.model_group_alias:
return None
_item = self.model_group_alias[model]
if isinstance(_item, str):
model = _item
else:
model = _item["model"]
return model
def _get_deployment_by_litellm_model(self, model: str) -> List:
"""
Get the deployment by litellm model.
"""
return [m for m in self.model_list if m["litellm_params"]["model"] == model]
def _common_checks_available_deployment(
self,
model: str,
messages: Optional[List[Dict[str, str]]] = None,
input: Optional[Union[str, List]] = None,
specific_deployment: Optional[bool] = False,
request_kwargs: Optional[Dict] = None,
) -> Tuple[str, Union[List, Dict]]:
"""
Common checks for 'get_available_deployment' across sync + async call.
If 'healthy_deployments' returned is None, this means the user chose a specific deployment
Returns
- str, the litellm model name
- List, if multiple models chosen
- Dict, if specific model chosen
"""
request_team_id: Optional[str] = None
if request_kwargs is not None:
metadata = request_kwargs.get("metadata") or {}
litellm_metadata = request_kwargs.get("litellm_metadata") or {}
request_team_id = metadata.get(
"user_api_key_team_id"
) or litellm_metadata.get("user_api_key_team_id")
# check if aliases set on litellm model alias map
if specific_deployment is True:
return model, self._get_deployment_by_litellm_model(model=model)
elif self.has_model_id(model):
deployment = self.get_deployment(model_id=model)
if deployment is not None:
deployment_model = deployment.litellm_params.model
return deployment_model, deployment.model_dump(exclude_none=True)
raise ValueError(
f"LiteLLM Router: Trying to call specific deployment, but Model ID :{model} does not exist in Model ID map"
)
_model_from_alias = self._get_model_from_alias(model=model)
if _model_from_alias is not None:
model = _model_from_alias
if model not in self.model_names:
# check if provider/ specific wildcard routing use pattern matching
pattern_deployments = self.pattern_router.get_deployments_by_pattern(
model=model,
)
if pattern_deployments:
return model, pattern_deployments
if (
request_team_id is not None
and request_team_id in self.team_pattern_routers
):
pattern_deployments = self.team_pattern_routers[
request_team_id
].get_deployments_by_pattern(
model=model,
)
if pattern_deployments:
return model, pattern_deployments
# check if default deployment is set
if self.default_deployment is not None:
# Shallow copy with nested litellm_params copy (100x+ faster than deepcopy)
updated_deployment = self.default_deployment.copy()
updated_deployment["litellm_params"] = self.default_deployment[
"litellm_params"
].copy()
updated_deployment["litellm_params"]["model"] = model
return model, updated_deployment
## get healthy deployments
### get all deployments
healthy_deployments = self._get_all_deployments(model_name=model)
if len(healthy_deployments) == 0:
# check if the user sent in a deployment name instead
healthy_deployments = self._get_deployment_by_litellm_model(model=model)
verbose_router_logger.debug(
f"initial list of deployments: {healthy_deployments}"
)
if len(healthy_deployments) == 0:
# Check for default fallbacks if no deployments are found for the requested model
if self._has_default_fallbacks():
fallback_model = self._get_first_default_fallback()
if fallback_model:
verbose_router_logger.info(
f"Model '{model}' not found. Attempting to use default fallback model '{fallback_model}'."
)
# Re-assign model to the fallback and try to get deployments again
model = fallback_model
healthy_deployments = self._get_all_deployments(model_name=model)
# If still no deployments after checking for fallbacks, raise an error
if len(healthy_deployments) == 0:
if self.get_model_list(model_name=model) is None:
message = f"You passed in model={model}. There is no 'model_name' with this string".format(
model
)
else:
message = f"You passed in model={model}. There are no healthy deployments for this model".format(
model
)
raise litellm.BadRequestError(
message=message,
model=model,
llm_provider="",
)
if litellm.model_alias_map and model in litellm.model_alias_map:
model = litellm.model_alias_map[
model
] # update the model to the actual value if an alias has been passed in
return model, healthy_deployments
async def async_get_healthy_deployments(
self,
model: str,
request_kwargs: Dict,
messages: Optional[List[Dict[str, str]]] = None,
input: Optional[Union[str, List]] = None,
specific_deployment: Optional[bool] = False,
parent_otel_span: Optional[Span] = None,
) -> Union[List[Dict], Dict]:
"""
Get the healthy deployments for a model.
Returns:
- List[Dict], if multiple models chosen
*OR*
- Dict, if specific model chosen
"""
model, healthy_deployments = self._common_checks_available_deployment(
model=model,
messages=messages,
input=input,
specific_deployment=specific_deployment,
request_kwargs=request_kwargs,
) # type: ignore
# IF TEAM ID SPECIFIED ON MODEL, AND REQUEST CONTAINS USER_API_KEY_TEAM_ID, FILTER OUT MODELS THAT ARE NOT IN THE TEAM
## THIS PREVENTS WRITING FILES OF OTHER TEAMS TO MODELS THAT ARE TEAM-ONLY MODELS
healthy_deployments = filter_team_based_models(
healthy_deployments=healthy_deployments,
request_kwargs=request_kwargs,
)
verbose_router_logger.debug(
f"healthy_deployments after team filter: {healthy_deployments}"
)
healthy_deployments = filter_web_search_deployments(
healthy_deployments=healthy_deployments,
request_kwargs=request_kwargs,
)
verbose_router_logger.debug(
f"healthy_deployments after web search filter: {healthy_deployments}"
)
if isinstance(healthy_deployments, dict):
return healthy_deployments
cooldown_deployments = await _async_get_cooldown_deployments(
litellm_router_instance=self, parent_otel_span=parent_otel_span
)
verbose_router_logger.debug(
f"async cooldown deployments: {cooldown_deployments}"
)
verbose_router_logger.debug(f"cooldown_deployments: {cooldown_deployments}")
healthy_deployments = self._filter_cooldown_deployments(
healthy_deployments=healthy_deployments,
cooldown_deployments=cooldown_deployments,
)
healthy_deployments = await self.async_callback_filter_deployments(
model=model,
healthy_deployments=healthy_deployments,
messages=(
cast(List[AllMessageValues], messages) if messages is not None else None
),
request_kwargs=request_kwargs,
parent_otel_span=parent_otel_span,
)
if self.enable_pre_call_checks and messages is not None:
healthy_deployments = self._pre_call_checks(
model=model,
healthy_deployments=cast(List[Dict], healthy_deployments),
messages=messages,
request_kwargs=request_kwargs,
)
# check if user wants to do tag based routing
healthy_deployments = await get_deployments_for_tag( # type: ignore
llm_router_instance=self,
model=model,
request_kwargs=request_kwargs,
healthy_deployments=healthy_deployments,
metadata_variable_name=self._get_metadata_variable_name_from_kwargs(
request_kwargs
),
)
if len(healthy_deployments) == 0:
exception = await async_raise_no_deployment_exception(
litellm_router_instance=self,
model=model,
parent_otel_span=parent_otel_span,
)
raise exception
return healthy_deployments
async def async_get_available_deployment(
self,
model: str,
request_kwargs: Dict,
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"
and self.routing_strategy != "simple-shuffle"
and self.routing_strategy != "cost-based-routing"
and self.routing_strategy != "latency-based-routing"
and self.routing_strategy != "least-busy"
): # prevent regressions for other routing strategies, that don't have async get available deployments implemented.
return self.get_available_deployment(
model=model,
messages=messages,
input=input,
specific_deployment=specific_deployment,
request_kwargs=request_kwargs,
)
try:
parent_otel_span = _get_parent_otel_span_from_kwargs(request_kwargs)
#########################################################
# Execute Pre-Routing Hooks
# this hook can modify the model, messages before the routing decision is made
#########################################################
pre_routing_hook_response = await self.async_pre_routing_hook(
model=model,
request_kwargs=request_kwargs,
messages=messages,
input=input,
specific_deployment=specific_deployment,
)
if pre_routing_hook_response is not None:
model = pre_routing_hook_response.model
messages = pre_routing_hook_response.messages
#########################################################
healthy_deployments = await self.async_get_healthy_deployments(
model=model,
request_kwargs=request_kwargs,
messages=messages,
input=input,
specific_deployment=specific_deployment,
parent_otel_span=parent_otel_span,
)
if isinstance(healthy_deployments, dict):
return healthy_deployments
start_time = time.time()
if (
self.routing_strategy == "usage-based-routing-v2"
and self.lowesttpm_logger_v2 is not None
):
deployment = (
await self.lowesttpm_logger_v2.async_get_available_deployments(
model_group=model,
healthy_deployments=healthy_deployments, # type: ignore
messages=messages,
input=input,
)
)
elif (
self.routing_strategy == "cost-based-routing"
and self.lowestcost_logger is not None
):
deployment = (
await self.lowestcost_logger.async_get_available_deployments(
model_group=model,
healthy_deployments=healthy_deployments, # type: ignore
messages=messages,
input=input,
)
)
elif (
self.routing_strategy == "latency-based-routing"
and self.lowestlatency_logger is not None
):
deployment = (
await self.lowestlatency_logger.async_get_available_deployments(
model_group=model,
healthy_deployments=healthy_deployments, # type: ignore
messages=messages,
input=input,
request_kwargs=request_kwargs,
)
)
elif self.routing_strategy == "simple-shuffle":
return simple_shuffle(
llm_router_instance=self,
healthy_deployments=healthy_deployments,
model=model,
)
elif (
self.routing_strategy == "least-busy"
and self.leastbusy_logger is not None
):
deployment = (
await self.leastbusy_logger.async_get_available_deployments(
model_group=model,
healthy_deployments=healthy_deployments, # type: ignore
)
)
else:
deployment = None
if deployment is None:
exception = await async_raise_no_deployment_exception(
litellm_router_instance=self,
model=model,
parent_otel_span=parent_otel_span,
)
raise exception
verbose_router_logger.info(
f"get_available_deployment for model: {model}, Selected deployment: {self.print_deployment(deployment)} for model: {model}"
)
end_time = time.time()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_success_hook(
service=ServiceTypes.ROUTER,
duration=_duration,
call_type="<routing_strategy>.async_get_available_deployments",
parent_otel_span=parent_otel_span,
start_time=start_time,
end_time=end_time,
)
)
return deployment
except Exception as e:
traceback_exception = traceback.format_exc()
# if router rejects call -> log to langfuse/otel/etc.
if request_kwargs is not None:
logging_obj = request_kwargs.get("litellm_logging_obj", None)
if logging_obj is not None:
## LOGGING
threading.Thread(
target=logging_obj.failure_handler,
args=(e, traceback_exception),
).start() # log response
# Handle any exceptions that might occur during streaming
asyncio.create_task(
logging_obj.async_failure_handler(e, traceback_exception) # type: ignore
)
raise e
async def async_get_available_deployment_for_pass_through(
self,
model: str,
request_kwargs: Dict,
messages: Optional[List[Dict[str, str]]] = None,
input: Optional[Union[str, List]] = None,
specific_deployment: Optional[bool] = False,
):
"""
Async version of get_available_deployment_for_pass_through
Only returns deployments configured with use_in_pass_through=True
"""
try:
parent_otel_span = _get_parent_otel_span_from_kwargs(request_kwargs)
# 1. Execute pre-routing hook
pre_routing_hook_response = await self.async_pre_routing_hook(
model=model,
request_kwargs=request_kwargs,
messages=messages,
input=input,
specific_deployment=specific_deployment,
)
if pre_routing_hook_response is not None:
model = pre_routing_hook_response.model
messages = pre_routing_hook_response.messages
# 2. Get healthy deployments
healthy_deployments = await self.async_get_healthy_deployments(
model=model,
request_kwargs=request_kwargs,
messages=messages,
input=input,
specific_deployment=specific_deployment,
parent_otel_span=parent_otel_span,
)
# 3. If specific deployment returned, verify if it supports pass-through
if isinstance(healthy_deployments, dict):
