mirror of
https://github.com/BerriAI/litellm.git
synced 2026-10-08 03:08:45 +00:00
Minor Fixes (#11868)
* fix(litellm_pre_call_utils.py): add user agent tags to spend logs in standard logging payload logic avoid clash when tag based routing is enabled * test: remove redundant test * test: rename oidc test to run earlier quicker debuging * fix(azure.py): return more detailed error message * fix(azure/common_utils.py): use default scope, if scope is none fixes oidc test * fix: always default to cognitiveservices.azure.com * test: update test
This commit is contained in:
Krish Dholakia
GitHub
parent
58dce17414
commit
7f8b2579a2
20 changed files with 506 additions and 445 deletions
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@ -288,9 +288,9 @@ class Logging(LiteLLMLoggingBaseClass):
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self.litellm_trace_id: str = litellm_trace_id or str(uuid.uuid4())
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self.function_id = function_id
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self.streaming_chunks: List[Any] = [] # for generating complete stream response
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self.sync_streaming_chunks: List[
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Any
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] = [] # for generating complete stream response
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self.sync_streaming_chunks: List[Any] = (
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[]
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) # for generating complete stream response
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self.log_raw_request_response = log_raw_request_response
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# Initialize dynamic callbacks
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@ -645,9 +645,9 @@ class Logging(LiteLLMLoggingBaseClass):
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if anthropic_cache_control_logger := AnthropicCacheControlHook.get_custom_logger_for_anthropic_cache_control_hook(
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non_default_params
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):
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self.model_call_details[
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"prompt_integration"
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] = anthropic_cache_control_logger.__class__.__name__
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self.model_call_details["prompt_integration"] = (
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anthropic_cache_control_logger.__class__.__name__
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)
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return anthropic_cache_control_logger
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#########################################################
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@ -665,9 +665,9 @@ class Logging(LiteLLMLoggingBaseClass):
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),
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)
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)
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self.model_call_details[
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"prompt_integration"
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] = vector_store_custom_logger.__class__.__name__
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self.model_call_details["prompt_integration"] = (
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vector_store_custom_logger.__class__.__name__
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)
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return vector_store_custom_logger
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return None
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@ -719,9 +719,9 @@ class Logging(LiteLLMLoggingBaseClass):
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model
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): # if model name was changes pre-call, overwrite the initial model call name with the new one
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self.model_call_details["model"] = model
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self.model_call_details["litellm_params"][
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"api_base"
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] = self._get_masked_api_base(additional_args.get("api_base", ""))
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self.model_call_details["litellm_params"]["api_base"] = (
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self._get_masked_api_base(additional_args.get("api_base", ""))
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)
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def pre_call(self, input, api_key, model=None, additional_args={}): # noqa: PLR0915
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# Log the exact input to the LLM API
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@ -750,10 +750,10 @@ class Logging(LiteLLMLoggingBaseClass):
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try:
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# [Non-blocking Extra Debug Information in metadata]
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if turn_off_message_logging is True:
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_metadata[
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"raw_request"
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] = "redacted by litellm. \
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_metadata["raw_request"] = (
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"redacted by litellm. \
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'litellm.turn_off_message_logging=True'"
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)
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else:
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curl_command = self._get_request_curl_command(
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api_base=additional_args.get("api_base", ""),
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@ -764,32 +764,32 @@ class Logging(LiteLLMLoggingBaseClass):
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_metadata["raw_request"] = str(curl_command)
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# split up, so it's easier to parse in the UI
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self.model_call_details[
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"raw_request_typed_dict"
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] = RawRequestTypedDict(
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raw_request_api_base=str(
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additional_args.get("api_base") or ""
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),
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raw_request_body=self._get_raw_request_body(
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additional_args.get("complete_input_dict", {})
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),
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raw_request_headers=self._get_masked_headers(
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additional_args.get("headers", {}) or {},
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ignore_sensitive_headers=True,
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),
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error=None,
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self.model_call_details["raw_request_typed_dict"] = (
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RawRequestTypedDict(
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raw_request_api_base=str(
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additional_args.get("api_base") or ""
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),
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raw_request_body=self._get_raw_request_body(
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additional_args.get("complete_input_dict", {})
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),
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raw_request_headers=self._get_masked_headers(
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additional_args.get("headers", {}) or {},
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ignore_sensitive_headers=True,
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),
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error=None,
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)
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)
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except Exception as e:
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self.model_call_details[
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"raw_request_typed_dict"
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] = RawRequestTypedDict(
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error=str(e),
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self.model_call_details["raw_request_typed_dict"] = (
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RawRequestTypedDict(
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error=str(e),
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)
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)
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_metadata[
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"raw_request"
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] = "Unable to Log \
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_metadata["raw_request"] = (
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"Unable to Log \
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raw request: {}".format(
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str(e)
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str(e)
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)
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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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@ -1120,9 +1120,9 @@ class Logging(LiteLLMLoggingBaseClass):
