mirror of
https://github.com/BerriAI/litellm.git
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fix(ci): black formatting + update OpenAPI compliance tests for spec changes
- Apply Black 26.x formatting to litellm_logging.py (parenthesized style) - Update test_input_types_match_spec to follow $ref to InteractionsInput schema (Google updated their OpenAPI spec to use $ref instead of inline oneOf) - Update test_content_schema_uses_discriminator to handle discriminator without explicit mapping (Google removed the mapping key from Content discriminator) Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
This commit is contained in:
Cursor Agent
parent
233b1d3101
commit
b763c87f46
2 changed files with 156 additions and 146 deletions
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@ -352,9 +352,9 @@ class Logging(LiteLLMLoggingBaseClass):
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)
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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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@ -782,9 +782,9 @@ class Logging(LiteLLMLoggingBaseClass):
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prompt_spec=prompt_spec,
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dynamic_callback_params=dynamic_callback_params,
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):
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self.model_call_details[
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"prompt_integration"
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] = logger.__class__.__name__
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self.model_call_details["prompt_integration"] = (
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logger.__class__.__name__
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)
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return logger
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except Exception:
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# If check fails, continue to next logger
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@ -852,9 +852,9 @@ 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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@ -866,9 +866,9 @@ class Logging(LiteLLMLoggingBaseClass):
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internal_usage_cache=None,
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llm_router=None,
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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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# Add to global callbacks so post-call hooks are invoked
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if (
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vector_store_custom_logger
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@ -928,9 +928,9 @@ 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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@ -959,9 +959,7 @@ 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"] = "redacted by litellm. \
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'litellm.turn_off_message_logging=True'"
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else:
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curl_command = self._get_request_curl_command(
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@ -973,35 +971,31 @@ 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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# NOTE: setting ignore_sensitive_headers to True will cause
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# the Authorization header to be leaked when calls to the health
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# endpoint are made and fail.
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raw_request_headers=self._get_masked_headers(
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additional_args.get("headers", {}) or {},
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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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# NOTE: setting ignore_sensitive_headers to True will cause
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# the Authorization header to be leaked when calls to the health
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# endpoint are made and fail.
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raw_request_headers=self._get_masked_headers(
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additional_args.get("headers", {}) or {},
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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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)
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_metadata[
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"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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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["raw_request"] = "Unable to Log \
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raw request: {}".format(str(e))
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if getattr(self, "logger_fn", None) and callable(self.logger_fn):
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try:
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self.logger_fn(
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@ -1301,13 +1295,13 @@ class Logging(LiteLLMLoggingBaseClass):
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for callback in callbacks:
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try:
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if isinstance(callback, CustomLogger):
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response: Optional[
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MCPPostCallResponseObject
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] = await callback.async_post_mcp_tool_call_hook(
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kwargs=kwargs,
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response_obj=post_mcp_tool_call_response_obj,
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start_time=start_time,
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end_time=end_time,
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response: Optional[MCPPostCallResponseObject] = (
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await callback.async_post_mcp_tool_call_hook(
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kwargs=kwargs,
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response_obj=post_mcp_tool_call_response_obj,
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start_time=start_time,
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end_time=end_time,
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)
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)
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######################################################################
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# if any of the callbacks modify the response, use the modified response
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@ -1502,9 +1496,9 @@ 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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@ -1530,9 +1524,9 @@ 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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@ -1688,9 +1682,9 @@ class Logging(LiteLLMLoggingBaseClass):
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result=logging_result
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)
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self.model_call_details[
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"standard_logging_object"
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] = self._build_standard_logging_payload(logging_result, start_time, end_time)
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self.model_call_details["standard_logging_object"] = (
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self._build_standard_logging_payload(logging_result, start_time, end_time)
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)
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if (
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standard_logging_payload := self.model_call_details.get(
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@ -1768,9 +1762,9 @@ 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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@ -1807,10 +1801,10 @@ class Logging(LiteLLMLoggingBaseClass):
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end_time=end_time,
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)
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elif isinstance(result, dict) or isinstance(result, list):
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self.model_call_details[
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"standard_logging_object"
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] = self._build_standard_logging_payload(
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result, start_time, end_time
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self.model_call_details["standard_logging_object"] = (
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self._build_standard_logging_payload(
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result, start_time, end_time
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)
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)
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if (
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standard_logging_payload := self.model_call_details.get(
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@ -1819,9 +1813,9 @@ class Logging(LiteLLMLoggingBaseClass):
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) is not None:
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emit_standard_logging_payload(standard_logging_payload)
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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:
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self.model_call_details["response_cost"] = None
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@ -1979,17 +1973,17 @@ 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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] = self._build_standard_logging_payload(
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complete_streaming_response, start_time, end_time
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self.model_call_details["standard_logging_object"] = (
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self._build_standard_logging_payload(
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complete_streaming_response, start_time, end_time
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)
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)
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if (
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standard_logging_payload := self.model_call_details.get(
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@ -2323,10 +2317,10 @@ 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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@ -2350,10 +2344,10 @@ 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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@ -2492,9 +2486,9 @@ 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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@ -2505,10 +2499,10 @@ 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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@ -2521,10 +2515,10 @@ 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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] = self._build_standard_logging_payload(
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complete_streaming_response, start_time, end_time
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self.model_call_details["standard_logging_object"] = (
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self._build_standard_logging_payload(
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complete_streaming_response, start_time, end_time
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)
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)
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# print standard logging payload
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@ -2551,9 +2545,9 @@ class Logging(LiteLLMLoggingBaseClass):
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# _success_handler_helper_fn
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if self.model_call_details.get("standard_logging_object") is 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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] = self._build_standard_logging_payload(result, start_time, end_time)
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self.model_call_details["standard_logging_object"] = (
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self._build_standard_logging_payload(result, start_time, end_time)
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)
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# print standard logging payload
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if (
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@ -2796,18 +2790,18 @@ 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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|
|
@ -3771,9 +3765,9 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
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service_name=arize_config.project_name,
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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 or arize_config.space_id},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 or arize_config.space_id},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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|
|
@ -3799,13 +3793,13 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
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existing_attrs = os.environ.get("OTEL_RESOURCE_ATTRIBUTES", "")
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# Add openinference.project.name attribute
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if existing_attrs:
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os.environ[
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"OTEL_RESOURCE_ATTRIBUTES"
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] = f"{existing_attrs},openinference.project.name={arize_phoenix_config.project_name}"
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os.environ["OTEL_RESOURCE_ATTRIBUTES"] = (
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f"{existing_attrs},openinference.project.name={arize_phoenix_config.project_name}"
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)
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else:
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os.environ[
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"OTEL_RESOURCE_ATTRIBUTES"
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] = f"openinference.project.name={arize_phoenix_config.project_name}"
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os.environ["OTEL_RESOURCE_ATTRIBUTES"] = (
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f"openinference.project.name={arize_phoenix_config.project_name}"
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)
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# Set Phoenix project name from environment variable
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phoenix_project_name = os.environ.get("PHOENIX_PROJECT_NAME", None)
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|
|
@ -3813,19 +3807,19 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
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existing_attrs = os.environ.get("OTEL_RESOURCE_ATTRIBUTES", "")
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# Add openinference.project.name attribute
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if existing_attrs:
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os.environ[
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"OTEL_RESOURCE_ATTRIBUTES"
|
||||
] = f"{existing_attrs},openinference.project.name={phoenix_project_name}"
|
||||
os.environ["OTEL_RESOURCE_ATTRIBUTES"] = (
|
||||
f"{existing_attrs},openinference.project.name={phoenix_project_name}"
|
||||
)
|
||||
else:
|
||||
os.environ[
|
||||
"OTEL_RESOURCE_ATTRIBUTES"
|
||||
] = f"openinference.project.name={phoenix_project_name}"
|
||||
os.environ["OTEL_RESOURCE_ATTRIBUTES"] = (
|
||||
f"openinference.project.name={phoenix_project_name}"
|
||||
)
|
||||
|
||||
# auth can be disabled on local deployments of arize phoenix
|
||||
if arize_phoenix_config.otlp_auth_headers is not None:
|
||||
os.environ[
|
||||
"OTEL_EXPORTER_OTLP_TRACES_HEADERS"
|
||||
] = arize_phoenix_config.otlp_auth_headers
|
||||
os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = (
|
||||
arize_phoenix_config.otlp_auth_headers
|
||||
)
|
||||
|
||||
for callback in _in_memory_loggers:
|
||||
if (
|
||||
|
|
@ -4012,9 +4006,9 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
|
|||
exporter="otlp_http",
|
||||
endpoint="https://langtrace.ai/api/trace",
|
||||
)
|
||||
os.environ[
|
||||
"OTEL_EXPORTER_OTLP_TRACES_HEADERS"
|
||||
] = f"api_key={os.getenv('LANGTRACE_API_KEY')}"
|
||||
os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = (
|
||||
f"api_key={os.getenv('LANGTRACE_API_KEY')}"
|
||||
)
|
||||
for callback in _in_memory_loggers:
|
||||
if (
|
||||
isinstance(callback, OpenTelemetry)
|
||||
|
|
@ -4938,10 +4932,10 @@ 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
|
||||
|
|
@ -5580,9 +5574,9 @@ 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
|
||||
|
||||
|
|
|
|||
|
|
@ -77,6 +77,11 @@ class TestRequestCompliance:
|
|||
schema = spec_dict["components"]["schemas"]["CreateModelInteractionParams"]
|
||||
input_schema = schema["properties"]["input"]
|
||||
|
||||
# The input property may be inline oneOf or a $ref to InteractionsInput
|
||||
if "$ref" in input_schema:
|
||||
ref_name = input_schema["$ref"].split("/")[-1]
|
||||
input_schema = spec_dict["components"]["schemas"][ref_name]
|
||||
|
||||
# Should be oneOf with multiple types
|
||||
assert "oneOf" in input_schema
|
||||
|
||||
|
|
@ -100,10 +105,21 @@ class TestRequestCompliance:
|
|||
assert "discriminator" in content_schema
|
||||
assert content_schema["discriminator"]["propertyName"] == "type"
|
||||
|
||||
# Check TextContent is an option
|
||||
mapping = content_schema["discriminator"]["mapping"]
|
||||
assert "text" in mapping
|
||||
print(f"Content type discriminator mapping: {list(mapping.keys())}")
|
||||
# Check TextContent is an option (via mapping if present, or via oneOf refs)
|
||||
mapping = content_schema["discriminator"].get("mapping")
|
||||
if mapping:
|
||||
assert "text" in mapping
|
||||
print(f"Content type discriminator mapping: {list(mapping.keys())}")
|
||||
else:
|
||||
# Discriminator without explicit mapping — verify via oneOf
|
||||
one_of = content_schema.get("oneOf", [])
|
||||
ref_names = [
|
||||
opt["$ref"].split("/")[-1] for opt in one_of if "$ref" in opt
|
||||
]
|
||||
assert "TextContent" in ref_names, (
|
||||
f"TextContent not found in oneOf refs: {ref_names}"
|
||||
)
|
||||
print(f"Content type discriminator (no mapping), oneOf refs: {ref_names}")
|
||||
|
||||
def test_text_content_schema(self, spec_dict):
|
||||
"""Verify TextContent schema."""
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue