forked from phoenix/litellm-mirror
making logging a class - adding input-callbacks
This commit is contained in:
Krrish Dholakia
parent
9ac577ed6f
commit
d792be89de
12 changed files with 237 additions and 315 deletions
161
litellm/utils.py
161
litellm/utils.py
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@ -135,48 +135,105 @@ def install_and_import(package: str):
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####### LOGGING ###################
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# Logging function -> log the exact model details + what's being sent | Non-Blocking
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def logging(
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model=None,
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input=None,
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custom_llm_provider=None,
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azure=False,
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class Logging:
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def __init__(self, model, messages, optional_params, litellm_params):
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self.model = model
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self.messages = messages
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self.optional_params = optional_params
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self.litellm_params = litellm_params
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self.logger_fn = litellm_params["logger_fn"]
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self.model_call_details = {
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"model": model,
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"messages": messages,
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"optional_params": self.optional_params,
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"litellm_params": self.litellm_params,
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}
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def pre_call(self, input, api_key, additional_args={}):
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try:
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print(f"logging pre call for model: {self.model}")
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self.model_call_details["input"] = input
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self.model_call_details["api_key"] = api_key
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self.model_call_details["additional_args"] = additional_args
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## User Logging -> if you pass in a custom logging function
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print_verbose(
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f"Logging Details: logger_fn - {self.logger_fn} | callable(logger_fn) - {callable(self.logger_fn)}"
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)
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if self.logger_fn and callable(self.logger_fn):
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try:
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self.logger_fn(
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self.model_call_details
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) # Expectation: any logger function passed in by the user should accept a dict object
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except Exception as e:
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print_verbose(
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f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}"
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)
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## Input Integration Logging -> If you want to log the fact that an attempt to call the model was made
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for callback in litellm.input_callback:
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try:
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if callback == "supabase":
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print_verbose("reaches supabase for logging!")
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model = self.model
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messages = self.messages
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print(f"litellm._thread_context: {litellm._thread_context}")
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supabaseClient.input_log_event(
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model=model,
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messages=messages,
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end_user=litellm._thread_context.user,
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litellm_call_id=self.litellm_params["litellm_call_id"],
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print_verbose=print_verbose,
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)
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pass
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except:
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pass
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except:
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print_verbose(
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f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}"
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)
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pass
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def post_call(self, input, api_key, original_response, additional_args={}):
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# Do something here
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try:
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self.model_call_details["input"] = input
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self.model_call_details["api_key"] = api_key
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self.model_call_details["original_response"] = original_response
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self.model_call_details["additional_args"] = additional_args
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## User Logging -> if you pass in a custom logging function
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print_verbose(
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f"Logging Details: logger_fn - {self.logger_fn} | callable(logger_fn) - {callable(self.logger_fn)}"
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)
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if self.logger_fn and callable(self.logger_fn):
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try:
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self.logger_fn(
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self.model_call_details
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) # Expectation: any logger function passed in by the user should accept a dict object
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except Exception as e:
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print_verbose(
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f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}"
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)
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except:
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print_verbose(
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f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {traceback.format_exc()}"
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)
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pass
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# Add more methods as needed
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def exception_logging(
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additional_args={},
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logger_fn=None,
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exception=None,
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):
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try:
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model_call_details = {}
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if model:
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model_call_details["model"] = model
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if azure:
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model_call_details["azure"] = azure
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if custom_llm_provider:
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model_call_details["custom_llm_provider"] = custom_llm_provider
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if exception:
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model_call_details["exception"] = exception
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if input:
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model_call_details["input"] = input
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if len(additional_args):
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model_call_details["additional_args"] = additional_args
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# log additional call details -> api key, etc.