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verbose_logger.debug(
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f"response_cost_failure_debug_information: {debug_info}"
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)
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self.model_call_details[
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"response_cost_failure_debug_information"
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] = debug_info
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self.model_call_details["response_cost_failure_debug_information"] = (
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debug_info
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)
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return None
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try:
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@ -1147,9 +1147,9 @@ class Logging(LiteLLMLoggingBaseClass):
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verbose_logger.debug(
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f"response_cost_failure_debug_information: {debug_info}"
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)
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self.model_call_details[
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"response_cost_failure_debug_information"
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] = debug_info
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self.model_call_details["response_cost_failure_debug_information"] = (
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debug_info
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)
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return None
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@ -1238,9 +1238,9 @@ class Logging(LiteLLMLoggingBaseClass):
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end_time = datetime.datetime.now()
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if self.completion_start_time is None:
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self.completion_start_time = end_time
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self.model_call_details[
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"completion_start_time"
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] = self.completion_start_time
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self.model_call_details["completion_start_time"] = (
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self.completion_start_time
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)
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self.model_call_details["log_event_type"] = "successful_api_call"
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self.model_call_details["end_time"] = end_time
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self.model_call_details["cache_hit"] = cache_hit
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@ -1320,39 +1320,39 @@ class Logging(LiteLLMLoggingBaseClass):
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"response_cost"
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]
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else:
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self.model_call_details[
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"response_cost"
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] = self._response_cost_calculator(result=logging_result)
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self.model_call_details["response_cost"] = (
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self._response_cost_calculator(result=logging_result)
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)
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## STANDARDIZED LOGGING PAYLOAD
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self.model_call_details[
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"standard_logging_object"
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] = get_standard_logging_object_payload(
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kwargs=self.model_call_details,
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init_response_obj=logging_result,
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start_time=start_time,
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end_time=end_time,
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logging_obj=self,
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status="success",
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standard_built_in_tools_params=self.standard_built_in_tools_params,
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self.model_call_details["standard_logging_object"] = (
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get_standard_logging_object_payload(
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kwargs=self.model_call_details,
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init_response_obj=logging_result,
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start_time=start_time,
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end_time=end_time,
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logging_obj=self,
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status="success",
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standard_built_in_tools_params=self.standard_built_in_tools_params,
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)
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)
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elif isinstance(result, dict) or isinstance(result, list):
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## STANDARDIZED LOGGING PAYLOAD
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self.model_call_details[
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"standard_logging_object"
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] = get_standard_logging_object_payload(
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kwargs=self.model_call_details,
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init_response_obj=result,
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start_time=start_time,
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end_time=end_time,
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logging_obj=self,
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status="success",
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standard_built_in_tools_params=self.standard_built_in_tools_params,
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self.model_call_details["standard_logging_object"] = (
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get_standard_logging_object_payload(
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kwargs=self.model_call_details,
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init_response_obj=result,
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start_time=start_time,
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end_time=end_time,
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logging_obj=self,
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status="success",
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standard_built_in_tools_params=self.standard_built_in_tools_params,
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)
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)
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elif standard_logging_object is not None:
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self.model_call_details[
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"standard_logging_object"
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] = standard_logging_object
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self.model_call_details["standard_logging_object"] = (
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standard_logging_object
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)
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else: # streaming chunks + image gen.
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self.model_call_details["response_cost"] = None
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@ -1424,23 +1424,23 @@ class Logging(LiteLLMLoggingBaseClass):
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verbose_logger.debug(
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"Logging Details LiteLLM-Success Call streaming complete"
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)
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self.model_call_details[
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"complete_streaming_response"
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] = complete_streaming_response
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self.model_call_details[
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"response_cost"
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] = self._response_cost_calculator(result=complete_streaming_response)
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self.model_call_details["complete_streaming_response"] = (
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complete_streaming_response
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)
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self.model_call_details["response_cost"] = (
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self._response_cost_calculator(result=complete_streaming_response)
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)
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## STANDARDIZED LOGGING PAYLOAD
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self.model_call_details[
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"standard_logging_object"
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] = get_standard_logging_object_payload(
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kwargs=self.model_call_details,
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init_response_obj=complete_streaming_response,