litellm_params = healthy_deployments.get("litellm_params", {})
if litellm_params.get("use_in_pass_through"):
return healthy_deployments
else:
raise litellm.BadRequestError(
message=f"Deployment {healthy_deployments.get('model_info', {}).get('id')} does not support pass-through endpoint (use_in_pass_through=False)",
model=model,
llm_provider="",
)
# 4. Filter deployments that support pass-through
pass_through_deployments = self._filter_pass_through_deployments(
healthy_deployments=healthy_deployments
)
if len(pass_through_deployments) == 0:
raise litellm.BadRequestError(
message=f"Model {model} has no deployments configured with use_in_pass_through=True. Please add use_in_pass_through: true to the deployment configuration",
model=model,
llm_provider="",
)
# 5. Apply load balancing strategy
start_time = time.perf_counter()
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=pass_through_deployments, # type: ignore
messages=messages,
input=input,
)
)
elif (
self.routing_strategy == "latency-based-routing"
and self.lowestlatency_logger is not None
):
deployment = (
await self.lowestlatency_logger.async_get_available_deployments(
model_group=model,
healthy_deployments=pass_through_deployments, # type: ignore
messages=messages,
input=input,
request_kwargs=request_kwargs,
)
)
elif self.routing_strategy == "simple-shuffle":
return simple_shuffle(
llm_router_instance=self,
healthy_deployments=pass_through_deployments,
model=model,
)
elif (
self.routing_strategy == "least-busy"
and self.leastbusy_logger is not None
):
deployment = (
await self.leastbusy_logger.async_get_available_deployments(
model_group=model,
healthy_deployments=pass_through_deployments, # type: ignore
)
)
else:
deployment = None
if deployment is None:
exception = await async_raise_no_deployment_exception(
litellm_router_instance=self,
model=model,
parent_otel_span=parent_otel_span,
)
raise exception
verbose_router_logger.info(
f"async_get_available_deployment_for_pass_through model: {model}, selected deployment: {self.print_deployment(deployment)}"
)
end_time = time.perf_counter()
_duration = end_time - start_time
asyncio.create_task(
self.service_logger_obj.async_service_success_hook(
service=ServiceTypes.ROUTER,
duration=_duration,
call_type="<routing_strategy>.async_get_available_deployments",
parent_otel_span=parent_otel_span,
start_time=start_time,
end_time=end_time,
)
)
return deployment
except Exception as e:
traceback_exception = traceback.format_exc()
if request_kwargs is not None:
logging_obj = request_kwargs.get("litellm_logging_obj", None)
if logging_obj is not None:
threading.Thread(
target=logging_obj.failure_handler,
args=(e, traceback_exception),
).start()
asyncio.create_task(
logging_obj.async_failure_handler(e, traceback_exception) # type: ignore
)
raise e
async def async_pre_routing_hook(
self,
model: str,
request_kwargs: Dict,
messages: Optional[List[Dict[str, str]]] = None,
input: Optional[Union[str, List]] = None,
specific_deployment: Optional[bool] = False,
) -> Optional[PreRoutingHookResponse]:
"""
This hook is called before the routing decision is made.
Used for the litellm auto-router to modify the request before the routing decision is made.
"""
#########################################################
# Check if any auto-router should be used
#########################################################
if model in self.auto_routers:
return await self.auto_routers[model].async_pre_routing_hook(
model=model,
request_kwargs=request_kwargs,
messages=messages,
input=input,
specific_deployment=specific_deployment,
)
return None
def get_available_deployment(
self,
model: str,
messages: Optional[List[Dict[str, str]]] = None,
input: Optional[Union[str, List]] = None,
specific_deployment: Optional[bool] = False,
request_kwargs: Optional[Dict] = None,
):
"""
Returns the deployment based on routing strategy
"""
# users need to explicitly call a specific deployment, by setting `specific_deployment = True` as completion()/embedding() kwarg
# When this was no explicit we had several issues with fallbacks timing out
model, healthy_deployments = self._common_checks_available_deployment(
model=model,
messages=messages,
input=input,
specific_deployment=specific_deployment,
)
if isinstance(healthy_deployments, dict):
return healthy_deployments
parent_otel_span: Optional[Span] = _get_parent_otel_span_from_kwargs(
request_kwargs
)
cooldown_deployments = _get_cooldown_deployments(
litellm_router_instance=self, parent_otel_span=parent_otel_span
)
healthy_deployments = self._filter_cooldown_deployments(
healthy_deployments=healthy_deployments,
cooldown_deployments=cooldown_deployments,
)
# filter pre-call checks
if self.enable_pre_call_checks and messages is not None:
healthy_deployments = self._pre_call_checks(
model=model,
healthy_deployments=healthy_deployments,
messages=messages,
request_kwargs=request_kwargs,
)
if len(healthy_deployments) == 0:
model_ids = self.get_model_ids(model_name=model)
_cooldown_time = self.cooldown_cache.get_min_cooldown(
model_ids=model_ids, parent_otel_span=parent_otel_span
)
_cooldown_list = _get_cooldown_deployments(
litellm_router_instance=self, parent_otel_span=parent_otel_span
)
raise RouterRateLimitError(
model=model,
cooldown_time=_cooldown_time,
enable_pre_call_checks=self.enable_pre_call_checks,
cooldown_list=_cooldown_list,
)
if self.routing_strategy == "least-busy" and self.leastbusy_logger is not None:
deployment = self.leastbusy_logger.get_available_deployments(
model_group=model, healthy_deployments=healthy_deployments # type: ignore
)
elif self.routing_strategy == "simple-shuffle":
# if users pass rpm or tpm, we do a random weighted pick - based on rpm/tpm
############## Check 'weight' param set for weighted pick #################
return simple_shuffle(
llm_router_instance=self,
healthy_deployments=healthy_deployments,
model=model,
)
elif (
self.routing_strategy == "latency-based-routing"
and self.lowestlatency_logger is not None
):
deployment = self.lowestlatency_logger.get_available_deployments(
model_group=model,
healthy_deployments=healthy_deployments, # type: ignore
request_kwargs=request_kwargs,
)
elif (
self.routing_strategy == "usage-based-routing"
and self.lowesttpm_logger is not None
):
deployment = self.lowesttpm_logger.get_available_deployments(
model_group=model,
healthy_deployments=healthy_deployments, # type: ignore
messages=messages,
input=input,
)
elif (
self.routing_strategy == "usage-based-routing-v2"
and self.lowesttpm_logger_v2 is not None
):
deployment = self.lowesttpm_logger_v2.get_available_deployments(
model_group=model,
healthy_deployments=healthy_deployments, # type: ignore
messages=messages,
input=input,
)
else:
deployment = None
if deployment is None:
verbose_router_logger.info(
f"get_available_deployment for model: {model}, No deployment available"
)
model_ids = self.get_model_ids(model_name=model)
_cooldown_time = self.cooldown_cache.get_min_cooldown(
model_ids=model_ids, parent_otel_span=parent_otel_span
)
_cooldown_list = _get_cooldown_deployments(
litellm_router_instance=self, parent_otel_span=parent_otel_span
)
raise RouterRateLimitError(
model=model,
cooldown_time=_cooldown_time,
enable_pre_call_checks=self.enable_pre_call_checks,
cooldown_list=_cooldown_list,
)
verbose_router_logger.info(
f"get_available_deployment for model: {model}, Selected deployment: {self.print_deployment(deployment)} for model: {model}"
)
return deployment
def get_available_deployment_for_pass_through(
self,
model: str,
messages: Optional[List[Dict[str, str]]] = None,
input: Optional[Union[str, List]] = None,
specific_deployment: Optional[bool] = False,
request_kwargs: Optional[Dict] = None,
):
"""
Returns deployments available for pass-through endpoints (based on load balancing strategy)
Similar to get_available_deployment, but only returns deployments with use_in_pass_through=True
Args:
model: Model name
messages: Optional list of messages
input: Optional input data
specific_deployment: Whether to find a specific deployment
request_kwargs: Optional request parameters
Returns:
Dict: Selected deployment configuration
Raises:
BadRequestError: If no deployment is configured with use_in_pass_through=True
RouterRateLimitError: If no pass-through deployments are available
"""
# 1. Perform common checks to get healthy deployments list
model, healthy_deployments = self._common_checks_available_deployment(
model=model,
messages=messages,
input=input,
specific_deployment=specific_deployment,
)
# 2. If the returned is a specific deployment (Dict), verify and return directly
if isinstance(healthy_deployments, dict):
litellm_params = healthy_deployments.get("litellm_params", {})
if litellm_params.get("use_in_pass_through"):
return healthy_deployments
else:
# Specific deployment does not support pass-through
raise litellm.BadRequestError(
message=f"Deployment {healthy_deployments.get('model_info', {}).get('id')} does not support pass-through endpoint (use_in_pass_through=False)",
model=model,
llm_provider="",
)
# 3. Filter deployments that support pass-through
pass_through_deployments = self._filter_pass_through_deployments(
healthy_deployments=healthy_deployments
)
if len(pass_through_deployments) == 0:
# No deployments support pass-through
raise litellm.BadRequestError(
message=f"Model {model} has no deployment configured with use_in_pass_through=True. Please add use_in_pass_through: true in the deployment configuration",
