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https://github.com/BerriAI/litellm.git
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fix(utils.py): fix cost tracking for cache hits (should be 0)
This commit is contained in:
Krrish Dholakia
parent
62ad6f19b7
commit
1ed6842009
2 changed files with 129 additions and 19 deletions
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@ -31,6 +31,7 @@ class MyCustomHandler(CustomLogger):
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self.sync_stream_collected_response = None # type: ignore
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self.user = None # type: ignore
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self.data_sent_to_api: dict = {}
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self.response_cost = 0
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def log_pre_api_call(self, model, messages, kwargs):
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print(f"Pre-API Call")
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@ -47,6 +48,8 @@ class MyCustomHandler(CustomLogger):
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self.success = True
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if kwargs.get("stream") == True:
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self.sync_stream_collected_response = response_obj
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print(f"response cost in log_success_event: {kwargs.get('response_cost')}")
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self.response_cost = kwargs.get("response_cost", 0)
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def log_failure_event(self, kwargs, response_obj, start_time, end_time):
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print(f"On Failure")
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@ -64,6 +67,10 @@ class MyCustomHandler(CustomLogger):
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self.stream_collected_response = response_obj
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self.async_completion_kwargs = kwargs
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self.user = kwargs.get("user", None)
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print(
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f"response cost in log_async_success_event: {kwargs.get('response_cost')}"
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)
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self.response_cost = kwargs.get("response_cost", 0)
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async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
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print(f"On Async Failure")
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@ -400,6 +407,50 @@ async def test_async_custom_handler_embedding_optional_param_bedrock():
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assert "user" not in customHandler_optional_params.data_sent_to_api
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@pytest.mark.asyncio
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async def test_cost_tracking_with_caching():
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"""
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Important Test - This tests if that cost is 0 for cached responses
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"""
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from litellm import Cache
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litellm.set_verbose = False
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litellm.cache = Cache(
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type="redis",
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host=os.environ["REDIS_HOST"],
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port=os.environ["REDIS_PORT"],
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password=os.environ["REDIS_PASSWORD"],
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)
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customHandler_optional_params = MyCustomHandler()
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litellm.callbacks = [customHandler_optional_params]
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messages = [
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{
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"role": "user",
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"content": f"write a one sentence poem about: {time.time()}",
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}
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]
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response1 = await litellm.acompletion(
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model="gpt-3.5-turbo",
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messages=messages,
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max_tokens=40,
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temperature=0.2,
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caching=True,
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)
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await asyncio.sleep(1) # success callback is async
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response_cost = customHandler_optional_params.response_cost
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assert response_cost > 0
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response2 = await litellm.acompletion(
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model="gpt-3.5-turbo",
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messages=messages,
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max_tokens=40,
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temperature=0.2,
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caching=True,
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)
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await asyncio.sleep(1) # success callback is async
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response_cost_2 = customHandler_optional_params.response_cost
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assert response_cost_2 == 0
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def test_redis_cache_completion_stream():
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from litellm import Cache
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@ -1060,7 +1060,12 @@ class Logging:
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and self.stream != True
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): # handle streaming separately
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try:
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self.model_call_details["response_cost"] = litellm.completion_cost(
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if self.model_call_details.get("cache_hit", False) == True:
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self.model_call_details["response_cost"] = 0.0
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else:
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self.model_call_details[
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"response_cost"
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] = litellm.completion_cost(
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completion_response=result,
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)
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verbose_logger.debug(
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@ -1096,7 +1101,7 @@ class Logging:
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def success_handler(
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self, result=None, start_time=None, end_time=None, cache_hit=None, **kwargs
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):
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verbose_logger.debug(f"Logging Details LiteLLM-Success Call")
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verbose_logger.debug(f"Logging Details LiteLLM-Success Call: {cache_hit}")
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start_time, end_time, result = self._success_handler_helper_fn(
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start_time=start_time,
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end_time=end_time,
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@ -1134,7 +1139,12 @@ class Logging:
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"complete_streaming_response"
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] = complete_streaming_response
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try:
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self.model_call_details["response_cost"] = litellm.completion_cost(
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if self.model_call_details.get("cache_hit", False) == True:
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self.model_call_details["response_cost"] = 0.0
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else:
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self.model_call_details[
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"response_cost"
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] = litellm.completion_cost(