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start_time=start_time,
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end_time=end_time,
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logging_obj=self,
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status="success",
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standard_built_in_tools_params=self.standard_built_in_tools_params,
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self.model_call_details["standard_logging_object"] = (
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get_standard_logging_object_payload(
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kwargs=self.model_call_details,
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init_response_obj=complete_streaming_response,
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start_time=start_time,
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end_time=end_time,
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logging_obj=self,
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status="success",
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standard_built_in_tools_params=self.standard_built_in_tools_params,
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)
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)
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callbacks = self.get_combined_callback_list(
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dynamic_success_callbacks=self.dynamic_success_callbacks,
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@ -1761,10 +1761,10 @@ class Logging(LiteLLMLoggingBaseClass):
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)
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else:
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if self.stream and complete_streaming_response:
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self.model_call_details[
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"complete_response"
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] = self.model_call_details.get(
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"complete_streaming_response", {}
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self.model_call_details["complete_response"] = (
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self.model_call_details.get(
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"complete_streaming_response", {}
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)
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)
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result = self.model_call_details["complete_response"]
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openMeterLogger.log_success_event(
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@ -1803,10 +1803,10 @@ class Logging(LiteLLMLoggingBaseClass):
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)
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else:
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if self.stream and complete_streaming_response:
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self.model_call_details[
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"complete_response"
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] = self.model_call_details.get(
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"complete_streaming_response", {}
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self.model_call_details["complete_response"] = (
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self.model_call_details.get(
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"complete_streaming_response", {}
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)
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)
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result = self.model_call_details["complete_response"]
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@ -1917,9 +1917,9 @@ class Logging(LiteLLMLoggingBaseClass):
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if complete_streaming_response is not None:
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print_verbose("Async success callbacks: Got a complete streaming response")
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self.model_call_details[
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"async_complete_streaming_response"
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] = complete_streaming_response
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self.model_call_details["async_complete_streaming_response"] = (
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complete_streaming_response
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)
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try:
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if self.model_call_details.get("cache_hit", False) is True:
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self.model_call_details["response_cost"] = 0.0
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@ -1929,10 +1929,10 @@ class Logging(LiteLLMLoggingBaseClass):
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model_call_details=self.model_call_details
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)
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# base_model defaults to None if not set on model_info
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self.model_call_details[
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"response_cost"
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] = self._response_cost_calculator(
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result=complete_streaming_response
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self.model_call_details["response_cost"] = (
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self._response_cost_calculator(
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result=complete_streaming_response
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)
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)
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verbose_logger.debug(
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@ -1945,16 +1945,16 @@ class Logging(LiteLLMLoggingBaseClass):
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self.model_call_details["response_cost"] = None
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## STANDARDIZED LOGGING PAYLOAD
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self.model_call_details[
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"standard_logging_object"
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] = get_standard_logging_object_payload(
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kwargs=self.model_call_details,
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init_response_obj=complete_streaming_response,
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start_time=start_time,
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end_time=end_time,
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logging_obj=self,
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status="success",
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standard_built_in_tools_params=self.standard_built_in_tools_params,
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self.model_call_details["standard_logging_object"] = (
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get_standard_logging_object_payload(
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kwargs=self.model_call_details,
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init_response_obj=complete_streaming_response,
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start_time=start_time,
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end_time=end_time,
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logging_obj=self,
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status="success",
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standard_built_in_tools_params=self.standard_built_in_tools_params,
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)
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)
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callbacks = self.get_combined_callback_list(
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dynamic_success_callbacks=self.dynamic_async_success_callbacks,
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|
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@ -2161,18 +2161,18 @@ class Logging(LiteLLMLoggingBaseClass):
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## STANDARDIZED LOGGING PAYLOAD
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self.model_call_details[
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"standard_logging_object"
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] = get_standard_logging_object_payload(
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kwargs=self.model_call_details,
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init_response_obj={},
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start_time=start_time,
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end_time=end_time,
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logging_obj=self,
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status="failure",
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error_str=str(exception),
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original_exception=exception,
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standard_built_in_tools_params=self.standard_built_in_tools_params,
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self.model_call_details["standard_logging_object"] = (
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get_standard_logging_object_payload(
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kwargs=self.model_call_details,
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init_response_obj={},
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start_time=start_time,
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end_time=end_time,
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logging_obj=self,
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status="failure",
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error_str=str(exception),
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original_exception=exception,
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standard_built_in_tools_params=self.standard_built_in_tools_params,
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)
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)
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return start_time, end_time
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|
|
@ -2990,9 +2990,9 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
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endpoint=arize_config.endpoint,
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)
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os.environ[
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"OTEL_EXPORTER_OTLP_TRACES_HEADERS"
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] = f"space_id={arize_config.space_key},api_key={arize_config.api_key}"
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os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = (
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f"space_id={arize_config.space_key},api_key={arize_config.api_key}"
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)
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for callback in _in_memory_loggers:
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if (
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isinstance(callback, ArizeLogger)
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|
|
@ -3016,9 +3016,9 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
|
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|
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# auth can be disabled on local deployments of arize phoenix
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if arize_phoenix_config.otlp_auth_headers is not None:
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os.environ[
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"OTEL_EXPORTER_OTLP_TRACES_HEADERS"
|
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] = arize_phoenix_config.otlp_auth_headers
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os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = (
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arize_phoenix_config.otlp_auth_headers
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)
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for callback in _in_memory_loggers:
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if (
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|
|
@ -3118,9 +3118,9 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
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exporter="otlp_http",
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endpoint="https://langtrace.ai/api/trace",
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)
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os.environ[
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"OTEL_EXPORTER_OTLP_TRACES_HEADERS"
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] = f"api_key={os.getenv('LANGTRACE_API_KEY')}"
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os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = (
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f"api_key={os.getenv('LANGTRACE_API_KEY')}"
|
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)
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for callback in _in_memory_loggers:
|
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if (
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isinstance(callback, OpenTelemetry)
|
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|
|
@ -3154,7 +3154,7 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
|
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)
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|
||||
langfuse_otel_config = LangfuseOtelLogger.get_langfuse_otel_config()
|
||||
|
||||
|
||||
# The endpoint and headers are now set as environment variables by get_langfuse_otel_config()
|
||||
otel_config = OpenTelemetryConfig(
|
||||
exporter=langfuse_otel_config.protocol,
|
||||
|
|
@ -3720,10 +3720,10 @@ class StandardLoggingPayloadSetup:
|
|||
for key in StandardLoggingHiddenParams.__annotations__.keys():
|
||||
if key in hidden_params:
|
||||
if key == "additional_headers":
|
||||
clean_hidden_params[
|
||||
"additional_headers"
|
||||
] = StandardLoggingPayloadSetup.get_additional_headers(
|
||||
hidden_params[key]
|
||||
clean_hidden_params["additional_headers"] = (
|
||||
StandardLoggingPayloadSetup.get_additional_headers(
|
||||
hidden_params[key]
|
||||
)
|
||||
)
|
||||
else:
|
||||
clean_hidden_params[key] = hidden_params[key] # type: ignore
|
||||
|
|
@ -3815,6 +3815,28 @@ class StandardLoggingPayloadSetup:
|
|||
else:
|
||||
return logging_obj.litellm_trace_id
|
||||
|
||||
@staticmethod
|
||||
def _get_user_agent_tags(proxy_server_request: dict) -> Optional[List[str]]:
|
||||
"""
|
||||
Return the user agent tags from the proxy server request for spend tracking
|
||||
"""
|
||||
user_agent_tags: Optional[List[str]] = None
|
||||
headers = proxy_server_request.get("headers", {})
|
||||
if headers is not None and isinstance(headers, dict):
|
||||
if "user-agent" in headers:
|
||||
user_agent = headers["user-agent"]
|
||||
if user_agent is not None:
|
||||
if user_agent_tags is None:
|
||||
user_agent_tags = []
|
||||
user_agent_part: Optional[str] = None
|
||||
if "/" in user_agent:
|
||||
user_agent_part = user_agent.split("/")[0]
|
||||
if user_agent_part is not None:
|
||||
user_agent_tags.append(user_agent_part)
|
||||
if user_agent is not None:
|
||||
user_agent_tags.append(user_agent)
|
||||
return user_agent_tags
|
||||
|
||||
|
||||
def get_standard_logging_object_payload(
|
||||
kwargs: Optional[dict],
|
||||
|
|
@ -3890,6 +3912,11 @@ def get_standard_logging_object_payload(
|
|||
if isinstance(metadata.get("tags", []), list)
|
||||
else []
|
||||
)
|
||||
user_agent_tags = StandardLoggingPayloadSetup._get_user_agent_tags(
|
||||
proxy_server_request
|
||||
)
|
||||
if user_agent_tags is not None:
|
||||
request_tags.extend(user_agent_tags)
|
||||
|
||||
# cleanup timestamps
|
||||
(
|
||||
|
|
@ -4103,9 +4130,9 @@ def scrub_sensitive_keys_in_metadata(litellm_params: Optional[dict]):
|
|||
):
|
||||
for k, v in metadata["user_api_key_metadata"].items():
|
||||
if k == "logging": # prevent logging user logging keys
|
||||
cleaned_user_api_key_metadata[
|
||||
k
|
||||
] = "scrubbed_by_litellm_for_sensitive_keys"
|
||||
cleaned_user_api_key_metadata[k] = (
|
||||
"scrubbed_by_litellm_for_sensitive_keys"
|
||||
)
|
||||
else:
|
||||
cleaned_user_api_key_metadata[k] = v
|
||||
|
||||
|
|
|
|||
|
|
@ -771,10 +771,12 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM):
|
|||
status_code = getattr(e, "status_code", 500)
|
||||
error_headers = getattr(e, "headers", None)
|
||||
error_response = getattr(e, "response", None)
|
||||
error_text = str(e)
|
||||
if error_headers is None and error_response:
|
||||
error_headers = getattr(error_response, "headers", None)
|
||||
error_text = error_response.text
|
||||
raise AzureOpenAIError(
|
||||
status_code=status_code, message=str(e), headers=error_headers
|
||||
status_code=status_code, message=error_text, headers=error_headers
|
||||
)
|
||||
|
||||
async def make_async_azure_httpx_request(
|
||||
|
|
|
|||
|
|
@ -162,7 +162,7 @@ def get_azure_ad_token_from_oidc(
|
|||
azure_ad_token: str,
|
||||
azure_client_id: Optional[str],
|
||||
azure_tenant_id: Optional[str],
|
||||
scope: str = "https://cognitiveservices.azure.com/.default",
|
||||
scope: Optional[str] = None,
|
||||
) -> str:
|
||||
"""
|
||||
Get Azure AD token from OIDC token
|
||||
|
|
@ -176,6 +176,8 @@ def get_azure_ad_token_from_oidc(
|
|||
Returns:
|
||||
`azure_ad_token_access_token` - str
|
||||
"""
|
||||
if scope is None:
|
||||
scope = "https://cognitiveservices.azure.com/.default"
|
||||
azure_authority_host = os.getenv(
|
||||
"AZURE_AUTHORITY_HOST", "https://login.microsoftonline.com"
|
||||
)
|
||||
|
|
@ -209,6 +211,7 @@ def get_azure_ad_token_from_oidc(
|
|||
return azure_ad_token_access_token
|
||||
|
||||
client = litellm.module_level_client
|
||||
|
||||
req_token = client.post(
|
||||
f"{azure_authority_host}/{azure_tenant_id}/oauth2/v2.0/token",
|
||||
data={
|
||||
|
|
@ -338,13 +341,19 @@ class BaseAzureLLM(BaseOpenAILLM):
|
|||
"azure_password", os.getenv("AZURE_PASSWORD")
|
||||
)
|
||||
scope = litellm_params.get(
|
||||
"azure_scope", os.getenv("AZURE_SCOPE", "https://cognitiveservices.azure.com/.default"))
|
||||
"azure_scope",
|
||||
os.getenv("AZURE_SCOPE", "https://cognitiveservices.azure.com/.default"),
|
||||
)
|
||||
if scope is None:
|
||||
scope = "https://cognitiveservices.azure.com/.default"
|
||||
max_retries = litellm_params.get("max_retries")
|
||||
timeout = litellm_params.get("timeout")
|
||||
if (
|
||||
not api_key
|
||||
and azure_ad_token_provider is None
|
||||
and tenant_id and client_id and client_secret
|
||||
and tenant_id
|
||||
and client_id
|
||||
and client_secret
|
||||
):
|
||||
verbose_logger.debug(
|
||||
"Using Azure AD Token Provider from Entra ID for Azure Auth"
|
||||
|
|
@ -355,7 +364,12 @@ class BaseAzureLLM(BaseOpenAILLM):
|
|||
client_secret=client_secret,
|
||||
scope=scope,
|
||||
)
|
||||
if azure_ad_token_provider is None and azure_username and azure_password and client_id:
|
||||
if (
|
||||
azure_ad_token_provider is None
|
||||
and azure_username
|
||||
and azure_password
|
||||
and client_id
|
||||
):
|
||||
verbose_logger.debug("Using Azure Username and Password for Azure Auth")
|
||||
azure_ad_token_provider = get_azure_ad_token_from_username_password(
|
||||
azure_username=azure_username,
|
||||
|
|
@ -381,7 +395,7 @@ class BaseAzureLLM(BaseOpenAILLM):
|
|||
"Using Azure AD token provider based on Service Principal with Secret workflow for Azure Auth"
|
||||
)
|
||||
try:
|
||||
azure_ad_token_provider = get_azure_ad_token_provider()
|
||||
azure_ad_token_provider = get_azure_ad_token_provider(azure_scope=scope)
|
||||
except ValueError:
|
||||
verbose_logger.debug("Azure AD Token Provider could not be used.")
|
||||
if api_version is None:
|
||||
|
|
@ -442,8 +456,10 @@ class BaseAzureLLM(BaseOpenAILLM):
|
|||
## build base url - assume api base includes resource name
|
||||
tenant_id = litellm_params.get("tenant_id", os.getenv("AZURE_TENANT_ID"))
|
||||
client_id = litellm_params.get("client_id", os.getenv("AZURE_CLIENT_ID"))
|
||||
scope = litellm_params.get("azure_scope", os.getenv(
|
||||
"AZURE_SCOPE", "https://cognitiveservices.azure.com/.default"))
|
||||
scope = litellm_params.get(
|
||||
"azure_scope",
|
||||
os.getenv("AZURE_SCOPE", "https://cognitiveservices.azure.com/.default"),
|
||||
)
|
||||
if client is None:
|
||||
if not api_base.endswith("/"):
|
||||
api_base += "/"
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
|
|
@ -1 +1 @@
|
|||
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|
||||
|
|
@ -1 +1 @@
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|
|
@ -390,11 +390,11 @@ class LiteLLMProxyRequestSetup:
|
|||
|
||||
## KEY-LEVEL SPEND LOGS / TAGS
|
||||
if "tags" in key_metadata and key_metadata["tags"] is not None:
|
||||
data[_metadata_variable_name][
|
||||
"tags"
|
||||
] = LiteLLMProxyRequestSetup._merge_tags(
|
||||
request_tags=data[_metadata_variable_name].get("tags"),
|
||||
tags_to_add=key_metadata["tags"],
|
||||
data[_metadata_variable_name]["tags"] = (
|
||||
LiteLLMProxyRequestSetup._merge_tags(
|
||||
request_tags=data[_metadata_variable_name].get("tags"),
|
||||
tags_to_add=key_metadata["tags"],
|
||||
)
|
||||
)
|
||||
if "spend_logs_metadata" in key_metadata and isinstance(
|
||||
key_metadata["spend_logs_metadata"], dict
|
||||
|
|
@ -483,23 +483,6 @@ class LiteLLMProxyRequestSetup:
|
|||
tags = [tag.strip() for tag in _tags]
|
||||
elif isinstance(headers["x-litellm-tags"], list):
|
||||
tags = headers["x-litellm-tags"]
|
||||
if "user-agent" in headers:
|
||||
"""
|
||||
Allow tracking spend by cli tools like Claude Code - e.g. "claude-cli/1.0.25 (external, cli)"
|
||||
"""
|
||||
# add user-agent to tags
|
||||
if tags is None:
|
||||
tags = []
|
||||
user_agent = headers["user-agent"]
|
||||
if user_agent is not None:
|
||||
user_agent_part: Optional[str] = None
|
||||
if "/" in user_agent:
|
||||
user_agent_part = user_agent.split("/")[
|
||||
0
|
||||
] # extract "claude-cli" - enables spend tracking acrosss versions
|
||||
if user_agent_part is not None:
|
||||
tags.append(user_agent_part)
|
||||
tags.append(user_agent) # append full user-agent
|
||||
# Check request body for tags
|
||||
if "tags" in data and isinstance(data["tags"], list):
|
||||
tags = data["tags"]
|
||||
|
|
@ -630,9 +613,9 @@ async def add_litellm_data_to_request( # noqa: PLR0915
|
|||
data[_metadata_variable_name]["litellm_api_version"] = version
|
||||
|
||||
if general_settings is not None:
|
||||
data[_metadata_variable_name][
|
||||
"global_max_parallel_requests"
|
||||
] = general_settings.get("global_max_parallel_requests", None)
|
||||
data[_metadata_variable_name]["global_max_parallel_requests"] = (
|
||||
general_settings.get("global_max_parallel_requests", None)
|
||||
)
|
||||
|
||||
### KEY-LEVEL Controls
|
||||
key_metadata = user_api_key_dict.metadata
|
||||
|
|
|
|||
|
|
@ -32,9 +32,14 @@ def get_azure_ad_token_provider(azure_scope: Optional[str] = None) -> Callable[[
|
|||
)
|
||||
|
||||
if azure_scope is None:
|
||||
azure_scope = os.environ.get("AZURE_SCOPE", "https://cognitiveservices.azure.com/.default")
|
||||
azure_scope = (
|
||||
os.environ.get("AZURE_SCOPE")
|
||||
or "https://cognitiveservices.azure.com/.default"
|
||||
)
|
||||
|
||||
cred: str = os.environ.get("AZURE_CREDENTIAL", AzureCredentialType.ClientSecretCredential)
|
||||
cred: str = os.environ.get(
|
||||
"AZURE_CREDENTIAL", AzureCredentialType.ClientSecretCredential
|
||||
)
|
||||
credential: Optional[
|
||||
Union[
|
||||
ClientSecretCredential,
|
||||
|
|
|
|||
|
|
@ -149,7 +149,9 @@ async def test_openai_azure_embedding_simple(model, api_base, api_key, sync_mode
|
|||
|
||||
assert isinstance(response.usage, litellm.Usage)
|
||||
except litellm.BadRequestError:
|
||||
print("Bad request error occurred - Together AI raises 404s for their embedding models")
|
||||
print(
|
||||
"Bad request error occurred - Together AI raises 404s for their embedding models"
|
||||
)
|
||||
pass
|
||||
|
||||
except Exception as e:
|
||||
|
|
@ -319,7 +321,7 @@ def test_openai_azure_embedding():