model=model,
llm_provider="",
)
# 4. Apply cooldown filtering
parent_otel_span: Optional[Span] = _get_parent_otel_span_from_kwargs(
request_kwargs
)
cooldown_deployments = _get_cooldown_deployments(
litellm_router_instance=self, parent_otel_span=parent_otel_span
)
pass_through_deployments = self._filter_cooldown_deployments(
healthy_deployments=pass_through_deployments,
cooldown_deployments=cooldown_deployments,
)
# 5. Apply pre-call checks (if enabled)
if self.enable_pre_call_checks and messages is not None:
pass_through_deployments = self._pre_call_checks(
model=model,
healthy_deployments=pass_through_deployments,
messages=messages,
request_kwargs=request_kwargs,
)
if len(pass_through_deployments) == 0:
model_ids = self.get_model_ids(model_name=model)
_cooldown_time = self.cooldown_cache.get_min_cooldown(
model_ids=model_ids, parent_otel_span=parent_otel_span
)
_cooldown_list = _get_cooldown_deployments(
litellm_router_instance=self, parent_otel_span=parent_otel_span
)
raise RouterRateLimitError(
model=model,
cooldown_time=_cooldown_time,
enable_pre_call_checks=self.enable_pre_call_checks,
cooldown_list=_cooldown_list,
)
# 6. Apply load balancing strategy
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=pass_through_deployments # type: ignore
)
elif self.routing_strategy == "simple-shuffle":
return simple_shuffle(
llm_router_instance=self,
healthy_deployments=pass_through_deployments,
model=model,
)
elif (
self.routing_strategy == "latency-based-routing"
and self.lowestlatency_logger is not None
):
deployment = self.lowestlatency_logger.get_available_deployments(
model_group=model,
healthy_deployments=pass_through_deployments, # type: ignore
request_kwargs=request_kwargs,
)
elif (
self.routing_strategy == "usage-based-routing"
and self.lowesttpm_logger is not None
):
deployment = self.lowesttpm_logger.get_available_deployments(
model_group=model,
healthy_deployments=pass_through_deployments, # type: ignore
messages=messages,
input=input,
)
elif (
self.routing_strategy == "usage-based-routing-v2"
and self.lowesttpm_logger_v2 is not None
):
deployment = self.lowesttpm_logger_v2.get_available_deployments(
model_group=model,
healthy_deployments=pass_through_deployments, # type: ignore
messages=messages,
input=input,
)
else:
deployment = None
if deployment is None:
verbose_router_logger.info(
f"get_available_deployment_for_pass_through model: {model}, no available deployments"
)
model_ids = self.get_model_ids(model_name=model)
_cooldown_time = self.cooldown_cache.get_min_cooldown(
model_ids=model_ids, parent_otel_span=parent_otel_span
)
_cooldown_list = _get_cooldown_deployments(
litellm_router_instance=self, parent_otel_span=parent_otel_span
)
raise RouterRateLimitError(
model=model,
cooldown_time=_cooldown_time,
enable_pre_call_checks=self.enable_pre_call_checks,
cooldown_list=_cooldown_list,
)
verbose_router_logger.info(
f"get_available_deployment_for_pass_through model: {model}, selected deployment: {self.print_deployment(deployment)}"
)
return deployment
def _filter_cooldown_deployments(
self, healthy_deployments: List[Dict], cooldown_deployments: List[str]
) -> List[Dict]:
"""
Filters out the deployments currently cooling down from the list of healthy deployments
Args:
healthy_deployments: List of healthy deployments
cooldown_deployments: List of model_ids cooling down. cooldown_deployments is a list of model_id's cooling down, cooldown_deployments = ["16700539-b3cd-42f4-b426-6a12a1bb706a", "16700539-b3cd-42f4-b426-7899"]
Returns:
List of healthy deployments
"""
verbose_router_logger.debug(f"cooldown deployments: {cooldown_deployments}")
# Convert to set for O(1) lookup and use list comprehension for O(n) filtering
cooldown_set = set(cooldown_deployments)
return [
deployment
for deployment in healthy_deployments
if deployment["model_info"]["id"] not in cooldown_set
]
def _filter_pass_through_deployments(
self, healthy_deployments: List[Dict]
) -> List[Dict]:
"""
Filter out deployments configured with use_in_pass_through=True
Args:
healthy_deployments: List of healthy deployments
Returns:
List[Dict]: Only includes a list of deployments that support pass-through
"""
verbose_router_logger.debug(
f"Filter pass-through deployments from {len(healthy_deployments)} healthy deployments"
)
pass_through_deployments = [
deployment
for deployment in healthy_deployments
if deployment.get("litellm_params", {}).get("use_in_pass_through", False)
]
verbose_router_logger.debug(
f"Found {len(pass_through_deployments)} deployments with pass-through enabled"
)
return pass_through_deployments
def _track_deployment_metrics(
self, deployment, parent_otel_span: Optional[Span], response=None
):
"""
Tracks successful requests rpm usage.
