litellm-mirror/litellm/integrations/custom_logger.py

215 lines
6.7 KiB
Python

#### What this does ####
# On success, logs events to Promptlayer
import os
import traceback
from typing import Any, Literal, Optional, Tuple, Union
import dotenv
from pydantic import BaseModel
from litellm.caching import DualCache
from litellm.proxy._types import UserAPIKeyAuth
from litellm.types.llms.openai import ChatCompletionRequest
from litellm.types.utils import AdapterCompletionStreamWrapper, ModelResponse
class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callback#callback-class
# Class variables or attributes
def __init__(self) -> None:
pass
def log_pre_api_call(self, model, messages, kwargs):
pass
def log_post_api_call(self, kwargs, response_obj, start_time, end_time):
pass
def log_stream_event(self, kwargs, response_obj, start_time, end_time):
pass
def log_success_event(self, kwargs, response_obj, start_time, end_time):
pass
def log_failure_event(self, kwargs, response_obj, start_time, end_time):
pass
#### ASYNC ####
async def async_log_stream_event(self, kwargs, response_obj, start_time, end_time):
pass
async def async_log_pre_api_call(self, model, messages, kwargs):
pass
async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
pass
async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
pass
#### PRE-CALL CHECKS - router/proxy only ####
"""
Allows usage-based-routing-v2 to run pre-call rpm checks within the picked deployment's semaphore (concurrency-safe tpm/rpm checks).
"""
async def async_pre_call_check(self, deployment: dict) -> Optional[dict]:
pass
def pre_call_check(self, deployment: dict) -> Optional[dict]:
pass
#### Fallback Events - router/proxy only ####
async def log_success_fallback_event(self, original_model_group: str, kwargs: dict):
pass
async def log_failure_fallback_event(self, original_model_group: str, kwargs: dict):
pass
#### ADAPTERS #### Allow calling 100+ LLMs in custom format - https://github.com/BerriAI/litellm/pulls
def translate_completion_input_params(
self, kwargs
) -> Optional[ChatCompletionRequest]:
"""
Translates the input params, from the provider's native format to the litellm.completion() format.
"""
pass
def translate_completion_output_params(
self, response: ModelResponse
) -> Optional[BaseModel]:
"""
Translates the output params, from the OpenAI format to the custom format.
"""
pass
def translate_completion_output_params_streaming(
self, completion_stream: Any
) -> Optional[AdapterCompletionStreamWrapper]:
"""
Translates the streaming chunk, from the OpenAI format to the custom format.
"""
pass
#### CALL HOOKS - proxy only ####
"""
Control the modify incoming / outgoung data before calling the model
"""
async def async_pre_call_hook(
self,
user_api_key_dict: UserAPIKeyAuth,
cache: DualCache,
data: dict,
call_type: Literal[
"completion",
"text_completion",
"embeddings",
"image_generation",
"moderation",
"audio_transcription",
"pass_through_endpoint",
],
) -> Optional[
Union[Exception, str, dict]
]: # raise exception if invalid, return a str for the user to receive - if rejected, or return a modified dictionary for passing into litellm
pass
async def async_post_call_failure_hook(
self, original_exception: Exception, user_api_key_dict: UserAPIKeyAuth
):
pass
async def async_post_call_success_hook(
self,
user_api_key_dict: UserAPIKeyAuth,
response,
):
pass
async def async_logging_hook(
self, kwargs: dict, result: Any, call_type: str
) -> Tuple[dict, Any]:
"""For masking logged request/response. Return a modified version of the request/result."""
return kwargs, result
def logging_hook(
self, kwargs: dict, result: Any, call_type: str
) -> Tuple[dict, Any]:
"""For masking logged request/response. Return a modified version of the request/result."""
return kwargs, result
async def async_moderation_hook(
self,
data: dict,
user_api_key_dict: UserAPIKeyAuth,
call_type: Literal["completion", "embeddings", "image_generation"],
):
pass
async def async_post_call_streaming_hook(
self,
user_api_key_dict: UserAPIKeyAuth,
response: str,
):
pass
#### SINGLE-USE #### - https://docs.litellm.ai/docs/observability/custom_callback#using-your-custom-callback-function
def log_input_event(self, model, messages, kwargs, print_verbose, callback_func):
try:
kwargs["model"] = model
kwargs["messages"] = messages
kwargs["log_event_type"] = "pre_api_call"
callback_func(
kwargs,
)
print_verbose(f"Custom Logger - model call details: {kwargs}")
except:
print_verbose(f"Custom Logger Error - {traceback.format_exc()}")
async def async_log_input_event(
self, model, messages, kwargs, print_verbose, callback_func
):
try:
kwargs["model"] = model
kwargs["messages"] = messages
kwargs["log_event_type"] = "pre_api_call"
await callback_func(
kwargs,
)
print_verbose(f"Custom Logger - model call details: {kwargs}")
except:
print_verbose(f"Custom Logger Error - {traceback.format_exc()}")
def log_event(
self, kwargs, response_obj, start_time, end_time, print_verbose, callback_func
):
# Method definition
try:
kwargs["log_event_type"] = "post_api_call"
callback_func(
kwargs, # kwargs to func
response_obj,
start_time,
end_time,
)
except:
print_verbose(f"Custom Logger Error - {traceback.format_exc()}")
pass
async def async_log_event(
self, kwargs, response_obj, start_time, end_time, print_verbose, callback_func
):
# Method definition
try:
kwargs["log_event_type"] = "post_api_call"
await callback_func(
kwargs, # kwargs to func
response_obj,
start_time,
end_time,
)
except:
print_verbose(f"Custom Logger Error - {traceback.format_exc()}")
pass