|
|||
os.environ.get("CIRCLE_OIDC_TOKEN") is None,
|
||||
reason="Cannot run without being in CircleCI Runner",
|
||||
)
|
||||
def test_openai_azure_embedding_with_oidc_and_cf():
|
||||
def test_aaaaaa_openai_azure_embedding_with_oidc_and_cf():
|
||||
# TODO: Switch to our own Azure account, currently using ai.moda's account
|
||||
os.environ["AZURE_TENANT_ID"] = "17c0a27a-1246-4aa1-a3b6-d294e80e783c"
|
||||
os.environ["AZURE_CLIENT_ID"] = "4faf5422-b2bd-45e8-a6d7-46543a38acd0"
|
||||
|
|
@ -646,7 +648,9 @@ from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
|
|||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@patch("litellm.llms.huggingface.embedding.handler.async_get_hf_task_embedding_for_model")
|
||||
@patch(
|
||||
"litellm.llms.huggingface.embedding.handler.async_get_hf_task_embedding_for_model"
|
||||
)
|
||||
@patch("litellm.llms.huggingface.embedding.handler.get_hf_task_embedding_for_model")
|
||||
@pytest.mark.parametrize("sync_mode", [True, False])
|
||||
async def test_hf_embedding_sentence_sim(
|
||||
|
|
@ -1110,10 +1114,13 @@ def test_embedding_response_ratelimit_headers(model):
|
|||
|
||||
# Azure is flaky with returning x-ratelimit-remaining-requests, we need to verify the upstream api returns this header
|
||||
# if upstream api returns this header, we need to verify the header is transformed by litellm
|
||||
if "llm_provider-x-ratelimit-limit-requests" in additional_headers or "x-ratelimit-limit-requests" in additional_headers:
|
||||
if (
|
||||
"llm_provider-x-ratelimit-limit-requests" in additional_headers
|
||||
or "x-ratelimit-limit-requests" in additional_headers
|
||||
):
|
||||
assert "x-ratelimit-remaining-requests" in additional_headers
|
||||
assert int(additional_headers["x-ratelimit-remaining-requests"]) > 0
|
||||
|
||||
|
||||
assert "x-ratelimit-remaining-tokens" in additional_headers
|
||||
assert int(additional_headers["x-ratelimit-remaining-tokens"]) > 0
|
||||
|
||||
|
|
@ -1172,4 +1179,3 @@ async def test_embedding_with_extra_headers(sync_mode):
|
|||
|
||||
mock_post.assert_called_once()
|
||||
assert "my-test-param" in mock_post.call_args.kwargs["headers"]
|
||||
|
||||
|
|
@ -83,7 +83,7 @@ def test_initialize_with_tenant_credentials_env_var(setup_mocks, monkeypatch):
|
|||
monkeypatch.setenv("AZURE_CLIENT_ID", "test-client-id")
|
||||
monkeypatch.setenv("AZURE_CLIENT_SECRET", "test-client-secret")
|
||||
monkeypatch.setenv("AZURE_SCOPE", "test-azure-scope")
|
||||
|
||||
|
||||
result = BaseAzureLLM().initialize_azure_sdk_client(
|
||||
litellm_params={},
|
||||
api_key=None,
|
||||
|
|
@ -98,7 +98,7 @@ def test_initialize_with_tenant_credentials_env_var(setup_mocks, monkeypatch):
|
|||
tenant_id="test-tenant-id",
|
||||
client_id="test-client-id",
|
||||
client_secret="test-client-secret",
|
||||
scope="test-azure-scope"
|
||||
scope="test-azure-scope",
|
||||
)
|
||||
|
||||
# Verify expected result
|
||||
|
|
@ -151,7 +151,7 @@ def test_initialize_with_username_password(monkeypatch, setup_mocks):
|
|||
"azure_username": "test-username",
|
||||
"azure_password": "test-password",
|
||||
"client_id": "test-client-id",
|
||||
"azure_scope": "test-azure-scope"
|
||||
"azure_scope": "test-azure-scope",
|
||||
},
|
||||
api_key=None,
|
||||
api_base="https://test.openai.azure.com",
|
||||
|
|
@ -173,7 +173,7 @@ def test_initialize_with_username_password(monkeypatch, setup_mocks):
|
|||
azure_username="test-username",
|
||||
azure_password="test-password",
|
||||
client_id="test-client-id",
|
||||
scope="test-azure-scope"
|
||||
scope="test-azure-scope",
|
||||
)
|
||||
|
||||
# Verify expected result
|
||||
|
|
@ -184,7 +184,7 @@ def test_initialize_with_oidc_token(setup_mocks, monkeypatch):
|
|||
monkeypatch.delenv("AZURE_CLIENT_ID", raising=False)
|
||||
monkeypatch.delenv("AZURE_TENANT_ID", raising=False)
|
||||
monkeypatch.delenv("AZURE_SCOPE", raising=False)
|
||||
|
||||
|
||||
# Test with azure_ad_token that starts with "oidc/"
|
||||
result = BaseAzureLLM().initialize_azure_sdk_client(
|
||||
litellm_params={"azure_ad_token": "oidc/test-token"},
|
||||
|
|
@ -196,8 +196,10 @@ def test_initialize_with_oidc_token(setup_mocks, monkeypatch):
|
|||
)
|
||||
|
||||
setup_mocks["oidc_token"].assert_called_once_with(
|
||||
azure_ad_token="oidc/test-token", azure_client_id=None, azure_tenant_id=None,
|
||||
scope="https://cognitiveservices.azure.com/.default"
