litellm-mirror/litellm/router_utils/fallback_event_handlers.py
Krish Dholakia 0c0498dd60
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Litellm dev 12 07 2024 (#7086)
* fix(main.py): support passing max retries to azure/openai embedding integrations

Fixes https://github.com/BerriAI/litellm/issues/7003

* feat(team_endpoints.py): allow updating team model aliases

Closes https://github.com/BerriAI/litellm/issues/6956

* feat(router.py): allow specifying model id as fallback - skips any cooldown check

Allows a default model to be checked if all models in cooldown

s/o @micahjsmith

* docs(reliability.md): add fallback to specific model to docs

* fix(utils.py): new 'is_prompt_caching_valid_prompt' helper util

Allows user to identify if messages/tools have prompt caching

Related issue: https://github.com/BerriAI/litellm/issues/6784

* feat(router.py): store model id for prompt caching valid prompt

Allows routing to that model id on subsequent requests

* fix(router.py): only cache if prompt is valid prompt caching prompt

prevents storing unnecessary items in cache

* feat(router.py): support routing prompt caching enabled models to previous deployments

Closes https://github.com/BerriAI/litellm/issues/6784

* test: fix linting errors

* feat(databricks/): convert basemodel to dict and exclude none values

allow passing pydantic message to databricks

* fix(utils.py): ensure all chat completion messages are dict

* (feat) Track `custom_llm_provider` in LiteLLMSpendLogs (#7081)

* add custom_llm_provider to SpendLogsPayload

* add custom_llm_provider to SpendLogs

* add custom llm provider to SpendLogs payload

* test_spend_logs_payload

* Add MLflow to the side bar (#7031)

Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>

* (bug fix) SpendLogs update DB catch all possible DB errors for retrying  (#7082)

* catch DB_CONNECTION_ERROR_TYPES

* fix DB retry mechanism for SpendLog updates

* use DB_CONNECTION_ERROR_TYPES in auth checks

* fix exp back off for writing SpendLogs

* use _raise_failed_update_spend_exception to ensure errors print as NON blocking

* test_update_spend_logs_multiple_batches_with_failure

* (Feat) Add StructuredOutputs support for Fireworks.AI (#7085)

* fix model cost map fireworks ai "supports_response_schema": true,

* fix supports_response_schema

* fix map openai params fireworks ai

* test_map_response_format

* test_map_response_format

* added deepinfra/Meta-Llama-3.1-405B-Instruct (#7084)

* bump: version 1.53.9 → 1.54.0

* fix deepinfra

* litellm db fixes LiteLLM_UserTable (#7089)

* ci/cd queue new release

* fix llama-3.3-70b-versatile

* refactor - use consistent file naming convention `AI21/` -> `ai21`  (#7090)

* fix refactor - use consistent file naming convention

* ci/cd run again

* fix naming structure

* fix use consistent naming (#7092)

---------

Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: Yuki Watanabe <31463517+B-Step62@users.noreply.github.com>
Co-authored-by: ali sayyah <ali.sayyah2@gmail.com>
2024-12-08 00:30:33 -08:00