"""
try:
model_id = deployment.get("model_info", {}).get("id", None)
if response is None:
# update self.deployment_stats
if model_id is not None:
self._update_usage(
model_id, parent_otel_span
) # update in-memory cache for tracking
except Exception as e:
verbose_router_logger.error(f"Error in _track_deployment_metrics: {str(e)}")
def get_num_retries_from_retry_policy(
self, exception: Exception, model_group: Optional[str] = None
):
return _get_num_retries_from_retry_policy(
exception=exception,
model_group=model_group,
model_group_retry_policy=self.model_group_retry_policy,
retry_policy=self.retry_policy,
)
def get_allowed_fails_from_policy(self, exception: Exception):
"""
BadRequestErrorRetries: Optional[int] = None
AuthenticationErrorRetries: Optional[int] = None
TimeoutErrorRetries: Optional[int] = None
RateLimitErrorRetries: Optional[int] = None
ContentPolicyViolationErrorRetries: Optional[int] = None
"""
# if we can find the exception then in the retry policy -> return the number of retries
allowed_fails_policy: Optional[AllowedFailsPolicy] = self.allowed_fails_policy
if allowed_fails_policy is None:
return None
if (
isinstance(exception, litellm.AuthenticationError)
and allowed_fails_policy.AuthenticationErrorAllowedFails is not None
):
return allowed_fails_policy.AuthenticationErrorAllowedFails
if (
isinstance(exception, litellm.Timeout)
and allowed_fails_policy.TimeoutErrorAllowedFails is not None
):
return allowed_fails_policy.TimeoutErrorAllowedFails
if (
isinstance(exception, litellm.RateLimitError)
and allowed_fails_policy.RateLimitErrorAllowedFails is not None
):
return allowed_fails_policy.RateLimitErrorAllowedFails
if (
isinstance(exception, litellm.ContentPolicyViolationError)
and allowed_fails_policy.ContentPolicyViolationErrorAllowedFails is not None
):
return allowed_fails_policy.ContentPolicyViolationErrorAllowedFails
if (
isinstance(exception, litellm.BadRequestError)
and allowed_fails_policy.BadRequestErrorAllowedFails is not None
):
return allowed_fails_policy.BadRequestErrorAllowedFails
def _initialize_alerting(self):
from litellm.integrations.SlackAlerting.slack_alerting import SlackAlerting
if self.alerting_config is None:
return
router_alerting_config: AlertingConfig = self.alerting_config
_slack_alerting_logger = SlackAlerting(
alerting_threshold=router_alerting_config.alerting_threshold,
alerting=["slack"],
default_webhook_url=router_alerting_config.webhook_url,
)
self.slack_alerting_logger = _slack_alerting_logger
litellm.logging_callback_manager.add_litellm_callback(_slack_alerting_logger) # type: ignore
litellm.logging_callback_manager.add_litellm_success_callback(
_slack_alerting_logger.response_taking_too_long_callback
)
verbose_router_logger.info(
"\033[94m\nInitialized Alerting for litellm.Router\033[0m\n"
)
def set_custom_routing_strategy(
self, CustomRoutingStrategy: CustomRoutingStrategyBase
):
"""
Sets get_available_deployment and async_get_available_deployment on an instanced of litellm.Router
Use this to set your custom routing strategy
Args:
CustomRoutingStrategy: litellm.router.CustomRoutingStrategyBase
"""
setattr(
self,
"get_available_deployment",
CustomRoutingStrategy.get_available_deployment,
)
setattr(
self,
"async_get_available_deployment",
CustomRoutingStrategy.async_get_available_deployment,
)
def flush_cache(self):
litellm.cache = None
self.cache.flush_cache()
def reset(self):
## clean up on close
litellm.success_callback = []
litellm._async_success_callback = []
litellm.failure_callback = []
litellm._async_failure_callback = []
self.retry_policy = None
self.flush_cache()
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