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if model:
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if (
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azure == True
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or model in litellm.open_ai_chat_completion_models
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or model in litellm.open_ai_chat_completion_models
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or model in litellm.open_ai_embedding_models
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):
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model_call_details["api_type"] = openai.api_type
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model_call_details["api_base"] = openai.api_base
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model_call_details["api_version"] = openai.api_version
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model_call_details["api_key"] = openai.api_key
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elif "replicate" in model:
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model_call_details["api_key"] = os.environ.get("REPLICATE_API_TOKEN")
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elif model in litellm.anthropic_models:
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model_call_details["api_key"] = os.environ.get("ANTHROPIC_API_KEY")
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elif model in litellm.cohere_models:
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model_call_details["api_key"] = os.environ.get("COHERE_API_KEY")
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model_call_details["additional_args"] = additional_args
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## User Logging -> if you pass in a custom logging function or want to use sentry breadcrumbs
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print_verbose(
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f"Logging Details: logger_fn - {logger_fn} | callable(logger_fn) - {callable(logger_fn)}"
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@ -206,10 +263,10 @@ def client(original_function):
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try:
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global callback_list, add_breadcrumb, user_logger_fn
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if (
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len(litellm.success_callback) > 0 or len(litellm.failure_callback) > 0
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len(litellm.input_callback) > 0 or len(litellm.success_callback) > 0 or len(litellm.failure_callback) > 0
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) and len(callback_list) == 0:
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callback_list = list(
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set(litellm.success_callback + litellm.failure_callback)
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set(litellm.input_callback + litellm.success_callback + litellm.failure_callback)
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)
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set_callbacks(
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callback_list=callback_list,
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@ -299,13 +356,16 @@ def client(original_function):
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result = None
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try:
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function_setup(*args, **kwargs)
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## MODEL CALL
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litellm_call_id = str(uuid.uuid4())
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kwargs["litellm_call_id"] = litellm_call_id
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## [OPTIONAL] CHECK CACHE
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start_time = datetime.datetime.now()
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if (litellm.caching or litellm.caching_with_models) and (
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cached_result := check_cache(*args, **kwargs)
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) is not None:
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result = cached_result
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else:
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## MODEL CALL
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result = original_function(*args, **kwargs)
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end_time = datetime.datetime.now()
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## Add response to CACHE
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@ -399,6 +459,7 @@ def get_litellm_params(
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together_ai=False,
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custom_llm_provider=None,
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custom_api_base=None,
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litellm_call_id=None,
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):
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litellm_params = {
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"return_async": return_async,
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@ -408,6 +469,7 @@ def get_litellm_params(
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"verbose": verbose,
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"custom_llm_provider": custom_llm_provider,
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"custom_api_base": custom_api_base,
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"litellm_call_id": litellm_call_id
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}
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return litellm_params
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@ -567,7 +629,8 @@ def set_callbacks(callback_list):
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global sentry_sdk_instance, capture_exception, add_breadcrumb, posthog, slack_app, alerts_channel, heliconeLogger, aispendLogger, berrispendLogger, supabaseClient
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try:
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for callback in callback_list:
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if callback == "sentry" or "SENTRY_API_URL" in os.environ:
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print(f"callback: {callback}")
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if callback == "sentry":
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try:
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import sentry_sdk
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except ImportError:
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@ -623,6 +686,7 @@ def set_callbacks(callback_list):
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elif callback == "berrispend":
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berrispendLogger = BerriSpendLogger()
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elif callback == "supabase":
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print(f"instantiating supabase")
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supabaseClient = Supabase()
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except Exception as e:
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raise e
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@ -743,7 +807,6 @@ def handle_failure(exception, traceback_exception, start_time, end_time, args, k
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"completion_tokens": 0,
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},
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}
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print(f"litellm._thread_context: {litellm._thread_context}")
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supabaseClient.log_event(
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model=model,
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messages=messages,
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@ -751,9 +814,9 @@ def handle_failure(exception, traceback_exception, start_time, end_time, args, k
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response_obj=result,
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start_time=start_time,
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end_time=end_time,
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litellm_call_id=kwargs["litellm_call_id"],
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print_verbose=print_verbose,
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)
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except:
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print_verbose(
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f"Error Occurred while logging failure: {traceback.format_exc()}"
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@ -769,7 +832,7 @@ def handle_failure(exception, traceback_exception, start_time, end_time, args, k
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pass
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except Exception as e:
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## LOGGING
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logging(logger_fn=user_logger_fn, exception=e)
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exception_logging(logger_fn=user_logger_fn, exception=e)
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pass
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@ -849,11 +912,12 @@ def handle_success(args, kwargs, result, start_time, end_time):
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response_obj=result,
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start_time=start_time,
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end_time=end_time,
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litellm_call_id=kwargs["litellm_call_id"],
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print_verbose=print_verbose,
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)
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except Exception as e:
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## LOGGING
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logging(logger_fn=user_logger_fn, exception=e)
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exception_logging(logger_fn=user_logger_fn, exception=e)
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print_verbose(
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f"[Non-Blocking] Success Callback Error - {traceback.format_exc()}"
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)
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@ -864,7 +928,7 @@ def handle_success(args, kwargs, result, start_time, end_time):
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pass
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except Exception as e:
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## LOGGING
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logging(logger_fn=user_logger_fn, exception=e)
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exception_logging(logger_fn=user_logger_fn, exception=e)
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print_verbose(
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f"[Non-Blocking] Success Callback Error - {traceback.format_exc()}"
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)
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@ -912,15 +976,6 @@ def exception_type(model, original_exception, custom_llm_provider):
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exception_type = type(original_exception).__name__
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else:
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exception_type = ""
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logging(
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model=model,
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additional_args={
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"error_str": error_str,
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"exception_type": exception_type,
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"original_exception": original_exception,
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},
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logger_fn=user_logger_fn,
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)
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if "claude" in model: # one of the anthropics
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if hasattr(original_exception, "status_code"):
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print_verbose(f"status_code: {original_exception.status_code}")
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@ -1030,7 +1085,7 @@ def exception_type(model, original_exception, custom_llm_provider):
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raise original_exception
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except Exception as e:
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## LOGGING
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logging(
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exception_logging(
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logger_fn=user_logger_fn,
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additional_args={
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"exception_mapping_worked": exception_mapping_worked,
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