247 lines
9.5 KiB
Python

from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import litellm
from litellm._logging import verbose_router_logger
from litellm.integrations.custom_logger import CustomLogger
from litellm.main import verbose_logger
if TYPE_CHECKING:
from litellm.router import Router as _Router
LitellmRouter = _Router
else:
LitellmRouter = Any
async def run_async_fallback(
*args: Tuple[Any],
litellm_router: LitellmRouter,
fallback_model_group: List[str],
original_model_group: str,
original_exception: Exception,
max_fallbacks: int,
fallback_depth: int,
**kwargs,
) -> Any:
"""
Loops through all the fallback model groups and calls kwargs["original_function"] with the arguments and keyword arguments provided.
If the call is successful, it logs the success and returns the response.
If the call fails, it logs the failure and continues to the next fallback model group.
If all fallback model groups fail, it raises the most recent exception.
Args:
litellm_router: The litellm router instance.
*args: Positional arguments.
fallback_model_group: List[str] of fallback model groups. example: ["gpt-4", "gpt-3.5-turbo"]
original_model_group: The original model group. example: "gpt-3.5-turbo"
original_exception: The original exception.
**kwargs: Keyword arguments.
Returns:
The response from the successful fallback model group.
Raises:
The most recent exception if all fallback model groups fail.
"""
### BASE CASE ### MAX FALLBACK DEPTH REACHED
if fallback_depth >= max_fallbacks:
raise original_exception
error_from_fallbacks = original_exception
for mg in fallback_model_group:
if mg == original_model_group:
continue
try:
# LOGGING
kwargs = litellm_router.log_retry(kwargs=kwargs, e=original_exception)
verbose_router_logger.info(f"Falling back to model_group = {mg}")
kwargs["model"] = mg
kwargs.setdefault("metadata", {}).update(
{"model_group": mg}
) # update model_group used, if fallbacks are done
kwargs["fallback_depth"] = fallback_depth + 1
kwargs["max_fallbacks"] = max_fallbacks
response = await litellm_router.async_function_with_fallbacks(
*args, **kwargs
)
verbose_router_logger.info("Successful fallback b/w models.")
# callback for successfull_fallback_event():
await log_success_fallback_event(
original_model_group=original_model_group,
kwargs=kwargs,
original_exception=original_exception,
)
return response
except Exception as e:
error_from_fallbacks = e
await log_failure_fallback_event(
original_model_group=original_model_group,
kwargs=kwargs,
original_exception=original_exception,
)
raise error_from_fallbacks
def run_sync_fallback(
litellm_router: LitellmRouter,
*args: Tuple[Any],
fallback_model_group: List[str],
original_model_group: str,
original_exception: Exception,
**kwargs,
) -> Any:
"""
Synchronous version of run_async_fallback.
Loops through all the fallback model groups and calls kwargs["original_function"] with the arguments and keyword arguments provided.
If the call is successful, returns the response.
If the call fails, continues to the next fallback model group.
If all fallback model groups fail, it raises the most recent exception.
Args:
litellm_router: The litellm router instance.
*args: Positional arguments.
fallback_model_group: List[str] of fallback model groups. example: ["gpt-4", "gpt-3.5-turbo"]
original_model_group: The original model group. example: "gpt-3.5-turbo"
original_exception: The original exception.
**kwargs: Keyword arguments.
Returns:
The response from the successful fallback model group.
Raises:
The most recent exception if all fallback model groups fail.
"""
error_from_fallbacks = original_exception
for mg in fallback_model_group:
if mg == original_model_group:
continue
try:
# LOGGING
kwargs = litellm_router.log_retry(kwargs=kwargs, e=original_exception)
verbose_router_logger.info(f"Falling back to model_group = {mg}")
kwargs["model"] = mg
kwargs.setdefault("metadata", {}).update(
{"model_group": mg}
) # update model_group used, if fallbacks are done
response = litellm_router.function_with_fallbacks(*args, **kwargs)
verbose_router_logger.info("Successful fallback b/w models.")
return response
except Exception as e:
error_from_fallbacks = e
raise error_from_fallbacks
async def log_success_fallback_event(
original_model_group: str, kwargs: dict, original_exception: Exception
):
"""
Log a successful fallback event to all registered callbacks.
This function iterates through all callbacks, initializing _known_custom_logger_compatible_callbacks if needed,
and calls the log_success_fallback_event method on CustomLogger instances.
Args:
original_model_group (str): The original model group before fallback.
kwargs (dict): kwargs for the request
Note:
Errors during logging are caught and reported but do not interrupt the process.
"""
from litellm.litellm_core_utils.litellm_logging import (
_init_custom_logger_compatible_class,
)
for _callback in litellm.callbacks:
if isinstance(_callback, CustomLogger) or (
_callback in litellm._known_custom_logger_compatible_callbacks
):
try:
_callback_custom_logger: Optional[CustomLogger] = None
if _callback in litellm._known_custom_logger_compatible_callbacks:
_callback_custom_logger = _init_custom_logger_compatible_class(
logging_integration=_callback, # type: ignore
llm_router=None,
internal_usage_cache=None,
)
elif isinstance(_callback, CustomLogger):
_callback_custom_logger = _callback
else:
verbose_router_logger.exception(
f"{_callback} logger not found / initialized properly"
)
continue
if _callback_custom_logger is None:
verbose_router_logger.exception(
f"{_callback} logger not found / initialized properly, callback is None"
)
continue
await _callback_custom_logger.log_success_fallback_event(
original_model_group=original_model_group,
kwargs=kwargs,
original_exception=original_exception,
)
except Exception as e:
verbose_router_logger.error(
f"Error in log_success_fallback_event: {str(e)}"
)
async def log_failure_fallback_event(
original_model_group: str, kwargs: dict, original_exception: Exception
):
"""
Log a failed fallback event to all registered callbacks.
This function iterates through all callbacks, initializing _known_custom_logger_compatible_callbacks if needed,
and calls the log_failure_fallback_event method on CustomLogger instances.
Args:
original_model_group (str): The original model group before fallback.
kwargs (dict): kwargs for the request
Note:
Errors during logging are caught and reported but do not interrupt the process.
"""
from litellm.litellm_core_utils.litellm_logging import (
_init_custom_logger_compatible_class,
)
for _callback in litellm.callbacks:
if isinstance(_callback, CustomLogger) or (
_callback in litellm._known_custom_logger_compatible_callbacks
):
try:
_callback_custom_logger: Optional[CustomLogger] = None
if _callback in litellm._known_custom_logger_compatible_callbacks:
_callback_custom_logger = _init_custom_logger_compatible_class(
logging_integration=_callback, # type: ignore
llm_router=None,
internal_usage_cache=None,
)
elif isinstance(_callback, CustomLogger):
_callback_custom_logger = _callback
else:
verbose_router_logger.exception(
f"{_callback} logger not found / initialized properly"
)
continue
if _callback_custom_logger is None:
verbose_router_logger.exception(
f"{_callback} logger not found / initialized properly"
)
continue
await _callback_custom_logger.log_failure_fallback_event(
original_model_group=original_model_group,
kwargs=kwargs,
original_exception=original_exception,
)
except Exception as e:
verbose_router_logger.error(
f"Error in log_failure_fallback_event: {str(e)}"
)