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completion_response=complete_streaming_response,
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)
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verbose_logger.debug(
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@ -1158,6 +1168,7 @@ class Logging:
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callbacks.append(callback)
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else:
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callbacks = litellm.success_callback
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for callback in callbacks:
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try:
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if callback == "lite_debugger":
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@ -1342,7 +1353,7 @@ class Logging:
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end_time=end_time,
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print_verbose=print_verbose,
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)
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elif (
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if (
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isinstance(callback, CustomLogger)
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and self.model_call_details.get("litellm_params", {}).get(
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"acompletion", False
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@ -1353,9 +1364,6 @@ class Logging:
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)
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== False
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): # custom logger class
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verbose_logger.info(
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f"success callbacks: Running SYNC Custom Logger Class"
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)
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if self.stream and complete_streaming_response is None:
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callback.log_stream_event(
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kwargs=self.model_call_details,
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@ -1377,7 +1385,7 @@ class Logging:
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start_time=start_time,
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end_time=end_time,
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)
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elif (
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if (
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callable(callback) == True
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and self.model_call_details.get("litellm_params", {}).get(
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"acompletion", False
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@ -1452,6 +1460,9 @@ class Logging:
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"complete_streaming_response"
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] = complete_streaming_response
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try:
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if self.model_call_details.get("cache_hit", False) == True:
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self.model_call_details["response_cost"] = 0.0
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else:
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self.model_call_details["response_cost"] = litellm.completion_cost(
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completion_response=complete_streaming_response,
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)
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@ -2217,7 +2228,7 @@ def client(original_function):
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if call_type == CallTypes.completion.value and isinstance(
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cached_result, dict
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):
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return convert_to_model_response_object(
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cached_result = convert_to_model_response_object(
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response_object=cached_result,
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model_response_object=ModelResponse(),
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stream=kwargs.get("stream", False),
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@ -2225,11 +2236,59 @@ def client(original_function):
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elif call_type == CallTypes.embedding.value and isinstance(
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cached_result, dict
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):
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return convert_to_model_response_object(
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cached_result = convert_to_model_response_object(
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response_object=cached_result,
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response_type="embedding",
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)
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else:
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# LOG SUCCESS
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cache_hit = True
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end_time = datetime.datetime.now()
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(
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model,
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custom_llm_provider,
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dynamic_api_key,
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api_base,
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) = litellm.get_llm_provider(
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model=model,
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custom_llm_provider=kwargs.get(
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"custom_llm_provider", None
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),
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api_base=kwargs.get("api_base", None),
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api_key=kwargs.get("api_key", None),
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)
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print_verbose(
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f"Async Wrapper: Completed Call, calling async_success_handler: {logging_obj.async_success_handler}"
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)
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logging_obj.update_environment_variables(
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model=model,
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user=kwargs.get("user", None),
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optional_params={},
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litellm_params={
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"logger_fn": kwargs.get("logger_fn", None),
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"acompletion": False,
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"metadata": kwargs.get("metadata", {}),
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"model_info": kwargs.get("model_info", {}),
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"proxy_server_request": kwargs.get(
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"proxy_server_request", None
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),
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"preset_cache_key": kwargs.get(
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"preset_cache_key", None
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),
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"stream_response": kwargs.get(
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"stream_response", {}
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),
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},
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input=kwargs.get("messages", ""),
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api_key=kwargs.get("api_key", None),
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original_response=str(cached_result),
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additional_args=None,
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stream=kwargs.get("stream", False),
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)
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threading.Thread(
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target=logging_obj.success_handler,
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args=(cached_result, start_time, end_time, cache_hit),
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).start()
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return cached_result
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# CHECK MAX TOKENS
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