|
||||
azure_ad_token="oidc/test-token",
|
||||
azure_client_id=None,
|
||||
azure_tenant_id=None,
|
||||
scope="https://cognitiveservices.azure.com/.default",
|
||||
)
|
||||
|
||||
# Verify expected result
|
||||
|
|
@ -225,7 +227,7 @@ def test_initialize_with_oidc_token_and_client_params(setup_mocks):
|
|||
azure_ad_token="oidc/test-token",
|
||||
azure_client_id="test-client-id",
|
||||
azure_tenant_id="test-tenant-id",
|
||||
scope="test-azure-scope"
|
||||
scope="test-azure-scope",
|
||||
)
|
||||
|
||||
# Verify expected result
|
||||
|
|
@ -254,7 +256,7 @@ def test_initialize_with_oidc_token_fallback_to_env(setup_mocks, monkeypatch):
|
|||
azure_ad_token="oidc/test-token",
|
||||
azure_client_id="env-client-id",
|
||||
azure_tenant_id="env-tenant-id",
|
||||
scope="https://cognitiveservices.azure.com/.default"
|
||||
scope="https://cognitiveservices.azure.com/.default",
|
||||
)
|
||||
|
||||
# Verify expected result
|
||||
|
|
@ -281,8 +283,10 @@ def test_initialize_with_oidc_token_no_credentials(setup_mocks, monkeypatch):
|
|||
|
||||
# Verify that get_azure_ad_token_from_oidc was called with None values
|
||||
setup_mocks["oidc_token"].assert_called_once_with(
|
||||
azure_ad_token="oidc/test-token", azure_client_id=None, azure_tenant_id=None,
|
||||
scope="https://cognitiveservices.azure.com/.default"
|
||||
azure_ad_token="oidc/test-token",
|
||||
azure_client_id=None,
|
||||
azure_tenant_id=None,
|
||||
scope="https://cognitiveservices.azure.com/.default",
|
||||
)
|
||||
|
||||
# Verify expected result
|
||||
|
|
@ -891,3 +895,102 @@ async def test_azure_client_cache_separates_sync_and_async():
|
|||
assert (
|
||||
mock_init_azure.call_count == 2
|
||||
), "initialize_azure_sdk_client should be called twice"
|
||||
|
||||
|
||||
def test_scope_always_string_in_initialize_azure_sdk_client(setup_mocks, monkeypatch):
|
||||
"""
|
||||
Test that the scope parameter in initialize_azure_sdk_client is always a string,
|
||||
regardless of the input provided (None, empty string, etc.).
|
||||
"""
|
||||
# Clear environment variables to ensure clean test state
|
||||
monkeypatch.delenv("AZURE_SCOPE", raising=False)
|
||||
|
||||
base_llm = BaseAzureLLM()
|
||||
expected_default_scope = "https://cognitiveservices.azure.com/.default"
|
||||
|
||||
# Test case 1: scope is None in litellm_params
|
||||
result = base_llm.initialize_azure_sdk_client(
|
||||
litellm_params={"azure_scope": None},
|
||||
api_key="test-api-key",
|
||||
api_base="https://test.openai.azure.com",
|
||||
model_name="gpt-4",
|
||||
api_version="2023-06-01",
|
||||
is_async=False,
|
||||
)
|
||||
|
||||
# Verify scope is a string and has the expected default value
|
||||
# We need to check the internal logic by inspecting what was passed to mocked functions
|
||||
setup_mocks["select_url"].assert_called()
|
||||
call_args = setup_mocks["select_url"].call_args[1]["azure_client_params"]
|
||||
# The scope should be used internally when setting up token providers
|
||||
|
||||
# Test case 2: azure_scope key is missing entirely
|
||||
result = base_llm.initialize_azure_sdk_client(
|
||||
litellm_params={},
|
||||
api_key="test-api-key",
|
||||
api_base="https://test.openai.azure.com",
|
||||
model_name="gpt-4",
|
||||
api_version="2023-06-01",
|
||||
is_async=False,
|
||||
)
|
||||
|
||||
# Test case 3: azure_scope is an empty string
|
||||
result = base_llm.initialize_azure_sdk_client(
|
||||
litellm_params={"azure_scope": ""},
|
||||
api_key="test-api-key",
|
||||
api_base="https://test.openai.azure.com",
|
||||
model_name="gpt-4",
|
||||
api_version="2023-06-01",
|
||||
is_async=False,
|
||||
)
|
||||
|
||||
# Test case 4: azure_scope is a valid custom string
|
||||
custom_scope = "https://custom.scope.com/.default"
|
||||
result = base_llm.initialize_azure_sdk_client(
|
||||
litellm_params={"azure_scope": custom_scope},
|
||||
api_key="test-api-key",
|
||||
api_base="https://test.openai.azure.com",
|
||||
model_name="gpt-4",
|
||||
api_version="2023-06-01",
|
||||
is_async=False,
|
||||
)
|
||||
|
||||
# Test case 5: Test with token authentication to verify scope is passed correctly
|
||||
setup_mocks["entra_token"].reset_mock()
|
||||
result = base_llm.initialize_azure_sdk_client(
|
||||
litellm_params={
|
||||
"azure_scope": None, # This should default to the expected scope
|
||||
"tenant_id": "test-tenant",
|
||||
"client_id": "test-client",
|
||||
"client_secret": "test-secret",
|
||||
},
|
||||
api_key=None, # No API key to trigger token authentication
|
||||
api_base="https://test.openai.azure.com",
|
||||
model_name="gpt-4",
|
||||
api_version="2023-06-01",
|
||||
is_async=False,
|
||||
)
|
||||
|
||||
# Verify that the token function was called with a string scope
|
||||
setup_mocks["entra_token"].assert_called_once()
|
||||
call_args = setup_mocks["entra_token"].call_args
|
||||
scope_arg = call_args[1]["scope"] # scope should be passed as keyword argument
|
||||
assert isinstance(
|
||||
scope_arg, str
|
||||
), f"Scope should be a string, got {type(scope_arg)}"
|
||||
assert (
|
||||
scope_arg == expected_default_scope
|
||||
), f"Scope should be {expected_default_scope}, got {scope_arg}"
|
||||
|
||||
# Test case 6: Test with environment variable set to None (edge case)
|
||||
monkeypatch.setenv("AZURE_SCOPE", "")
|
||||
result = base_llm.initialize_azure_sdk_client(
|
||||
litellm_params={"azure_scope": None},
|
||||
api_key="test-api-key",
|
||||
api_base="https://test.openai.azure.com",
|
||||
model_name="gpt-4",
|
||||
api_version="2023-06-01",
|
||||
is_async=False,
|
||||
)
|
||||
|
||||
print("All scope tests passed - scope is always a string")
|
||||
|
|
|
|||
|
|
@ -216,83 +216,3 @@ async def test_add_litellm_data_to_request_audio_transcription_multipart():
|
|||
"jobID:214590dsff09fds",
|
||||
"taskName:run_page_classification",
|
||||
]
|
||||
|
||||
|
||||
def test_add_request_tag_to_metadata_user_agent_parsing():
|
||||
"""
|
||||
Test that user agent parsing works correctly in add_request_tag_to_metadata
|
||||
"""
|
||||
|
||||
# Test case 1: User agent with version (contains "/")
|
||||
headers_with_version = {"user-agent": "claude-cli/1.0.25 (external, cli)"}
|
||||
data = {}
|
||||
result = LiteLLMProxyRequestSetup.add_request_tag_to_metadata(
|
||||
llm_router=None,
|
||||
headers=headers_with_version,
|
||||
data=data,
|
||||
)
|
||||
expected_tags = ["claude-cli", "claude-cli/1.0.25 (external, cli)"]
|
||||
assert result == expected_tags
|
||||
|
||||
# Test case 2: User agent without version (no "/")
|
||||
headers_without_version = {"user-agent": "my-custom-client"}
|
||||
data = {}
|
||||
result = LiteLLMProxyRequestSetup.add_request_tag_to_metadata(
|
||||
llm_router=None,
|
||||
headers=headers_without_version,
|
||||
data=data,
|
||||
)
|
||||
expected_tags = ["my-custom-client"]
|
||||
assert result == expected_tags
|
||||
|
||||
# Test case 3: No user agent header
|
||||
headers_no_user_agent = {}
|
||||
data = {}
|
||||
result = LiteLLMProxyRequestSetup.add_request_tag_to_metadata(
|
||||
llm_router=None,
|
||||
headers=headers_no_user_agent,
|
||||
data=data,
|
||||
)
|
||||
assert result is None
|
||||
|
||||
# Test case 4: User agent with existing x-litellm-tags
|
||||
headers_with_existing_tags = {
|
||||
"user-agent": "postman/7.36.1",
|
||||
"x-litellm-tags": "existing-tag1, existing-tag2",
|
||||
}
|
||||
data = {}
|
||||
result = LiteLLMProxyRequestSetup.add_request_tag_to_metadata(
|
||||
llm_router=None,
|
||||
headers=headers_with_existing_tags,
|
||||
data=data,
|
||||
)
|
||||
expected_tags = ["existing-tag1", "existing-tag2", "postman", "postman/7.36.1"]
|
||||
assert result == expected_tags
|
||||
|
||||
# Test case 5: User agent with tags in request body (body tags override header tags)
|
||||
headers_with_user_agent = {"user-agent": "curl/7.68.0"}
|
||||
data = {"tags": ["body-tag1", "body-tag2"]}
|
||||
result = LiteLLMProxyRequestSetup.add_request_tag_to_metadata(
|
||||
llm_router=None,
|
||||
headers=headers_with_user_agent,
|
||||
data=data,
|
||||
)
|
||||
# When tags exist in data, they override everything else
|
||||
expected_tags = ["body-tag1", "body-tag2"]
|
||||
assert result == expected_tags
|
||||
|
||||
# Test case 6: Complex user agent string
|
||||
headers_complex = {
|
||||
"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
|
||||
}
|
||||
data = {}
|
||||
result = LiteLLMProxyRequestSetup.add_request_tag_to_metadata(
|
||||
llm_router=None,
|
||||
headers=headers_complex,
|
||||
data=data,
|
||||
)
|
||||
expected_tags = [
|
||||
"Mozilla",
|
||||
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
|
||||
]
|
||||
assert result == expected_tags
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue